CN114244193B - Networked direct current motor optimal control method, system, equipment and medium - Google Patents

Networked direct current motor optimal control method, system, equipment and medium Download PDF

Info

Publication number
CN114244193B
CN114244193B CN202210102953.3A CN202210102953A CN114244193B CN 114244193 B CN114244193 B CN 114244193B CN 202210102953 A CN202210102953 A CN 202210102953A CN 114244193 B CN114244193 B CN 114244193B
Authority
CN
China
Prior art keywords
current motor
direct current
control signal
loop system
feedback controller
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210102953.3A
Other languages
Chinese (zh)
Other versions
CN114244193A (en
Inventor
张萌
吴卓睿
胡建晨
管晓宏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian Jiaotong University
Original Assignee
Xian Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian Jiaotong University filed Critical Xian Jiaotong University
Priority to CN202210102953.3A priority Critical patent/CN114244193B/en
Publication of CN114244193A publication Critical patent/CN114244193A/en
Application granted granted Critical
Publication of CN114244193B publication Critical patent/CN114244193B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/06—Arrangements for speed regulation of a single motor wherein the motor speed is measured and compared with a given physical value so as to adjust the motor speed
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/14—Electronic commutators
    • H02P6/15—Controlling commutation time

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Control Of Electric Motors In General (AREA)

Abstract

The invention belongs to the technical field of automatic control, and discloses a networked direct current motor optimal control method, a system, equipment and a medium, wherein the method comprises the following steps: describing the running dynamic of the direct current motor through a discrete time model with Markov parameters, and constructing an open-loop system model with time-varying time delay of the direct current motor; the method comprises the steps of constructing a feedback controller of the direct current motor through weighting a time-varying time delay system state and a normal system state of the direct current motor and obtaining a control signal according to the feedback controller; the quantization control signals are quantized through a logarithmic quantizer, quantization errors are processed through a sector boundary method, the quantization control signals are obtained, an open-loop system model is combined, a closed-loop system model of the direct-current motor is obtained, a closed-loop system stability criterion is constructed and solved, feedback controller parameters are obtained, control signal data of the direct-current motor are obtained based on the feedback controller parameters, and the direct-current motor is controlled according to the control signal data. Accurate control can be realized under the condition that time-varying time delay exists in the system state.

Description

Networked direct current motor optimal control method, system, equipment and medium
Technical Field
The invention belongs to the technical field of automatic control, and relates to a networked direct current motor optimal control method, a networked direct current motor optimal control system, networked direct current motor optimal control equipment and a networked direct current motor optimal control medium.
Background
The direct current motor can realize the mutual conversion of direct current electric energy and mechanical energy, and is widely applied to the fields of electric power systems, intelligent manufacturing, aerospace and the like. With the improvement of the interconnection degree of the modern control system and the continuous expansion of the scale, the direct current motor gradually becomes an important component of a plurality of networked control systems. Although networking can bring great convenience to the control system, the performance of the networked direct current motor control system is greatly restricted due to data loss, transmission delay and the like caused by limited communication bandwidth.
At present, most of networked direct current motor control methods only use normal system states to design controllers, information contained in system time delay states is not fully discovered, extra conservation is introduced to closed loop system stability criteria when time delay problems are processed, difficulty is brought to solving of controller parameters, practical application of control algorithms is not facilitated, and stable control of the direct current motor is difficult to achieve.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a networked direct current motor optimal control method, a networked direct current motor optimal control system, networked direct current motor optimal control equipment and a networked direct current motor optimal control medium.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect of the present invention, a networked direct current motor optimization control method includes:
describing the running dynamic of the direct current motor through a discrete time model with Markov parameters, and constructing an open-loop system model with time-varying time delay of the direct current motor;
the method comprises the steps of constructing a feedback controller of the direct current motor through weighting a time-varying time delay system state and a normal system state of the direct current motor and obtaining a control signal according to the feedback controller;
quantizing the control signal by a logarithmic quantizer, processing the quantization error by adopting a sector boundary method to obtain a quantized control signal, and obtaining a closed-loop system model of the direct-current motor according to the quantized control signal and an open-loop system model;
the construction is such that the closed loop system model satisfies l 2 -l ∞ The method comprises the steps of (1) obtaining a Lyapunov function with performance constraint and random stability, and obtaining a closed loop system stability criterion according to the constructed Lyapunov function and a lower bound primer;
and solving a closed loop system stability criterion to obtain a feedback controller parameter, obtaining control signal data of the direct current motor through the feedback controller based on the feedback controller parameter, and controlling the direct current motor according to the control signal data.
Optionally, the describing the running dynamics of the direct current motor by using a discrete time model with markov parameters, and constructing an open loop system model with time-varying time delay of the direct current motor includes:
describing the running dynamics of the direct current motor by the following discrete time Markov jump model to obtain an open-loop system model with time-varying time delay of the direct current motor:
wherein k is the specific moment of discrete time; x (k) ∈R n Is the system state of the direct current motor; u (k) ∈R p The quantized control signal is used as a quantized control signal of the direct current motor; omega (k) ∈R m Is contained in l 2 [0, +%), z (k) ∈R d The system output of the direct current motor is that rho (k) is the initial state of the system of the direct current motor, and n, p, m and d are the variable dimensions; d (k) is time-varying time delay of the direct current motor, and meets the requirement ofWherein, d is 0.ltoreq.d->Upper and lower bounds for d (k), respectively; a is that τk ,A d,τk ,B 1τk ,D 1τk ,C τk ,C d,τk ,B 2τk ,D 2τk All are preset system matrix parameters; τk is a random variable, a Markov process, and τk values are included in the set +.>In (1), transition probability pi ab The method comprises the following steps:
Pr{τ(k+1)＝b|τk＝a}＝π ab
wherein pi ab ∈[0,1]And for allThere is->
Optionally, the constructing the feedback controller of the dc motor by weighting the time-varying delay system state and the normal system state of the dc motor and obtaining the control signal according to the feedback controller includes:
the feedback controller of the direct current motor is constructed as follows:
v(k)＝K(τk)x(k)+K d (τk)x(k-d(k))
wherein v (K) is a control signal of the DC motor, K (τk) and K d And (τk) is a feedback controller parameter.
Optionally, the quantizing the control signal by the logarithmic quantizer includes:
the control signal is quantized using a logarithmic quantizer as follows:
f(v)＝[f 1 (v 1 ),f 2 (v 2 ),...,f q (v q )] T
δ i ＝[(1-ρ i )/(1+ρ i )]
where q is the total number of logarithmic quantizers, v 1 Is the i-th element of the control signal v and satisfies f i (-v i )＝-f i (-v i )，χ i Is the quantization level set, χ, of the logarithmic quantizer ij Is a quantized value, 0 < ρ i < 1 is a preset quantization density, delta i For the quantization density function, i and j are function subscripts;
the processing the quantization error by adopting the sector boundary method to obtain the quantization control signal comprises the following steps:
the quantization error delta is processed by the following method k v(k)：
f(v(k))-v(k)＝Δ k v(k)
Wherein delta is k ＝diag{Δ 1k ,Δ 2k ,...Δ qk }；||Δ ik ||≤δ i ；Δ ik The quantized coefficient of the ith control signal at the k moment;
resulting in quantization control signal u (k) =f (v (k))= (i+Δ k )v(k)；
The obtaining the closed loop system model of the direct current motor according to the quantized control signal and the open loop system model comprises the following steps:
substituting the quantized control signal into an open-loop system model to obtain a closed-loop system model of the direct current motor as follows:
wherein:
optionally, the construction is such that the closed loop system model satisfies l 2 -l ∞ The performance constrained and randomly stable Lyapunov function includes:
the following Lyapunov function was constructed:
wherein,τk＝a，τ(k+1)＝b，P a (k)、Q 1 (i)、Q 2 (i) And Q 3 (i) For the pending Lyapunov matrix, H 1 And H 2 For the relaxation variables to be solved, ζ (i) is the system state difference of the direct current motor at the moment i+1 and the moment i, and ζ (k) is the system state difference of the direct current motor at the moment k+1 and the moment k.
Optionally, the closed loop system stability criteria include:
the presence of a parameter y > 0,so thatAnd, for any->Presence of P a (k)＞0、Q 1 (k)＞0、Q 1 (k-d)＞0、Q 2 (k)＞0、/>Q 3 (k)＞0、Q 3 (k-d(k))＞0，H 1 ＞0，H 2 > 0 and S such that:
wherein,
Θ 11 ＝-P a (k)-H 1 +Q 1 (k)+Q 2 (k)+(d+1)Q 3 (k)；Θ 22 ＝-Q 1 (k-d)-H 1 -H 2 ；
Θ 32 ＝H 2 -S T ；Θ 33 ＝S+S T -2H 2 -Q 3 (k-d(k))；Θ 42 ＝S T ；Θ 43 ＝H 2 -S T ；
i is an identity matrix;
wherein P is a (k)、Q 1 (k)、Q 1 (k-d)、Q 2 (k)、Q 3 (k) And Q 3 (k-d (k)) are all pending Lyapunov matrices, H 1 、H 2 And S are both relaxation variables to be solved.
In a second aspect of the present invention, a networked dc motor optimization control system includes:
the open loop model construction module is used for describing the running dynamics of the direct current motor through a discrete time model with Markov parameters and constructing an open loop system model with time-varying time delay of the direct current motor;
the controller construction module is used for constructing a feedback controller of the direct current motor through weighting the time-varying time delay system state and the normal system state of the direct current motor and obtaining a control signal according to the feedback controller;
the closed loop model construction module is used for quantizing the control signal through the logarithmic quantizer, processing the quantization error by adopting a sector boundary method to obtain a quantized control signal, and obtaining a closed loop system model of the direct current motor according to the quantized control signal and the open loop system model;
a stability criterion construction module for constructing a closed loop system model to satisfy l 2 -l ∞ The method comprises the steps of (1) obtaining a Lyapunov function with performance constraint and random stability, and obtaining a closed loop system stability criterion according to the constructed Lyapunov function and a lower bound primer;
the control module is used for solving the stability criterion of the closed-loop system, obtaining the parameters of the feedback controller, obtaining the control signal data of the direct-current motor through the feedback controller based on the parameters of the feedback controller, and controlling the direct-current motor according to the control signal data.
In a third aspect of the present invention, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the steps of the networked direct current motor optimization control method are implemented when the processor executes the computer program.
In a fourth aspect of the present invention, a computer readable storage medium stores a computer program which, when executed by a processor, implements the steps of the networked dc motor optimization control method described above.
Compared with the prior art, the invention has the following beneficial effects:
the networked direct current motor optimization control method of the invention models the direct current motor by using a discrete time model with Markov parameters, fully considers the model uncertainty caused by internal and external disturbance of the direct current motor, accurately characterizes the dynamic characteristics of the direct current motor, designs a feedback controller by weighting the time-varying delay system state and the normal system state of the direct current motor, quantitatively codes control signals, and then transmits the control signals, thereby greatly reducing the data transmission quantity, and then constructs a closed loop system model to meet l 2 -l ∞ And the Lyapunov function with performance constraint and random stability is combined with a lower boundary theorem according to the constructed Lyapunov function to obtain a closed loop system stability criterion, a relaxation variable optimization time-varying time delay boundary is introduced by combining the lower boundary theorem and the Lyapunov function, conservation caused by time delay to the stability criterion is reduced to the minimum, information of a system time delay state is fully explored, balance between control performance and conservation of the system stability criterion is considered, and further accurate control is realized under the condition that time-varying time delay exists. Meanwhile, the quantization error is processed by adopting a sector boundary method in the process of designing the controller, the suppression of system noise is considered, the suppression ratio of the system output to the noise is allowed to be set, the influence of the noise on the control system can be suppressed within a small range, and the control accuracy is further improved.
Drawings
FIG. 1 is a flowchart of a networked DC motor optimization control method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a networked DC motor optimization control logic according to an embodiment of the present invention;
fig. 3 is a schematic diagram showing comparison between angular velocity responses of a networked dc motor according to an embodiment of the present invention and a dc motor according to an existing networked dc motor optimization control method;
fig. 4 is a schematic diagram showing comparison of angular current response of a networked dc motor according to an embodiment of the present invention and a dc motor according to an existing networked dc motor optimization control method.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the attached drawing figures:
referring to fig. 1 and 2, in an embodiment of the present invention, a networked dc motor optimization control method is provided, which achieves accurate control of a dc motor in the case of time-varying delay in the state of a dc motor system, and balances the control performance and the conservation of system stability criteria.
Specifically, the networked direct current motor optimization control method comprises the following steps:
s1: describing the running dynamic of the direct current motor through a discrete time model with Markov parameters, and constructing an open-loop system model with time-varying time delay of the direct current motor.
In particular, considering that a dc motor system in an industrial application generally operates in a network environment, a delay exists in signal transmission and affects a control effect, an operation dynamics of the dc motor in the network environment is described by a discrete time model with markov parameters, and the operation dynamics of the dc motor in the network environment is described by the following discrete time markov jump model:
wherein k is the specific moment of discrete time; x (k) ∈R n Is the system state of the direct current motor; u (k) ∈R p The quantized control signal is used as a quantized control signal of the direct current motor; omega (k) ∈R m Is contained in l 2 [0, +%), z (k) ∈R d The system output of the direct current motor is that rho (k) is the initial state of the system of the direct current motor, and n, p, m and d are the variable dimensions; d (k) is time-varying time delay of the direct current motor, and meets the requirement ofWherein, d is 0.ltoreq.d->Upper and lower bounds for d (k), respectively; a is that τk ,A d,τk ,B 1τk ,D 1τk ,C τk ,C d,τk ,B 2τk ,D 2τk All are preset system matrix parameters; τk is a random variable, a Markov process, and τk values are included in the set +.>In (1), transition probability pi ab The method comprises the following steps:
Pr{τ(k+1)＝b|τk＝a}＝π ab
wherein pi ab ∈[0,1]And for allThere is->
S2: and constructing a feedback controller of the direct current motor by weighting the time-varying time delay system state and the normal system state of the direct current motor and obtaining a control signal according to the feedback controller.
Specifically, the time-varying delay system state and the normal system state of the weighted direct current motor construct the following feedback controller of the direct current motor:
v(k)＝K(τk)x(k)+K d (τk)x(k-d(k))
wherein v (K) is a control signal of the DC motor, K (τk) and K d And (τk) is a feedback controller parameter.
S3: the quantization control signal is quantized by a logarithmic quantizer, the quantization error is processed by a sector boundary method, the quantization control signal is obtained, and a closed loop system model of the direct current motor is obtained according to the quantization control signal and an open loop system model.
Specifically, the control signal v (k) is quantized, the signal transmission quantity is reduced on the premise of ensuring the stability of the system, the requirement on the system network bandwidth is reduced, and the considered logarithmic quantizer is as follows:
f(v)＝[f 1 (v 1 ),f 2 (v 2 ),...,f q (v q )] T
wherein: q is the total number of logarithmic quantizers, v 1 Is the ith element of v and satisfies f i (-v i )＝-f i (-v i )。
The quantization level set of the logarithmic quantizer is described as:
wherein: 0 < ρ i < 1 is the defined quantization density χ ij Is a quantized value, i and j are functional indices.
Quantization function f i (v i ) Is defined as:
wherein: quantization Density function delta i ＝[(1-ρ i )/(1+ρ i )]。
The quantization error can be guaranteed to be controlled within the sector area by the sector boundary method, more specifically, the quantization error can be handled by:
f(v(k))-v(k)＝Δ k v(k)
wherein: delta k ＝diag{Δ 1k ,Δ 2k ,...Δ qk }；||Δ ik ||≤δ i ；Δ ik Is the quantized coefficient of the ith control signal at time k.
The mathematical expression to get f (v (k)) is:
f(v(k))＝(I+Δ k )v(k)
the executor receives the quantization control signal u (k) from the feedback controller as:
u(k)＝f(v(k))
therefore, substituting the quantized control signal into the open loop system model can result in a closed loop system model as:
wherein:
s4: structure of the deviceSo that the closed loop system model satisfies l 2 -l ∞ And (3) a Lyapunov function with performance constraint and random stability is obtained, and a closed loop system stability criterion is obtained according to the constructed Lyapunov function and a lower bound primer.
Specifically, a closed loop system stability criterion is constructed by combining a lower bound lemma to reduce the influence of a processing delay process on system stability analysis, reduce the conservation of control design, and the lower bound lemma is described as follows:
for R m The median of the open subset C of (C) is a positive function f 1 ,f 2 ,...,f s :R m -R, the interactive convex combination function F (x) of the above functions on subset C is defined as:
wherein: beta i Satisfy beta i > 0, and has beta 1 +β 2 +…+β s ＝1。
The minimum of the function is:
wherein: equation g i,j ：R m R satisfies g i,j (x)＝g j,i (x) And has:
first, let τk=a, τ (k+1) =b, construct the following Lyapunov function that fully contains all available system information so that the closed-loop system model satisfies l 2 -l ∞ Performance constraint and random stabilization:
wherein,τk＝a，τ(k+1)＝b，P a (k)、Q 1 (i)、Q 2 (i) And Q 3 (i) For the pending Lyapunov matrix, H 1 And H 2 For the relaxation variables to be solved, ζ (i) is the system state difference of the direct current motor at the moment i+1 and the moment i, and ζ (k) is the system state difference of the direct current motor at the moment k+1 and the moment k.
Then, prescribe l 2 -l ∞ Performance conditions, ensuring that the closed loop system can meet l 2 -l ∞ The performance condition is to restrain the influence of noise on the closed loop system, namely, under the optimization action of the designed feedback controller, the output z (k) and the noise w (k) epsilon l of the closed loop system 2 [0, +%) is finally satisfied
Lyapunov function based on construction is combined with lower bound quotients to obtain a result of meeting l 2 -l ∞ Performance constraints and sufficient conditions to ensure closed loop system stability:
given a parameter gamma > 0, the closed loop system is randomly stable and satisfies l 2 -l ∞ The performance is conditioned by the presence of P for any a ε L i,a ＞0,Q 1i ＞0,Q 2i ＞0,Q 3i ＞0,H 1 ＞0,H 2 > 0 and S, i.e. P is present a (k)＞0、Q 1 (k)＞0、Q 1 (k-d)＞0、Q 2 (k)＞0、Q 3 (k)＞0、Q 3 (k-d(k))＞0，H 1 ＞0，H 2 > 0 and S such that:
wherein,
Θ 11 ＝-P a (k)-H 1 +Q 1 (k)+Q 2 (k)+(d+1)Q 3 (k)；Θ 22 ＝-Q 1 (k-d)-H 1 -H 2 ；
Θ 32 ＝H 2 -S T ；Θ 33 ＝S+S T -2H 2 -Q 3 (k-d(k))；Θ 42 ＝S T ；Θ 43 ＝H 2 -S T ；
i is an identity matrix;
wherein P is a (k)、Q 1 (k)、Q 1 (k-d)、Q 2 (k)、Q 3 (k) And Q 3 (k-d (k)) are all pending Lyapunov matrices, H 1 、H 2 And S are both relaxation variables to be solved.
The Lyapunov function constructed as described below allows the closed loop system model to satisfy l 2 -l ∞ Performance constraints and random stabilization. Specifically, the difference value of the Lyapunov functions at two adjacent moments is calculated according to a closed-loop system, and the expectation is taken to obtain:
wherein:simultaneously, the method comprises the following steps: />
To facilitate the subsequent controller design process, two new vectors are defined:and the following inequality can be obtained: />
According to the lower bound quotation, the method comprises the following steps:
wherein:/>
combining the above equations yields:
wherein:it is apparent that when Ω < 0, the closed loop system has random stability.
Then, consider the equation:
because E { DeltaV (x (k), a) -w have been obtained T (k) w (k) } is < 0, and under zero initial conditions:
by Lyapunov function:and has the following steps: />
Then it is possible to obtain:
wherein: ζ (k) is defined as: ζ (k) = [ x ] T (k) x T (k-d(k)) w T (k)] T 。
Thus, it can be verified that the closed loop system satisfies l 2 -l ∞ Performance constraints:
/>
s5: solving a closed loop system stability criterion to obtain a feedback controller parameter, obtaining control signal data of the direct current motor through the feedback controller based on the feedback controller parameter, and controlling the direct current motor according to the control signal data.
Specifically, the system and the operation parameters of the direct current motor are obtained, the stability criterion of the closed loop system is solved according to the system and the operation parameters, the minimum positive real number gamma which can enable the stability criterion of the closed loop system to be established is obtained, the feedback controller parameters are brought into the feedback controller, the control signal data of the direct current motor are obtained, and then the operation of the direct current motor is controlled according to the control signal data of the direct current motor.
The networked direct current motor optimization control method is further described by combining a specific example:
first consider a dc motor system model described by a discrete-time markov jump model, the system state vector x (k) =v (k) i (k) x 3 (k) T Wherein v (k) is the motor angular velocity, i (k) is the motor current, x 3 (k) Is an integral term. All known parameters of the system are as follows:
C d (1)＝C d (2)＝C d (3)＝0 0 0，B 2 (1)＝B 2 (2)＝B 2 (3)＝0，D 1 (1)＝D 1 (2)＝D 1 (3)＝0.1，
substituting all the parameters into a closed-loop system stability criterion, solving the minimum positive real number gamma capable of enabling the closed-loop system stability criterion to be established by utilizing MATLAB, and obtaining parameters K (1) and K of a feedback controller after solving the optimization problem d (1)，K(2)，K d (2)，K(3)，K d (3) Substituting the obtained parameters into a feedback controller to control the rotating speed and current of the direct current motor to expected values.
Referring to fig. 3, a schematic diagram of comparing the angular velocity response of the dc motor under the networked dc motor optimization control method of the present invention and the existing networked dc motor optimization control method is shown. Referring to fig. 4, a comparison schematic diagram of angular current response of the dc motor under the networked dc motor optimization control method of the present invention and the existing networked dc motor optimization control method is shown. Therefore, the networked direct current motor optimal control method provided by the invention can effectively control the angular speed and current of the direct current motor in a network environment to be in an ideal state, and is superior to the existing networked direct current motor optimal control method.
In summary, the networked direct current motor optimization control method of the invention models the direct current motor by using the discrete time model with Markov parameters, fully considers the model uncertainty caused by internal and external disturbance of the direct current motor, accurately characterizes the dynamic characteristics of the direct current motor, designs the feedback controller by weighting the time-varying delay system state and the normal system state of the direct current motor, quantitatively codes the control signals, and then transmits the control signals, thereby greatly reducing the data transmission quantity, and then constructs the closed loop system model to meet l 2 -l ∞ And the Lyapunov function with performance constraint and random stability is combined with a lower boundary theorem according to the constructed Lyapunov function to obtain a closed loop system stability criterion, a relaxation variable optimization time-varying time delay boundary is introduced by combining the lower boundary theorem and the Lyapunov function, conservation caused by time delay to the stability criterion is reduced to the minimum, information of a system time delay state is fully explored, balance between control performance and conservation of the system stability criterion is considered, and further accurate control is realized under the condition that time-varying time delay exists. Meanwhile, the quantization error is processed by adopting a sector boundary method in the process of designing the controller, the suppression of system noise is considered, the suppression ratio of the system output to the noise is allowed to be set, the influence of the noise on the control system can be suppressed within a small range, and the control accuracy is further improved.
The following are device embodiments of the present invention that may be used to perform method embodiments of the present invention. For details not disclosed in the apparatus embodiments, please refer to the method embodiments of the present invention.
In still another embodiment of the present invention, a networked dc motor optimization control system is provided, which can be used to implement the above-mentioned networked dc motor optimization control method, and in particular, the networkThe optimized control system of the DC motor comprises an open-loop model building module, a controller building module, a closed-loop model building module, a stability criterion building module and a control module. The open loop model construction module is used for describing the running dynamic of the direct current motor through a discrete time model with Markov parameters and constructing an open loop system model with time-varying time delay of the direct current motor; the controller construction module is used for constructing a feedback controller of the direct current motor through weighting the time-varying time delay system state and the normal system state of the direct current motor and obtaining a control signal according to the feedback controller; the closed loop model construction module is used for quantizing the control signal through the logarithmic quantizer, processing the quantization error by adopting a sector boundary method to obtain a quantized control signal, and obtaining a closed loop system model of the direct current motor according to the quantized control signal and the open loop system model; the stability criterion construction module is used for constructing the closed loop system model to meet the requirement of l 2 -l ∞ The method comprises the steps of (1) obtaining a Lyapunov function with performance constraint and random stability, and obtaining a closed loop system stability criterion according to the constructed Lyapunov function and a lower bound primer; the control module is used for solving the stability criterion of the closed-loop system, obtaining the parameters of the feedback controller, obtaining the control signal data of the direct-current motor through the feedback controller based on the parameters of the feedback controller, and controlling the direct-current motor according to the control signal data.
In one possible implementation manner, the describing the running dynamics of the direct current motor through the discrete time model with the markov parameters, and constructing the open loop system model with the time-varying time delay of the direct current motor includes:
describing the running dynamics of the direct current motor by the following discrete time Markov jump model to obtain an open-loop system model with time-varying time delay of the direct current motor:
wherein k is the specific moment of discrete time; x (k) ∈R n Is the system state of the direct current motor; u (k) ∈R p The quantized control signal is used as a quantized control signal of the direct current motor; omega (k) ∈R m Is contained in l 2 [0, +%), z (k) ∈R d The system output of the direct current motor is that rho (k) is the initial state of the system of the direct current motor, and n, p, m and d are the variable dimensions; d (k) is time-varying time delay of the direct current motor, and meets the requirement ofWherein, d is 0.ltoreq.d->Upper and lower bounds for d (k), respectively; a is that τk ,A d,τk ,B 1τk ,D 1τk ,C τk ,C d,τk ,B 2τk ,D 2τk All are preset system matrix parameters; τk is a random variable, a Markov process, and τk values are included in the set +.>In (1), transition probability pi ab The method comprises the following steps: />
Pr{τ(k+1)＝b|τk＝a}＝π ab
Wherein pi ab ∈[0,1]And for allThere is->
In one possible implementation manner, the constructing the feedback controller of the direct current motor by weighting the time-varying delay system state and the normal system state of the direct current motor and obtaining the control signal according to the feedback controller includes:
the feedback controller of the direct current motor is constructed as follows:
v(k)＝K(τk)x(k)+K d (τk)x(k-d(k))
wherein v (K) is a control signal of the DC motor, K (τk) and K d And (τk) is a feedback controller parameter.
In one possible implementation, the quantizing the control signal by the logarithmic quantizer includes:
the control signal is quantized using a logarithmic quantizer as follows:
f(v)＝[f 1 (v 1 ),f 2 (v 2 ),...,f q (v q )] T
δ i ＝[(1-ρ i )/(1+ρ i )]
where q is the total number of logarithmic quantizers, v 1 Is the i-th element of the control signal v and satisfies f i (-v i )＝-f i (-v i )，χ i Is the quantization level set, χ, of the logarithmic quantizer ij Is a quantized value, 0 < ρ i < 1 is a preset quantization density, delta i For the quantization density function, i and j are function subscripts;
the processing the quantization error by adopting the sector boundary method to obtain the quantization control signal comprises the following steps:
the quantization error delta is processed by the following method k v(k)：
f(v(k))-v(k)＝Δ k v(k)
Wherein delta is k ＝diag{Δ 1k ,Δ 2k ,...Δ qk }；||Δ ik ||≤δ i ；Δ ik The quantized coefficient of the ith control signal at the k moment;
resulting in quantization control signal u (k) =f (v (k))= (i+Δ k )v(k)；
The obtaining the closed loop system model of the direct current motor according to the quantized control signal and the open loop system model comprises the following steps:
substituting the quantized control signal into an open-loop system model to obtain a closed-loop system model of the direct current motor as follows:
wherein:/>
in one possible embodiment, the configuration is such that the closed loop system model satisfies l 2 -l ∞ The performance constrained and randomly stable Lyapunov function includes:
the following Lyapunov function was constructed:
wherein,τk＝a，τ(k+1)＝b，P a (k)、Q 1 (i)、Q 2 (i) And Q 3 (i) For the pending Lyapunov matrix, H 1 And H 2 For the relaxation variables to be solved, ζ (i) is the system state difference of the direct current motor at the moment i+1 and the moment i, and ζ (k) is the system state difference of the direct current motor at the moment k+1 and the moment k.
In one possible embodiment, the closed loop system stability criteria include:
the presence parameter gamma > 0, such thatAnd, for any->Presence of P i,a ＞0，Q 1i ＞0，Q 2i ＞0，Q 3i ＞0，H 1 ＞0，H 2 > 0 and S, i.e. P is present a (k)＞0、Q 1 (k)＞0、Q 1 (k-d)＞0、Q 2 (k)＞0、/>Q 3 (k)＞0、Q 3 (k-d(k))＞0，H 1 ＞0，H 2 > 0 and S such that:
/>
wherein,
Θ 11 ＝-P a (k)-H 1 +Q 1 (k)+Q 2 (k)+(d+1)Q 3 (k)；Θ 22 ＝-Q 1 (k-d)-H 1 -H 2 ；
Θ 32 ＝H 2 -S T ；Θ 33 ＝S+S T -2H 2 -Q 3 (k-d(k))；Θ 42 ＝S T ；Θ 43 ＝H 2 -S T ；
i is an identity matrix;
wherein P is a (k)、Q 1 (k)、Q 1 (k-d)、Q 2 (k)、Q 3 (k) And Q 3 (k-d (k)) are all pending Lyapunov matrices, H 1 、H 2 And S are both relaxation variables to be solved.
All relevant contents of each step related to the foregoing embodiment of the method for optimizing and controlling a networked direct current motor may be cited to the functional description of the functional module corresponding to the system of the method for optimizing and controlling a networked direct current motor in the embodiment of the present invention, which is not repeated here.
The division of the modules in the embodiments of the present invention is schematically only one logic function division, and there may be another division manner in actual implementation, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist separately and physically, or two or more modules may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions within a computer storage medium to implement the corresponding method flow or corresponding functions; the processor provided by the embodiment of the invention can be used for operating the networked direct current motor optimal control method.
In yet another embodiment of the present invention, a storage medium, specifically a computer readable storage medium (Memory), is a Memory device in a computer device, for storing a program and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the networked DC motor optimization control method in the above embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (6)

1. The networked direct current motor optimal control method is characterized by comprising the following steps of:
describing the running dynamic of the direct current motor through a discrete time model with Markov parameters, and constructing an open-loop system model with time-varying time delay of the direct current motor;
the method comprises the steps of constructing a feedback controller of the direct current motor through weighting a time-varying time delay system state and a normal system state of the direct current motor and obtaining a control signal according to the feedback controller;
quantizing the control signal by a logarithmic quantizer, processing the quantization error by adopting a sector boundary method to obtain a quantized control signal, and obtaining a closed-loop system model of the direct-current motor according to the quantized control signal and an open-loop system model;
the construction is such that the closed loop system model satisfies l 2 -l ∞ The method comprises the steps of (1) obtaining a Lyapunov function with performance constraint and random stability, and obtaining a closed loop system stability criterion according to the constructed Lyapunov function and a lower bound primer;
solving a closed loop system stability criterion to obtain a feedback controller parameter, obtaining control signal data of the direct current motor through the feedback controller based on the feedback controller parameter, and controlling the direct current motor according to the control signal data;
describing the running dynamic state of the direct current motor through a discrete time model with Markov parameters, and constructing an open-loop system model with time-varying time delay of the direct current motor comprises the following steps:
describing the running dynamics of the direct current motor by the following discrete time Markov jump model to obtain an open-loop system model with time-varying time delay of the direct current motor:
wherein k is the specific moment of discrete time; x (k) ∈R n Is the system state of the direct current motor; u (k) ∈R p The quantized control signal is used as a quantized control signal of the direct current motor; omega (k) ∈R m Is contained in l 2 [0, +%), z (k) ∈R d The system output of the direct current motor is that rho (k) is the initial state of the system of the direct current motor, and n, p, m and d are the variable dimensions; d (k) is time-varying time delay of the direct current motor, and meets the requirement ofWherein, 0 is less than or equal tod，/>Upper and lower bounds for d (k), respectively; a is that τk ,A d,τk ,B 1τk ,D 1τk ,C τk ,C d,τk ,B 2τk ,D 2τk All are preset system matrix parameters; τk is a random variable, a Markov process, and τk values are contained in the setIn (1), transition probability pi ab The method comprises the following steps:
Pr{τ(k+1)＝b|τk＝a}＝π ab
wherein pi ab ∈[0,1]And for allThere is->
The step of constructing a feedback controller of the direct current motor through weighting the time-varying time delay system state and the normal system state of the direct current motor and obtaining a control signal according to the feedback controller comprises the following steps:
the feedback controller of the direct current motor is constructed as follows:
v(k)＝K(τk)x(k)+K d (τk)x(k-d(k))
wherein v (K) is a control signal of the DC motor, K (τk) and K d (τk) is a feedback controller parameter;
the quantizing the control signal by the logarithmic quantizer comprises:
the control signal is quantized using a logarithmic quantizer as follows:
f(v)＝[f 1 (v 1 ),f 2 (v 2 ),...,f q (v q )] T
δ i ＝[(1-ρ i )/(1+ρ i )]
where q is the total number of logarithmic quantizers, v i Is the i-th element of the control signal v and satisfies f i (-v i )＝-f i (-v i )，χ i Is the quantization level set, χ, of the logarithmic quantizer ij Is a quantized value, 0 < ρ i < 1 is a preset quantization density, delta i For the quantization density function, i and j are function subscripts;
the processing the quantization error by adopting the sector boundary method to obtain the quantization control signal comprises the following steps:
the quantization error delta is processed by the following method k v(k)：
f(v(k))-v(k)＝Δ k v(k)
Wherein delta is k ＝diag{Δ 1k ,Δ 2k ,...Δ qk }；||Δ ik ||≤δ i ；Δ ik The quantized coefficient of the ith control signal at the k moment;
resulting in quantization control signal u (k) =f (v (k))= (i+Δ k )v(k)；
The obtaining the closed loop system model of the direct current motor according to the quantized control signal and the open loop system model comprises the following steps:
substituting the quantized control signal into an open-loop system model to obtain a closed-loop system model of the direct current motor as follows:
wherein:
2. the networked direct current motor optimization control method according to claim 1, wherein the configuration is such that a closed-loop system model satisfies l 2 -l ∞ Performance constrained and randomly stableThe Lyapunov function includes:
the following Lyapunov function was constructed:
wherein,τk＝a，τ(k+1)＝b，P a (k)、Q 1 (i)、Q 2 (i) And Q 3 (i) For the pending Lyapunov matrix, H 1 And H 2 For the relaxation variables to be solved, ζ (i) is the system state difference of the direct current motor at the moment i+1 and the moment i, and ζ (k) is the system state difference of the direct current motor at the moment k+1 and the moment k.
3. The networked direct current motor optimization control method according to claim 2, wherein the closed loop system stability criterion comprises:
the presence parameter gamma > 0, such thatAnd, for any->Presence of P a (k)＞0、Q 1 (k)＞0、Q 1 (k-d)＞0、Q 2 (k)＞0、/>Q 3 (k)＞0、Q 3 (k-d(k))＞0，H 1 ＞0，H 2 > 0 and S such that:
wherein,
Θ 11 ＝-P a (k)-H 1 +Q 1 (k)+Q 2 (k)+(d+1)Q 3 (k)；Θ 22 ＝-Q 1 (k-d)-H 1 -H 2 ；
Θ 32 ＝H 2 -S T ；Θ 33 ＝S+S T -2H 2 -Q 3 (k-d(k))；Θ 42 ＝S T ；Θ 43 ＝H 2 -S T ；
i is an identity matrix;
wherein P is a (k)、Q 1 (k)、Q 1 (k-d)、Q 2 (k)、Q 3 (k) And Q 3 (k-d (k)) are all pending Lyapunov matrices, H 1 、H 2 And S are both relaxation variables to be solved.
4. A networked dc motor optimization control system, comprising:
the open loop model construction module is used for describing the running dynamics of the direct current motor through a discrete time model with Markov parameters and constructing an open loop system model with time-varying time delay of the direct current motor;
the controller construction module is used for constructing a feedback controller of the direct current motor through weighting the time-varying time delay system state and the normal system state of the direct current motor and obtaining a control signal according to the feedback controller;
the closed loop model construction module is used for quantizing the control signal through the logarithmic quantizer, processing the quantization error by adopting a sector boundary method to obtain a quantized control signal, and obtaining a closed loop system model of the direct current motor according to the quantized control signal and the open loop system model;
a stability criterion construction module for constructing a closed loop system model to satisfy l 2 -l ∞ The method comprises the steps of (1) obtaining a Lyapunov function with performance constraint and random stability, and obtaining a closed loop system stability criterion according to the constructed Lyapunov function and a lower bound primer;
the control module is used for solving the stability criterion of the closed-loop system, obtaining the parameters of the feedback controller, obtaining the control signal data of the direct-current motor through the feedback controller based on the parameters of the feedback controller, and controlling the direct-current motor according to the control signal data;
describing the running dynamic state of the direct current motor through a discrete time model with Markov parameters, and constructing an open-loop system model with time-varying time delay of the direct current motor comprises the following steps:
describing the running dynamics of the direct current motor by the following discrete time Markov jump model to obtain an open-loop system model with time-varying time delay of the direct current motor:
wherein k is the specific moment of discrete time; x (k) ∈R n Is the system state of the direct current motor; u (k) ∈R p The quantized control signal is used as a quantized control signal of the direct current motor; omega (k) ∈R m Is contained in l 2 [0, +%), z (k) ∈R d For the system output of the DC motor, ρ (k) is straightThe initial state of the system of the flow motor, n, p, m, d are the dimension of the variable; d (k) is time-varying time delay of the direct current motor, and meets the requirement ofWherein, 0 is less than or equal tod，/>Upper and lower bounds for d (k), respectively; a is that τk ,A d,τk ,B 1τk ,D 1τk ,C τk ,C d,τk ,B 2τk ,D 2τk All are preset system matrix parameters; τk is a random variable, a Markov process, and τk values are contained in the setIn (1), transition probability pi ab The method comprises the following steps:
Pr{τ(k+1)＝b|τk＝a}＝π ab
wherein pi ab ∈[0,1]And for allThere is->
The step of constructing a feedback controller of the direct current motor through weighting the time-varying time delay system state and the normal system state of the direct current motor and obtaining a control signal according to the feedback controller comprises the following steps:
the feedback controller of the direct current motor is constructed as follows:
v(k)＝K(τk)x(k)+K d (τk)x(k-d(k))
wherein v (K) is a control signal of the DC motor, K (τk) and K d (τk) is a feedback controller parameter;
the quantizing the control signal by the logarithmic quantizer comprises:
the control signal is quantized using a logarithmic quantizer as follows:
f(v)＝[f 1 (v 1 ),f 2 (v 2 ),...,f q (v q )] T
δ i ＝[(1-ρ i )/(1+ρ i )]
where q is the total number of logarithmic quantizers, v i Is the i-th element of the control signal v and satisfies f i (-v i )＝-f i (-v i )，χ i Is the quantization level set, χ, of the logarithmic quantizer ij Is a quantized value, 0 < ρ i < 1 is a preset quantization density, delta i For the quantization density function, i and j are function subscripts;
the processing the quantization error by adopting the sector boundary method to obtain the quantization control signal comprises the following steps:
the quantization error delta is processed by the following method k v(k)：
f(v(k))-v(k)＝Δ k v(k)
Wherein delta is k ＝diag{Δ 1k ,Δ 2k ,...Δ qk }；||Δ ik ||≤δ i ；Δ ik The quantized coefficient of the ith control signal at the k moment;
resulting in quantization control signal u (k) =f (v (k))= (i+Δ k )v(k)；
The obtaining the closed loop system model of the direct current motor according to the quantized control signal and the open loop system model comprises the following steps:
substituting the quantized control signal into an open-loop system model to obtain a closed-loop system model of the direct current motor as follows:
wherein:
5. a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the networked direct current motor optimization control method according to any one of claims 1 to 3 when the computer program is executed.
6. A computer-readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the networked direct current motor optimization control method according to any one of claims 1 to 3.
CN202210102953.3A 2022-01-27 2022-01-27 Networked direct current motor optimal control method, system, equipment and medium Active CN114244193B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210102953.3A CN114244193B (en) 2022-01-27 2022-01-27 Networked direct current motor optimal control method, system, equipment and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210102953.3A CN114244193B (en) 2022-01-27 2022-01-27 Networked direct current motor optimal control method, system, equipment and medium

Publications (2)

Publication Number Publication Date
CN114244193A CN114244193A (en) 2022-03-25
CN114244193B true CN114244193B (en) 2024-04-05

Family

ID=80747345

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210102953.3A Active CN114244193B (en) 2022-01-27 2022-01-27 Networked direct current motor optimal control method, system, equipment and medium

Country Status (1)

Country Link
CN (1) CN114244193B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004330361A (en) * 2003-05-08 2004-11-25 Japan Science & Technology Agency Method for recognizing/generating motion data by hidden markov model, and motion controlling method using the same and its controlling system
CN107861381A (en) * 2017-09-18 2018-03-30 南京邮电大学 The method of direct current generator networking tracking control unit
CN108803334A (en) * 2018-06-21 2018-11-13 广东工业大学 Direct current generator finite-time control method and system based on nonhomogeneous Markov model
CN109270833A (en) * 2018-10-23 2019-01-25 大连海事大学 A kind of Varied scope fuzzy control method based on brshless DC motor Q study
CN110620528A (en) * 2019-09-20 2019-12-27 电子科技大学 Multichannel direct current motor system control method based on second-order supercoiled sliding mode
CN112803860A (en) * 2021-03-01 2021-05-14 西北工业大学 Permanent magnet motor control method based on output feedback sliding mode control and anti-saturation PI
CN113364386A (en) * 2021-05-26 2021-09-07 潍柴动力股份有限公司 H-infinity current control method and system based on reinforcement learning of permanent magnet synchronous motor

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004330361A (en) * 2003-05-08 2004-11-25 Japan Science & Technology Agency Method for recognizing/generating motion data by hidden markov model, and motion controlling method using the same and its controlling system
CN107861381A (en) * 2017-09-18 2018-03-30 南京邮电大学 The method of direct current generator networking tracking control unit
CN108803334A (en) * 2018-06-21 2018-11-13 广东工业大学 Direct current generator finite-time control method and system based on nonhomogeneous Markov model
CN109270833A (en) * 2018-10-23 2019-01-25 大连海事大学 A kind of Varied scope fuzzy control method based on brshless DC motor Q study
CN110620528A (en) * 2019-09-20 2019-12-27 电子科技大学 Multichannel direct current motor system control method based on second-order supercoiled sliding mode
CN112803860A (en) * 2021-03-01 2021-05-14 西北工业大学 Permanent magnet motor control method based on output feedback sliding mode control and anti-saturation PI
CN113364386A (en) * 2021-05-26 2021-09-07 潍柴动力股份有限公司 H-infinity current control method and system based on reinforcement learning of permanent magnet synchronous motor

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Observer-Based Adaptive SMC for Nonlinear Uncertain Singular Semi-Markov Jump Systems With Applications to DC Motor;Wenhai Qi et al.;《IEEE Transactions on Circuits and Systems I: Regular Papers》;第65卷(第9期);第2951 - 2960页 *
占必红 等.《武汉大学学报（工学版）》.2015,第48卷(第2期),第249-255页. *

Also Published As

Publication number Publication date
CN114244193A (en) 2022-03-25

Similar Documents

Publication Publication Date Title
Zong et al. Guaranteed cost finite‐time control for semi‐Markov jump systems with event‐triggered scheme and quantization input
Zribi et al. A new PID neural network controller design for nonlinear processes
Shen et al. Quantized output feedback control for stochastic semi-Markov jump systems with unreliable links
Zhang et al. Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics
CN112884136B (en) Bounded clustering projection synchronous regulation control method and system for coupled neural network
CN107615186A (en) The method and apparatus of Model Predictive Control
Sun et al. Predictor-based periodic event-triggered control for dual-rate networked control systems with disturbances
Xi et al. Adaptive fuzzy guaranteed performance control for uncertain nonlinear systems with event-triggered input
Zhao et al. Event-triggered ADP for nonzero-sum games of unknown nonlinear systems
CN108931918B (en) Servo motor control method and device
Yu et al. Robust repetitive control and disturbance rejection based on two‐dimensional model and equivalent‐input‐disturbance approach
Shen et al. Iterative learning tracking for multisensor systems: A weighted optimization approach
Baldi et al. Adaptive mixing control with multiple estimators
Valluru et al. Stabilization of nonlinear inverted pendulum system using MOGA and APSO tuned nonlinear PID controller
Zhou et al. Asynchronous dissipative control of discrete-time fuzzy Markov jump systems with dynamic state and input quantization
Balaghiinaloo et al. An ℓ2-consistent event-triggered control policy for linear systems
CN116154788A (en) Voltage control method and device, terminal equipment and storage medium
CN115085202A (en) Power grid multi-region intelligent power collaborative optimization method, device, equipment and medium
CN114995498A (en) Multi-agent cooperative control method and device for transmission time lag
CN114244193B (en) Networked direct current motor optimal control method, system, equipment and medium
CN111221311A (en) Complex network distributed pulse synchronization method and system based on parameter variational method
Belhaj et al. MIMO PI controllers for LTI systems with multiple time delays based on ILMIs and sensitivity functions
CN116880191A (en) Intelligent control method of process industrial production system based on time sequence prediction
Yu et al. Adaptive quantised control of switched stochastic strict‐feedback non‐linear systems with asymmetric input saturation
Abbaszadeh et al. Constrained nonlinear model predictive control of an MMA polymerization process via evolutionary optimization

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant