CN104881512A - Particle swarm optimization-based automatic design method of ripple-free deadbeat controller - Google Patents
Particle swarm optimization-based automatic design method of ripple-free deadbeat controller Download PDFInfo
- Publication number
- CN104881512A CN104881512A CN201510171000.2A CN201510171000A CN104881512A CN 104881512 A CN104881512 A CN 104881512A CN 201510171000 A CN201510171000 A CN 201510171000A CN 104881512 A CN104881512 A CN 104881512A
- Authority
- CN
- China
- Prior art keywords
- mrow
- particle
- ripple
- design
- msub
- 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.)
- Pending
Links
- 239000002245 particle Substances 0.000 title claims abstract description 75
- 238000013461 design Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005457 optimization Methods 0.000 title abstract description 8
- 238000012546 transfer Methods 0.000 claims abstract description 44
- 238000004422 calculation algorithm Methods 0.000 claims description 14
- 230000003247 decreasing effect Effects 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 6
- 230000000452 restraining effect Effects 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 238000005070 sampling Methods 0.000 description 5
- 238000004088 simulation Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000012938 design process Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention discloses a particle swarm optimization-based automatic design method of a ripple-free deadbeat controller. The method includes the steps of 1, starting design; 2, acquiring a z-transfer function of a controlled object; 3, determining the number of parameters to be solved, and randomly initializing the parameters to be solved; 4, initializing the particle swarm optimization; 5, updating and restraining the speeds and positions of particles; 6, calculating fitness values of the particles and updating optimal positions of the particles; 7, judging whether termination conditions are met; and 8, ending the design. The method has the advantages such that the design of the ripple-free deadbeat controller is independent of manual computing, the design of the ripple-free deadbeat controller is automated, the design time is short, the efficiency is high, the precision is high, the reliability is high, and the method is provided for the theoretical and experimental teaching and engineering application of the ripple-free deadbeat controller.
Description
Technical Field
The invention relates to a ripple-free minimum-beat controller automatic design method based on a particle swarm algorithm, and belongs to the technical field of automatic control.
Background
The automatic control is a very important tool in the field of modern industrial control, and is widely applied to the fields of industrial production, transportation and military because of labor saving, timely response and high automation degree of the automatic control. Under the requirements of stability, accuracy and rapidity, automatic control continuously develops towards the directions of high stability, high precision, high speed and high reliability.
The minimum beat controller means that the system outputs a steady-state error of zero through minimum beat (limited beat) under the action of typical input signals (such as step signals, speed signals and acceleration signals). Therefore, the minimum-beat control system is also called a minimum-beat no-difference system or a minimum-beat follow-up system, and is essentially a time optimal control system, and on the premise of accuracy, the system adjustment time is required to be shortest or as short as possible, namely rapidity and accuracy are required for a closed-loop Z transfer function. To ripple-free the control system between sampling instants to reduce system power consumption and mechanical wear, a ripple-free minimum beat controller has emerged. Compared with other automatic control methods, the ripple-free minimum-beat control belongs to a digital design method, namely, the direct method has the advantages of short adjustment time, good control effect and the like, and is widely applied to engineering.
The design of the ripple-less least beat controller depends on the input signal type, the controlled object transfer function and the sampling period of the system. In engineering application, the input signal type, the controlled object parameter and the sampling period are changed frequently due to various reasons, so that the ripple-free minimum beat controller needs frequent repeated design to adapt to condition changes.
Heretofore, ripple-free minimum beat controller designs in engineering applications have remained entirely or partially dependent on manual calculations (i.e., some calculations were done by a computer, but unknown parameter solutions have remained dependent on manual). The design work of the ripple-free minimum-beat controller based on manual or semi-manual operation depends on manual single-step design and calculation of related parameters, a large amount of time and energy of related designers are occupied, the working strength of the designers is greatly increased, the problems of low design precision, poor reliability and the like of the ripple-free minimum-beat controller caused by factors such as insufficient effective numbers, fatigue or carelessness and the like occur, and hidden dangers are brought to safe and reliable operation of an automatic control system.
In conclusion, the manual or semi-manual design method of the ripple-free minimum-beat controller has the defects of high working strength, long design time, low efficiency, low precision, poor reliability and the like.
Disclosure of Invention
Aiming at the problems of the manual or semi-manual design technology, the invention provides an automatic design method of a ripple-free minimum-beat controller based on a particle swarm algorithm, which can realize the automation of the design of the ripple-free minimum-beat controller.
In order to achieve the purpose, the invention adopts the technical scheme that: a ripple-free minimum beat controller automatic design method based on a particle swarm algorithm comprises the following steps:
step 1: beginning of design
Step 2: obtaining a Z transfer function of a controlled object
Firstly, a transfer function G of a controlled object is obtained through modeling0(s) thereafter applying a zero-order keeper method to G0(s) discretizing into a Z transfer function G (Z), extracting zero points, poles and gains of G (Z), determining the number u of zero points and the number v of unstable poles, determining a parameter N according to the difference between the number of zero points and the number of poles, and determining the value of m according to the type of an input signal;
and step 3: determining the number of parameters to be solved, and randomly initializing the parameters to be solved
Determining a parameter k to be solved based on the parameters m and v in the step 2iDetermining the number of the parameters p to be solved based on the parameters N and u in the step 2iAfter that, the parameter k to be solved is initialized randomlyiAnd pi；
And 4, step 4: initializing particle swarm algorithm
Initializing the population scale, initial inertial weight, termination inertial weight, current iteration number, maximum iteration number, adjusting parameter, acceleration factor, individual optimal position and population optimal position parameter of the particle swarm, and randomly initializing the position vector and the velocity vector of the particle swarm;
and 5: updating and constraining the velocity and position of particles
Updating the inertia weight by using a decreasing formula, updating the velocity vector of the particle, and constraining the velocity vector of the particle; updating the position vector of the particle, and resetting the position vector of the particle beyond the search space;
step 6: calculating particle fitness value and updating optimal position of particle
According to the design theory of a ripple-free minimum beat controller, parameters (u, v, N, m) and a parameter to be solved (k)i、pi) Determining a closed loop Z transfer function W (Z) and a closed loop error Z transfer function We(z) an expression; mixing W (z) and WeExpanding the expression of (z), obtaining each coefficient, and solving W (z) and We(z) the absolute value of the difference between the coefficients of the expression correspondence terms is taken as a particle fitness value;
calculating the fitness value of each particle, and updating the individual optimal position and the group optimal position of the particle;
and 7: judging whether the termination condition is satisfied
If the termination condition is met, terminating the search and outputting a search result; otherwise, returning to the step 5 to continue searching;
and 8: and finishing the design.
Wherein, the particle group algorithm in the step 5 adopts an inertia weight automatic adjustment strategy;
the decreasing strategy of the inertia weight concave function has higher convergence speed and convergence precision at the same time, and the decreasing formula of omega is set as follows:
wherein, ω ismaxIs the initial inertial weight, ωminIn order to terminate the inertial weight, iter is the current iteration number, MaxIter is the maximum iteration number, and c is an adjusting parameter; at the beginning of an iterationFirst, the parameter ω is measuredmax、ωminInitializing iter, MaxIter and c; at the beginning of each iteration, an automatic update operation is performed on the inertial weight ω by equation (1).
Compared with the existing manual or semi-manual design method: according to the design theory and method of a ripple-free minimum beat controller in a computer control system, the fitness function is constructed by utilizing the relation between a closed-loop Z transfer function and a closed-loop error Z transfer function, and unknown coefficients in the closed-loop Z transfer function and the closed-loop error Z transfer function are solved by applying a particle swarm optimization algorithm.
The invention automatically completes the design of the ripple-free minimum-beat controller by utilizing a computer without manual participation, so that the design of the minimum-beat ripple-free controller does not depend on manual single-step design and calculation of related parameters, the working strength of related designers is greatly reduced, the problems of low design precision, poor reliability and the like of the controller caused by factors such as insufficient effective numbers, fatigue or carelessness and the like in the manual design process are avoided, and the invention has the advantages of less design time, high efficiency, high precision, high reliability and the like.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a functional block diagram of a computer control system;
FIG. 3 is a graph of fitness change curves;
FIG. 4 is a graph of variation of parameters to be solved;
FIG. 5 is a diagram of a simulation model of a ripple-free minimum beat controller;
FIG. 6 is a diagram of the output of the control system with the input unit ramp signal
FIG. 7 is a table comparing the results of manual control parameter calculations with the optimized values.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples, but the practice of the invention is not limited thereto.
A controlled object using the method of the present invention has a transfer function ofThe sampling period is 1s, and the input is a unit ramp signal.
As shown in fig. 1, the specific technical solution of the embodiment of the present invention has the following steps:
step 1: beginning of design
Step 2: obtaining a Z transfer function of a controlled object
Firstly, a transfer function G of a controlled object is obtained through modeling0(s) thereafter applying a zero-order keeper method to G0And(s) discretizing into a Z transfer function G (Z), extracting zero points, poles and gains of the G (Z), determining the number u of the zero points and the number v of unstable poles, determining a parameter N according to the difference between the number of the zero points and the number of the poles, and determining the value of m according to the type of the input signal.
A functional block diagram of a computer control system is shown in fig. 2. Under the action of input signal, transfer function G of controlled object0(s) discretization into a Z transfer function G (Z),
wherein, b1,b2,…,buIs u zeros of G (z); a is1,a2,…,avIs the v unstable poles of G (z); f. of1,f2,…,fwIs w stable poles of G (z); k is a constant coefficient; z is a radical of-NIs the pure hysteresis element of G (z).
Extracting the zero, the pole and the gain of G (z), determining the number u of the zero, and according to the sufficient requirements of the discrete system for stability: all poles are located in a unit circle, so that the number v of unstable poles is determined, a parameter N is determined according to the difference between the number of zero points and the number of poles, the value of m is related to the input type, and m is 1, 2 and 3 and respectively corresponds to unit step, unit slope and unit acceleration input.
And step 3: determining the number of parameters to be solved, and randomly initializing the parameters to be solved
Determining a parameter k to be solved based on the parameters m and v in the step 2iDetermining the number of the parameters p to be solved based on the parameters N and u in the step 2iAfter that, the parameter k to be solved is initialized randomlyiAnd pi。
According to the design theory and method of the ripple-free minimum beat controller, the closed loop Z transfer function of the system shown in FIG. 2 is
Wherein Q is1(z)＝k0+k1z-1+…+km+v-1z-(m+v-1)。
Closed loop error Z transfer function of
Wherein Q is2(z)＝1+p1z-1+…+pN+u-1z-(N+u-1)；k0、k1、...、km+v-1And p1、p2、...、pN+u-1Is the parameter to be solved. Determining a parameter k to be solved based on the parameters m and v in the step 2iDetermining the number of the parameters p to be solved based on the parameters N and u in the step 2iDetermining the dimension of the particle swarm according to the number of the parameters to be solved, and initializing the parameters to be solved;
and 4, step 4: initializing particle swarm algorithm
Initializing the population scale, initial inertial weight, termination inertial weight, current iteration number, maximum iteration number, adjusting parameter, acceleration factor, individual optimal position and population optimal position parameter of the particle swarm, and randomly initializing the position vector and the velocity vector of the particle swarm.
And 5: updating and constraining the velocity and position of particles
Updating the inertia weight by using a decreasing formula, updating the velocity vector of the particle, and constraining the velocity vector of the particle; the position vector of the particle is updated and the position vector of the particle beyond the search space is reset.
Substituting the parameter value obtained by the initialization in the step 4 and the current iteration number into an inertia weight decreasing formula
Wherein, ω ismaxIs the initial inertial weight, ωminIn order to terminate the inertial weight, iter is the current iteration number, MaxIter is the maximum iteration number, and c is an adjusting parameter; the inertial weight is updated, and the search capability and the exploration performance of the group are balanced by introducing the inertial weight omega; the larger omega is beneficial to expanding the search range and increasing the diversity of the group, and the smaller omega can enhance the local search capability; generally, an omega descending strategy is adopted, the omega at the initial stage of searching is larger, which is beneficial to avoiding the particle swarm from being trapped in local optimization, and the omega at the tail end of searching is smaller, which is beneficial to accelerating the convergence speed of the particle swarm. The decreasing strategy of the inertia weight concave function has higher convergence speed and convergence precision at the same time. Before the iteration starts, the parameter omega is first alignedmax、ωminInitializing iter, MaxIter and c, starting each iteration, and automatically updating the inertia weight omega by the formula (1);
updating the velocity vector and the position vector of the particle after each iteration; based on the initialized parameters in step 4, the velocity update formula of the ith particle at the time k +1 is as follows:
wherein, c1、c2Is an acceleration factor, and can adjust the step length of each iteration; rand () is [0,1 ]]Random constants within the range.
When a new particle is generated, the particle needs to satisfy a velocity constraint condition, and the velocity constraint formula of the particle is as follows:
the position of the ith particle at the time of k +1 is updated by the formula
Where r is a constraint factor, typically set to 1;
for new particles that may jump out of the search space, the new particles need to be restricted from being within the search space. Assuming the particles in the d-dimensionRange is [ Ld,Ud]If the new particle is out of the range, the position constraint formula is adopted as follows:
step 6: calculating particle fitness value and updating optimal position of particle
According to the design theory of a ripple-free minimum beat controller, parameters (u, v, N, m) and a parameter to be solved (k)i、pi) Determining a closed loop Z transfer function W (Z) and a closed loop error Z transfer function We(z) is as follows. Mixing W (z) and WeExpanding the expression of (z), obtaining each coefficient, and solving W (z) and WeThe absolute value of the difference between the coefficients of the corresponding terms of the expression (z) is taken as a particle fitness value.
As can be seen from the formulas (3) and (4), the key point of the design of the ripple-free minimum-beat controller is to determine the unknown parameter k of the closed-loop Z transfer function W (Z)0,k1,…,km+v-1Transfer function W with closed loop error ZeUnknown parameter p of (z)1,p2,…,pN+u-1I.e. a multidimensional function optimization problem; in view of this, the particles may be sequentially encoded as (k)0,k1,…,km+v-1,p1,p2,…,pN+u-1). Closed loop Z transfer function W (Z) and closed loop error Z transfer function We(z) has the following functionThe relationship is as follows:
W(z)＝1-We(z) (9)
according to formula (10), W (z) and WeExpanding the expression of (z), obtaining each coefficient, and solving W (z) and WeThe expression (z) uses the absolute value of the difference between the coefficients of the corresponding terms as the fitness value. The smaller the fitness function value is, the closer the unknown parameter is to the true value. When the fitness function value is 0, the corresponding particle position parameters are the closed loop Z transfer function W (Z) to be solved and the closed loop error Z transfer function We(Z) determining the closed-loop Z transfer function and the closed-loop error Z transfer function, and substituting the closed-loop Z transfer function and the closed-loop error Z transfer function into the following formula to complete the design of the ripple-free minimum beat controller:
in each iteration, the particle updates its position by tracking the individual optimal position and the population optimal position. The individual optimal position updating formula is as follows:
wherein,for the optimal position of the individual or individuals,and f is a fitness function, wherein f is the position of the ith particle in the search space at the moment k.
The updating formula of the optimal position of the group is as follows:
wherein,and the optimal position of the population is obtained.
And 7: judging whether the termination condition is satisfied
If the termination condition is met, terminating the search and outputting a search result; otherwise, returning to the step 5 to continue searching.
And 8: and finishing the design.
The whole design process is divided into 8 steps: (1) starting design; (2) acquiring a Z transfer function of a controlled object; (3) determining the number of parameters to be solved, and initializing the parameters to be solved randomly; (4) initializing a particle swarm algorithm; (5) updating and constraining the velocity and position of the particles; (6) calculating the fitness value of the particle and updating the optimal position of the particle; (7) judging whether a termination condition is met; (8) and finishing the design.
FIG. 2 is a schematic block diagram of a computer control system. Firstly, the controlled object G0(s) together with the zero-order keeper, form a generalized object, and the input signal R (z) acts on the controlled object through the digital controller D (z) to realize the output of the signal Y (z).
As shown in FIG. 7, the particle swarm optimization is applied to solve the parameter k obtained by the ripple-free least-beat controller0、k1And p1The numerical value of the method is completely consistent with the manual calculation result, and the feasibility and the accuracy of the method are proved.
As shown in fig. 3, the fitness function varies such that the fitness function value decays rapidly from a relatively large number to zero as the number of iterations increases, showing the gradual reduction of the error to zero.
As shown in fig. 4, in the variation process of the parameter to be solved, as the number of iterations increases, the parameter to be solved gradually approaches to the specific value, and the process that the parameter to be solved gradually approaches to the true value is shown.
Fig. 5 is a simulation model diagram of the ripple-less least-beat controller, in which an input signal, a controller transfer function, a controlled object transfer function, a zero-order retainer, and the like are input to a simulation model according to known conditions to perform a simulation experiment.
Fig. 6 is an output diagram of the control system when the input is a unit slope signal, the solid line is a control effect curve of the output response, the steady-state error of the output signal of the system is zero after 2 beats, and no ripple exists among sampling points, which is consistent with the manual design result, and the feasibility and the accuracy of the method are verified.
In summary, the following steps: according to the design theory and method of the ripple-free minimum beat controller in the computer control system, the fitness function is constructed by utilizing the relation between the closed-loop Z transfer function and the closed-loop error Z transfer function, the unknown coefficients in the closed-loop Z transfer function and the closed-loop error Z transfer function are solved by utilizing the particle swarm optimization algorithm, and the full automation of the design of the ripple-free minimum beat controller is realized.
The invention ensures that the design of the minimum-beat ripple-free controller does not depend on manual single-step design and the calculation of related parameters, greatly reduces the working strength of related personnel, avoids the problems of low design precision, poor reliability and the like of the controller caused by factors such as insufficient effective numbers, fatigue or carelessness and the like in the manual design process, and has the advantages of short design time, high efficiency, high precision, good reliability and the like.
Claims (2)
1. A ripple-free minimum beat controller automatic design method based on a particle swarm algorithm is characterized by comprising the following steps:
step 1: beginning of design
Step 2: obtaining a Z transfer function of a controlled object
Firstly, a transfer function G of a controlled object is obtained through modeling0(s) thereafter applying a zero-order keeper method to G0Discretizing into Z transfer function G (Z), extracting zero, pole and gain of G (Z), determining the number u of zeros and the number v of unstable poles, and calculating the sum of the numbers of zeros and the number v of unstable polesDetermining a parameter N according to the difference of the number of poles, and determining the value of m according to the type of the input signal;
and step 3: determining the number of parameters to be solved, and randomly initializing the parameters to be solved
Determining a parameter k to be solved based on the parameters m and v in the step 2iDetermining the number of the parameters p to be solved based on the parameters N and u in the step 2iAfter that, the parameter k to be solved is initialized randomlyiAnd pi；
And 4, step 4: initializing particle swarm algorithm
Initializing the population scale, initial inertial weight, termination inertial weight, current iteration number, maximum iteration number, adjusting parameter, acceleration factor, individual optimal position and population optimal position parameter of the particle swarm, and randomly initializing the position vector and the velocity vector of the particle swarm;
and 5: updating and constraining the velocity and position of particles
Updating the inertia weight by using a decreasing formula, updating the velocity vector of the particle, and constraining the velocity vector of the particle; updating the position vector of the particle, and resetting the position vector of the particle beyond the search space;
step 6: calculating particle fitness value and updating optimal position of particle
According to the design theory of a ripple-free minimum beat controller, parameters (u, v, N, m) and a parameter to be solved (k)i、pi) Determining a closed loop Z transfer function W (Z) and a closed loop error Z transfer function We(z) an expression; mixing W (z) and WeExpanding the expression of (z), obtaining each coefficient, and solving W (z) and We(z) the absolute value of the difference between the coefficients of the expression correspondence terms is taken as a particle fitness value;
calculating the fitness value of each particle, and updating the individual optimal position and the group optimal position of the particle;
and 7: judging whether the termination condition is satisfied
If the termination condition is met, terminating the search and outputting a search result; otherwise, returning to the step 5 to continue searching;
and 8: and finishing the design.
2. The automatic design method of the ripple-free minimum beat controller based on the particle swarm algorithm according to claim 1, wherein the particle swarm algorithm in step 5 adopts an inertia weight automatic adjustment strategy;
the decreasing strategy of the inertia weight concave function has higher convergence speed and convergence precision at the same time, and the decreasing formula of omega is set as follows:
wherein, ω ismaxIs the initial inertial weight, ωminIn order to terminate the inertial weight, iter is the current iteration number, MaxIter is the maximum iteration number, and c is an adjusting parameter; before the iteration starts, the parameter omega is first alignedmax、ωminInitializing iter, MaxIter and c; at the beginning of each iteration, an automatic update operation is performed on the inertial weight ω by equation (1).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510171000.2A CN104881512A (en) | 2015-04-13 | 2015-04-13 | Particle swarm optimization-based automatic design method of ripple-free deadbeat controller |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510171000.2A CN104881512A (en) | 2015-04-13 | 2015-04-13 | Particle swarm optimization-based automatic design method of ripple-free deadbeat controller |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104881512A true CN104881512A (en) | 2015-09-02 |
Family
ID=53949005
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510171000.2A Pending CN104881512A (en) | 2015-04-13 | 2015-04-13 | Particle swarm optimization-based automatic design method of ripple-free deadbeat controller |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104881512A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105243242A (en) * | 2015-11-13 | 2016-01-13 | 中国矿业大学 | Disturbance-considering minimum-beat controller automatic design method based on particle swarm algorithm |
| CN106200619A (en) * | 2016-08-15 | 2016-12-07 | 浙江大学 | The PI of subsidiary controller output constraint controls loop performance appraisal procedure |
| CN106372270A (en) * | 2015-07-23 | 2017-02-01 | 中国科学院沈阳自动化研究所 | Life cycle group search optimization algorithm-based optimization design method for pressure container |
| CN108365772A (en) * | 2018-03-12 | 2018-08-03 | 南京理工大学 | A kind of current transformer current inner loop optimum gain determines method |
| CN109039173A (en) * | 2018-08-09 | 2018-12-18 | 沈阳工业大学 | A kind of PMLSM iterative learning control method and system based on hybridization particle group optimizing |
| CN117520933A (en) * | 2023-12-29 | 2024-02-06 | 长春师范大学 | Environment monitoring method and system based on machine learning |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006004097A (en) * | 2004-06-16 | 2006-01-05 | Fuji Electric Systems Co Ltd | Pump controller |
| CN103592852A (en) * | 2013-11-29 | 2014-02-19 | 西南交通大学 | PID (Proportion Integration Differentiation) controller optimizing design method based on particle swarm membrane algorithm |
| CN103645636A (en) * | 2013-11-25 | 2014-03-19 | 南京航空航天大学 | PID controller parameter optimization method |
-
2015
- 2015-04-13 CN CN201510171000.2A patent/CN104881512A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006004097A (en) * | 2004-06-16 | 2006-01-05 | Fuji Electric Systems Co Ltd | Pump controller |
| CN103645636A (en) * | 2013-11-25 | 2014-03-19 | 南京航空航天大学 | PID controller parameter optimization method |
| CN103592852A (en) * | 2013-11-29 | 2014-02-19 | 西南交通大学 | PID (Proportion Integration Differentiation) controller optimizing design method based on particle swarm membrane algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 刘道: "基于改进粒子群优化算法的PID参数整定研究", 《中国优秀硕士学位论文信息科技辑》 * |
| 杨庆华等: "最少拍控制系统实验的参数计算", 《广西大学学报（自然科学版）》 * |
| 陈贵敏等: "粒子群优化算法的惯性权值递减策略研究", 《西安交通大学学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372270A (en) * | 2015-07-23 | 2017-02-01 | 中国科学院沈阳自动化研究所 | Life cycle group search optimization algorithm-based optimization design method for pressure container |
| CN105243242A (en) * | 2015-11-13 | 2016-01-13 | 中国矿业大学 | Disturbance-considering minimum-beat controller automatic design method based on particle swarm algorithm |
| CN106200619A (en) * | 2016-08-15 | 2016-12-07 | 浙江大学 | The PI of subsidiary controller output constraint controls loop performance appraisal procedure |
| CN106200619B (en) * | 2016-08-15 | 2018-11-20 | 浙江大学 | The PI control loop performance estimating method of subsidiary controller output constraint |
| CN108365772A (en) * | 2018-03-12 | 2018-08-03 | 南京理工大学 | A kind of current transformer current inner loop optimum gain determines method |
| CN109039173A (en) * | 2018-08-09 | 2018-12-18 | 沈阳工业大学 | A kind of PMLSM iterative learning control method and system based on hybridization particle group optimizing |
| CN117520933A (en) * | 2023-12-29 | 2024-02-06 | 长春师范大学 | Environment monitoring method and system based on machine learning |
| CN117520933B (en) * | 2023-12-29 | 2024-03-19 | 长春师范大学 | Environment monitoring method and system based on machine learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104881512A (en) | Particle swarm optimization-based automatic design method of ripple-free deadbeat controller | |
| JP4223894B2 (en) | PID parameter adjustment device | |
| CN103645636A (en) | PID controller parameter optimization method | |
| CN104197793B (en) | Missile PID controller parameter self-adaptive adjustment method | |
| CN105425612B (en) | A kind of method for optimizing of Adaptive System of Water-Turbine Engine control parameter | |
| CN105843073A (en) | Method for analyzing wing structure aero-elasticity stability based on aerodynamic force uncertain order reduction | |
| CN102063524B (en) | Performance reliability simulation method based on improved self-adaption selective sampling | |
| CN103728988B (en) | SCARA robot trajectory tracking control method based on internal model | |
| CN108153259B (en) | Multi-controller optimal state estimation control strategy design method based on Kalman filtering | |
| CN104199295A (en) | Electromechanical servo system friction compensation and variable structure control method based on neural network | |
| CN104463381A (en) | Building energy consumption predication method based on KPCA and WLSSVM | |
| CN104252133A (en) | Longitudinal control law smooth switching method of unmanned aerial vehicle | |
| CN106383523B (en) | A kind of aircraft nonlinear attitude control system stability analysis method | |
| CN105298734B (en) | A kind of parameter identification method of Adaptive System of Water-Turbine Engine | |
| CN105915121A (en) | Servo system inertia identification method adopting genetic algorithm for optimization | |
| CN104852639A (en) | Parameter self-tuning speed controller of permanent magnet synchronous motor based on neural network | |
| CN112564557A (en) | Control method, device and equipment of permanent magnet synchronous motor and storage medium | |
| CN108803601B (en) | Robust prediction tracking control method for mobile robot with communication constraint | |
| CN107863910A (en) | Permanent magnet synchronous motor optimal fractional order PID control method with strong tracking performance | |
| CN102566427A (en) | Aircraft robust control method | |
| CN106059412B (en) | DC motor with separate excitation method for controlling number of revolution based on reliability rule base reasoning | |
| CN113189871B (en) | Thermal power generating unit flexible operation-oriented coordination control strategy | |
| CN110262221B (en) | PID controller parameter control method for object in thermal process | |
| CN111460576A (en) | Characteristic value tracking method for flutter analysis of wings | |
| CN104270046A (en) | Motor control method based on self-learning of rotating speed-current two-dimensional fuzzy model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150902 |