CN107463097B - Self-adaptive quantitative fault-tolerant control device and method for underwater robot - Google Patents

Self-adaptive quantitative fault-tolerant control device and method for underwater robot Download PDF

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CN107463097B
CN107463097B CN201710890830.XA CN201710890830A CN107463097B CN 107463097 B CN107463097 B CN 107463097B CN 201710890830 A CN201710890830 A CN 201710890830A CN 107463097 B CN107463097 B CN 107463097B
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袁源
王铮
朱战霞
孙冲
陈诗瑜
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Northwestern Polytechnical University
Shenzhen Institute of Northwestern Polytechnical University
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Abstract

The invention discloses a self-adaptive quantization fault-tolerant control device and a method thereof for an underwater robot, wherein the device comprises an inner ring control module, wherein the inner ring control module controls a compensation and feedback module and generates a quantization control signal through a signal quantizer to control the underwater robot; the compensation and feedback module comprises an actuating mechanism fault self-adaptive compensation module, a nonlinear feedback module and an uncertainty self-adaptive compensation module; the inner ring control module generates a quantized control signal through a signal quantizer based on the underwater robot kinematic model, the underwater robot dynamic model and the expectation module. The adaptive fault compensator is designed, and can process the gain fault and perturbation fault of the actuating mechanism; by designing the inverse adaptation law, the control distribution matrix drift caused by control signal quantization is compensated.

Description

Self-adaptive quantitative fault-tolerant control device and method for underwater robot
Technical Field
The invention belongs to the technical field of underwater robot control; relates to a self-adaptive quantitative fault-tolerant control device of an underwater robot; the method also relates to a self-adaptive quantitative fault-tolerant control method of the underwater robot.
Background
In engineering control systems, signal quantization is of great significance. Quantized control signals are common in digital circuitry, network control systems, and hybrid control systems. Quantization of the control input signal generally refers to the conversion of a continuous control signal into a series of discrete control variables, which introduces drift and the occurrence of additional uncertainty in the control distribution matrix in the control system. On the other hand, the underwater robot has important application value in many aspects such as ocean resource utilization, underwater engineering construction and the like, and is a powerful tool for researching and developing deep sea resources by human beings. In a digital control system of an underwater robot, quantization of control input is inevitable. Therefore, the research on the quantitative motion control method of the underwater robot has important theoretical and practical significance.
Due to the special complex underwater environment, the fault of the underwater robot executing mechanism is difficult to avoid, and the fault-tolerant control under the condition of researching the fault is also necessary. Many researchers have studied about the fault-tolerant control technology of underwater robots. Yang and the like research the fault-tolerant control technology of the steering oar joint control type underwater robot. Fang et al studied fault diagnosis techniques for underwater robots, including fault detection of sensors and fault identification of thrusters. Yang and the like research a fault diagnosis and fault-tolerant control method of the underwater robot based on a Gaussian particle filter. However, the above documents are all studied for continuous measurement signals and control signals, and not for discontinuous quantized fault-tolerant control systems. Based on the situation that input signal quantification and an actuating mechanism fault coexist, the uncertainty and the additional uncertainty of the control distribution matrix are compensated based on the self-adaptive thought, and the underwater robot can be guaranteed to track the expected signal by using the quantified signal under the fault situation.
Disclosure of Invention
The invention provides a self-adaptive quantitative fault-tolerant control device of an underwater robot, which is provided with a self-adaptive fault compensator and can process gain faults and perturbation faults of an execution mechanism.
The invention also provides a self-adaptive quantization fault-tolerant control method of the underwater robot, which compensates the control distribution matrix drift caused by control signal quantization by designing a reverse self-adaptive law.
The technical scheme of the invention is as follows: an adaptive quantization fault-tolerant control device of an underwater robot comprises an inner ring control module, wherein the inner ring control module controls a compensation and feedback module and generates a quantization control signal through a signal quantizer to control the underwater robot; the compensation and feedback module comprises an actuating mechanism fault self-adaptive compensation module, a nonlinear feedback module and an uncertainty self-adaptive compensation module; the inner ring control module generates a quantized control signal through a signal quantizer based on the underwater robot kinematic model, the underwater robot dynamic model and the expectation module.
The other technical scheme of the invention is as follows: an adaptive quantitative fault-tolerant control method for an underwater robot comprises the following steps:
step 1, constructing a kinematic dynamics model of an underwater robot;
step 2, controlling and inputting the underwater robot, finishing signal quantization by adopting a signal quantizer, and establishing a quantization model of the signal quantizer;
step 3, establishing a fault model of an executing mechanism of the underwater robot;
and 4, establishing a self-adaptive quantitative control model of the underwater robot.
Furthermore, the invention is characterized in that:
wherein the quantization model in step 2 can be decomposed into a linear part and a non-linear unsealed part.
Wherein the failure of the actuator in step 3 comprises: the actuator outputs a measure of the fault, the offset fault of the actuator, and the gain fault of the actuator.
Wherein the types of the faults of the actuating mechanism in the step 3 comprise: no fault type, partial fault type, and full fault type.
And step 4, designing an inner ring virtual control law.
Wherein step 4 further comprises approximating the inner loop tracking error by fuzzy logic.
Compared with the prior art, the invention has the beneficial effects that: the method starts from the kinematic dynamics of the underwater robot, and can use a quantitative control signal to realize the motion control of the underwater robot under the condition that an executing mechanism has a fault; the invention can overcome the influence of time-varying external interference and has stronger robustness and self-adaptability. The control method provided by the invention can realize fault-tolerant control and has non-vulnerability; the control gain varies according to external interference and fault situation changes, and is non-conservative. In addition, the controller has simple structure, can reduce the operation load of the computer and has higher practical value.
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FIG. 1 is a schematic diagram of a control structure according to the present invention;
fig. 2 is a schematic diagram of efficiency loss and offset failure of the underwater robot actuator according to the present invention.
In the figure: 1 is a desired module; 2 is an inner ring control module; 3, an executing mechanism fault self-adaptive compensation module; 4 is a nonlinear feedback module; 5, an underwater robot kinematic model; 6, an underwater robot dynamic model; 7 is an uncertainty adaptive compensation module; 8 is the nonlinear model of the actuating mechanism; and 9 is a signal quantizer.
Detailed Description
The technical solution of the present invention is further explained with reference to the accompanying drawings and specific embodiments.
The invention provides a self-adaptive quantization fault-tolerant control device of an underwater robot, which comprises an inner ring control module 2, wherein the inner ring control module 2 is connected with a compensation and feedback module consisting of an execution mechanism fault self-adaptive compensation module 3, a nonlinear feedback module 4 and an uncertainty self-adaptive compensation module 7, and the inner ring control module 2 enables a signal quantizer 8 to generate a quantization control signal through fault information of the underwater robot provided by the compensation and feedback module; simultaneously, a nonlinear model 8 of the actuator is constructed, a dynamic model 6 of the underwater robot and a kinematic model 5 of the underwater robot are constructed on the basis again, and the inner-loop control module 2 is controlled jointly based on the dynamic model 6 of the underwater robot, the kinematic model 5 of the underwater robot and the expected signal generated by the expected module 1.
The invention also provides a self-adaptive quantitative fault-tolerant control method of the underwater robot, which comprises the following steps:
step 1, constructing a kinematic dynamics model of an underwater robot; the kinematics dynamics model of the underwater robot is as follows:
Figure BDA0001421116250000041
wherein M is an inertia matrix, C (v) is a Coriolis force and centripetal force matrix, D (v) is a hydrodynamic matrix, g (η) is a restoring force and moment vector, N is the number of actuating mechanisms, and taudJ (η) is a transformation matrix for external disturbance forces and moments, η represents position and attitude vectors of the underwater robot,
Figure BDA0001421116250000047
representing the velocity vector of the underwater robot.
Figure BDA0001421116250000042
Control output vector representing an actuator of the underwater robot, u ═ u1,u2,…,un]T，Q(u)＝[Q1(u1),Q2(u2),…,Qn(un)]TWherein Q isi(ui) Is composed of
Figure BDA0001421116250000043
Quantitative value of (a), F [ Q (u)]Indicating a quantized signal in a fault situation.
Step 2, controlling and inputting the underwater robot, finishing signal quantization by adopting a signal quantizer, and establishing a quantization model of the signal quantizer; the specific underwater robot control input adopts a signal quantizer to complete signal quantization, and the model can be expressed as follows:
Figure BDA0001421116250000044
wherein
Figure BDA0001421116250000045
j＝1,2,…，ui,min> 0 for q (u)i) The dead zone parameter 0 < rhoi＜1,δi＝(1-ρi)/(1+ρi) Constant ρiE (0,1) is a measure of the quantization density, that is, ρiThe smaller the quantizer, the coarser the quantizer. In general, Qi(ui) Is decomposed into a linear part and a non-linear part:
Qi(ui)＝ui+Δi(3)
wherein
Figure BDA0001421116250000046
Step 3, establishing a fault model of an executing mechanism of the underwater robot; as shown in fig. 2, in an underwater complex environment, an actuator of an intelligent robot is difficult to avoid failure, the control efficiency of the deflection of the control surface changes along with the change of the density and the flow rate of water flow, and the difference of the characteristics of the water flow often causes the deviation type failure of the hydrodynamic rudder. Based on the above analysis, and considering the quantization process of the underwater robot control input signal, the actuator fault can be modeled as follows:
Fi[Qi(ui)]＝hi(t)Qi(ui)+di,u(t)＝hi(t)ui+hi(t)Δi+di,u(t) (4)
wherein Fi[Qi(ui)]In order to be the output of the actuator,
Figure BDA0001421116250000058
representing an offset failure of the actuator, hi(t) represents a measure of actuator gain failure at [0, 1%]Taking values in between. Three types of faults may be represented by hi(t) is expressed as:
hi(t) ═ 1: the actuator works at full efficiency.
0＜hi(t) < 1, the actuator partially loses its efficiency. E.g. hiA value of (t) 0.8 characterizes a 20% loss of efficiency of the actuator. h isiAnd (t) is 0, the actuating mechanism is in a blocking state, and the output of the actuating mechanism is not influenced by the input any more.
Step 4, establishing a self-adaptive quantitative control model of the underwater robot, and assuming that the expected signal is ηdDefining the tracking error as eη＝η-ηdDeriving dynamic equations for the available tracking errorComprises the following steps:
Figure BDA0001421116250000051
the outer ring virtual control law is designed as follows:
Figure BDA0001421116250000052
wherein k is1> 0 is a design parameter. Further, defining the inner loop tracking error as ev＝v-vvirtualThen the dynamic equation for the inner loop tracking error can be expressed as:
Figure BDA0001421116250000053
selecting
Figure BDA0001421116250000054
To V0The derivation is carried out to obtain:
Figure BDA0001421116250000055
since C (v), g (η) and the hydrodynamic matrix D (v) are also assumed to be unknown, the present invention introduces fuzzy logic to approximate them.
-C(v)v-D(v)v＝θT(t)φ(v)+εφ(10)
Wherein,
Figure BDA0001421116250000056
for a matrix of unknown parameters, NlIn order to number the fuzzy logic,
Figure BDA0001421116250000057
a fuzzy approximation function, whose components can be expressed as:
Figure BDA0001421116250000061
l＝1,2,…,Nl。
Figure BDA0001421116250000062
for fuzzy approximation error, satisfy
Figure BDA0001421116250000063
Definition of
Figure BDA0001421116250000064
Simple calculations can yield:
Figure BDA0001421116250000065
wherein k is 0.2785, the ratio of k to k,
Figure BDA00014211162500000612
in order to design the constants of the two-phase,
Figure BDA0001421116250000066
further obtaining:
Figure BDA0001421116250000067
it is noted that
Figure BDA0001421116250000068
Wherein tau isd,i(t) satisfies
Figure BDA0001421116250000069
Further derivation shows that:
Figure BDA00014211162500000610
definition of d ═ supt≥0||ui,min+di,u(t)+τd,i(t) |, then
Figure BDA00014211162500000611
Further, it can be seen that:
Figure BDA00014211162500000613
wherein epsilondThe more than 0 is the design parameter,
Figure BDA00014211162500000614
then:
Figure BDA00014211162500000615
thus, it can be seen that:
Figure BDA0001421116250000071
the virtual control law is designed as follows:
Figure BDA0001421116250000072
wherein
Figure BDA0001421116250000073
Is composed of
Figure BDA0001421116250000074
Is defined as an estimate of
Figure BDA0001421116250000075
Continuing to derive:
Figure BDA0001421116250000076
in order to overcome the control efficiency drift caused by good signals, the following analysis is carried out:
Figure BDA0001421116250000077
definition Hi＝1/inft≥0||hi||，
Figure BDA00014211162500000713
As an estimate of the value of the error,
Figure BDA00014211162500000712
the actual control law is designed as follows:
Figure BDA0001421116250000078
thus:
Figure BDA0001421116250000079
thus:
Figure BDA00014211162500000710
further progress is made to obtain:
Figure BDA00014211162500000711
the final Lyapunov function was chosen as:
Figure BDA0001421116250000081
therefore, the following steps are carried out:
Figure BDA0001421116250000082
the selection of the adaptation law is known as:
Figure BDA0001421116250000083
the adaptation law may cause the control system to converge, where
Figure BDA0001421116250000084
σd,σHIs a normal number.

Claims (4)

1. An adaptive quantitative fault-tolerant control method for an underwater robot is characterized by comprising the following steps:
step 1, constructing a kinematic dynamics model of the underwater robot:
Figure FDA0002311339680000011
wherein M is an inertia matrix, C (v) is a Coriolis force and centripetal force matrix, D (v) is a hydrodynamic matrix, g (η) is a restoring force and moment vector, N is the number of actuating mechanisms, and taudFor external disturbance forces and moments, J (η) is a transformation matrix, η represents the position and attitude vectors of the underwater robot, v ∈ RnRepresenting a velocity vector of the underwater robot; u is an element of RnControl output vector representing an actuator of the underwater robot, u ═ u1,u2,…,un]T；
Q(u)＝[Q1(u1),Q2(u2),…,Qn(un)]TWherein Q isi(ui) Is uiE is the quantized value of R; f [ Q (u)]A quantized signal representing a fault condition;
step 2, controlling and inputting the underwater robot, finishing signal quantization by adopting a signal quantizer (8), and establishing a quantization model of the signal quantization;
the quantization model in the step 2 is expressed as:
Figure FDA0002311339680000012
wherein
Figure FDA0002311339680000013
ui,min> 0 for q (u)i) The dead zone parameter 0 < rhoi＜1,δi＝(1-ρi)/(1+vi) (ii) a Constant rhoiE (0,1) is a measure of the quantization density, that is, ρiThe smaller, the coarser the quantizer; and Qi(ui) Can be decomposed into a linear part and a non-linear part which are not sealed;
step 3, establishing a fault model of an executing mechanism of the underwater robot;
the step 3, the failure of the actuator comprises: the scale of actuator output failure, actuator offset failure, and actuator gain failure;
the actuator failure model is as follows:
Fi[Qi(ui)]＝hi(t)Qi(ui)+di,u(t)＝hi(t)ui+hi(t)Δi+di,u(t) (4)
wherein Fi[Qi(ui)]As output of the actuator, di,u(t) E R represents the offset fault of the actuator, hi(t) represents a measure of actuator gain failure at [0, 1%]Taking values; three types of faults may be represented by hi(t) is expressed as:
hi(t) ═ 1: the actuator works at full efficiency;
0＜hi(t) < 1, the actuator partially loses its efficiency; e.g. hi(t) 0.8 characterizes a 20% loss of efficiency of the actuator; h isi(t) is 0, the actuating mechanism is in a blocking state, and the output of the actuating mechanism is not influenced by the input any more;
step 4, establishing a self-adaptive quantitative control model of the underwater robot, and assuming that the expected signal is ηdDefining the tracking error as eη＝η-ηdThe dynamic equation from which the tracking error is derived is:
Figure FDA0002311339680000021
the outer ring virtual control law is designed as follows:
Figure FDA0002311339680000022
wherein k is1More than 0 is a design parameter; further, defining the inner loop tracking error as ev＝v-vvirtualThen the dynamic equation for the inner loop tracking error can be expressed as:
Figure FDA0002311339680000023
selecting
Figure FDA0002311339680000024
To V0The derivation is carried out to obtain:
Figure FDA0002311339680000025
since C (v), g (η) and the hydrodynamic matrix D (v) are also assumed to be unknown, fuzzy logic is introduced to approximate them, ideally:
-C(v)v-D(v)v＝θT(t)φ(v)+εφ(10)
wherein,
Figure FDA0002311339680000026
for a matrix of unknown parameters, NlIn order to number the fuzzy logic,
Figure FDA0002311339680000027
a fuzzy approximation function, whose components can be expressed as:
Figure FDA0002311339680000031
l＝1,2,…,Nl；εφ∈Rnfor fuzzy approximation error, satisfy
Figure FDA0002311339680000032
Definition of
Figure FDA0002311339680000033
The calculation can obtain:
Figure FDA0002311339680000034
wherein k is 0.2785, the ratio of k to k,
Figure FDA0002311339680000035
in order to design the constants of the two-phase,
Figure FDA0002311339680000036
further obtaining:
Figure FDA0002311339680000037
it is noted that
Figure FDA0002311339680000038
Wherein tau isd,i(t) satisfies τd＝[τd,1,τd,2,…,τd,n]T(ii) a Further derivation shows that:
Figure FDA0002311339680000039
definition of d ═ supt≥0||ui,min+di,u(t)+τd,i(t) |, then
Figure FDA00023113396800000310
Further, it can be seen that:
Figure FDA00023113396800000311
wherein epsilondThe more than 0 is the design parameter,
Figure FDA00023113396800000312
then:
Figure FDA00023113396800000313
thus, it can be seen that:
Figure FDA0002311339680000041
the virtual control law is designed as follows:
Figure FDA0002311339680000042
wherein
Figure FDA0002311339680000043
Is composed of
Figure FDA00023113396800000413
d estimated value, definition
Figure FDA0002311339680000044
Continuing to derive:
Figure FDA0002311339680000045
in order to overcome the control efficiency drift caused by good signals, the following analysis is carried out:
Figure FDA0002311339680000046
definition Hi＝1/inft≥0||hi||，
Figure FDA0002311339680000047
As an estimate of the value of the error,
Figure FDA0002311339680000048
the actual control law is designed as follows:
Figure FDA0002311339680000049
thus:
Figure FDA00023113396800000410
thus:
Figure FDA00023113396800000411
further progress is made to obtain:
Figure FDA00023113396800000412
the final Lyapunov function was chosen as:
Figure FDA0002311339680000051
therefore, the following steps are carried out:
Figure FDA0002311339680000052
the selection of the adaptation law is known as:
Figure FDA0002311339680000053
the adaptation law may cause the control system to converge, where
Figure FDA0002311339680000054
σd,σHIs a normal number.
2. The adaptive quantitative fault-tolerant control method of an underwater robot according to claim 1, wherein the types of the fault of the actuator in the step 3 include: no fault type, partial fault type, and full fault type.
3. The adaptive quantitative fault-tolerant control method of the underwater robot as claimed in claim 1, wherein the step 4 further comprises designing an inner-loop virtual control law.
4. The adaptive quantitative fault-tolerant control method of an underwater robot according to any one of claims 1 or 3, characterized in that the step 4 further comprises approximating the inner-loop tracking error by fuzzy logic.
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