CN110320800B - Compensation method and device of control system, medium and intelligent equipment - Google Patents
Compensation method and device of control system, medium and intelligent equipment Download PDFInfo
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Abstract
The invention discloses a control system compensation method, a device, a medium and intelligent equipment, wherein the method comprises the following steps: acquiring nonlinear parameters and uncertain disturbance parameters of a system to be compensated, and determining a system model of the system to be compensated according to the nonlinear parameters and the uncertain disturbance parameters; determining whether to dynamically compensate the system to be compensated according to the operation result of the system model; if yes, dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to a compensation controller. The invention can solve the problems that the existing compensation to the control system is mostly an accurate model, and in actual engineering application, the control system has nonlinear factors and uncertain disturbance, thereby greatly reducing the stability and control precision of the system and improving the satisfaction degree of user control experience.
Description
Technical Field
The invention relates to the technical field of control of mechanical arms, in particular to a control system compensation method, a system, a medium and intelligent equipment.
Background
In practical engineering applications, particularly control systems, there are many non-linear factors and uncertain disturbances in the various system components. In the running process, the stability of the system is limited due to the adverse effect generated by nonlinear factors and uncertain disturbance, the uncertainty of the system is caused, the control precision is reduced, and the development of high-precision tip technology is restricted
As is well known, a control system refers to a management system having its own objects and functions, which is composed of a control subject, a control object, and a control medium. With the rapid development of industry, the high-precision industry also rapidly develops, and the requirements of the high-precision technology industry on a control system are also continuously improved. Therefore, research for accurately dynamically compensating for nonlinear factors and uncertain disturbances in a control system, particularly in the field of high-precision tips, is particularly important.
The prior art is directed to compensation of control systems, which are mostly accurate models, whereas in practical engineering applications, the control system must have nonlinear factors and uncertain disturbances. If no effective method is adopted to process nonlinear factors and uncertain disturbances, the stability and control accuracy of the system are greatly reduced. Meanwhile, approximation errors can be generated by adopting a neural network control algorithm, so that the system cannot converge rapidly, the calculated amount is increased, and the burden is brought to hardware. In most of the control methods realized, as the system order increases, the control effect is difficult to ensure, which is difficult to meet the current requirements in the field of high precision tips.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a control system compensation method, a system, a medium and intelligent equipment which can track and compensate uncertainty in real time and ensure stability of a control system.
The control system compensation method provided by the invention comprises the following steps:
acquiring nonlinear parameters and uncertain disturbance parameters of a system to be compensated, and determining a system model of the system to be compensated according to the nonlinear parameters and the uncertain disturbance parameters;
determining whether to dynamically compensate the system to be compensated according to the operation result of the system model;
if yes, dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to a compensation controller.
According to the control system compensation method provided by the invention, firstly, nonlinear parameters and uncertain disturbance parameters of a system to be compensated are obtained, a system model of the system to be compensated is determined according to the nonlinear parameters and the uncertain disturbance parameters, and whether the system to be compensated is dynamically compensated is determined according to the operation result of the system model; if yes, dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to a compensation controller. According to the control system compensation method provided by the invention, the nonlinear factors and the uncertain disturbances are dynamically compensated, so that the stability and the control precision of the system are improved; meanwhile, the problems that the system cannot be converged rapidly, the calculated amount is increased and the load is brought to hardware due to approximation errors generated by the conventional neural network control algorithm can be solved, and the actual application requirements are met.
In addition, the control system compensation method according to the present invention may further have the following additional technical features:
further, the system model of the system to be compensated is:
wherein x is i U is the input of the system, g is the state variable of the system i Is a nonlinear parameter d i For uncertain interference parameters.
Further, the step of determining whether to dynamically compensate the system to be compensated according to the operation result of the system model includes:
constructing a low-order expansion state observer according to the uncertain disturbance parameters and approximation errors generated during the construction of the neural network model;
real-time tracking the uncertain disturbance of the system to be compensated through the extended state observer, and generating a corresponding tracking effect diagram according to the tracking result;
calculating an optimal matching subgraph with highest matching degree between the tracking effect graph and the standard effect graph;
and determining whether to dynamically compensate the system to be compensated according to the correlation degree of the optimal matching subgraph and the standard effect graph.
Further, the step of constructing a low-order extended state observer according to the uncertain disturbance parameters and approximation errors generated during the construction of the neural network model comprises the following steps:
and leading the uncertain interference parameters into a low-order expanded state observer to obtain an interference estimated value, wherein the model of the low-order expanded state observer is as follows:
the formula of the interference estimation value is as follows:
further, the step of dynamically compensating the system to be compensated according to the operation result and a preset standard includes:
determining a neural network control model according to the nonlinear parameters;
calculating a virtual control model according to the neural network control model;
calculating a sliding mode control model according to the virtual control model;
and determining the linearity of the operation result and a preset standard, and dynamically compensating the system to be compensated through the sliding mode control model.
Further, the formula for determining the neural network control model according to the nonlinear parameter is as follows:
further, the virtual control model is:
the sliding mode control model is as follows:
another embodiment of the invention provides a compensation device of a control system, which solves the problems that the existing compensation to the control system is mostly an accurate model, and in actual engineering application, the control system has nonlinear factors and uncertain disturbance, so that the stability and control precision of the system are greatly reduced, and the satisfaction degree of user control experience is improved.
According to an embodiment of the present invention, a control system compensation device includes:
the acquisition module is used for acquiring nonlinear parameters and uncertain disturbance parameters of the system to be compensated and determining a system model of the system to be compensated according to the nonlinear parameters and the uncertain disturbance parameters;
the judging module is used for determining whether to dynamically compensate the system to be compensated according to the operation result of the system model;
and the compensation module is used for dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to the compensation controller.
Another embodiment of the present invention also proposes a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described method.
The invention also provides intelligent equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the control system compensation method when executing the program.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a control system compensation method according to a first embodiment of the present invention;
FIG. 2 is a detailed flowchart of step S102 in FIG. 1;
FIG. 3 is a schematic diagram of the tracking effect of the embodiment of FIG. 1;
fig. 4 is a block diagram of a control system compensation device according to a second embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a control system compensation method according to a first embodiment of the present invention includes steps S101 to S103:
step S101, nonlinear parameters and uncertain disturbance parameters of a system to be compensated are obtained, and a system model of the system to be compensated is determined according to the nonlinear parameters and the uncertain disturbance parameters.
In this embodiment, a data intelligent device is taken as an example for explanation, but it should be understood that the embodiment of the invention is not limited thereto, and the method of the embodiment of the invention can be applied to any intelligent device, i.e. any electronic device capable of performing system compensation. Specifically, in the prior art, most of the aimed systems are accurate models, and in practical engineering application, nonlinear factors and uncertain disturbances must exist in the control system. If no effective method is adopted to process nonlinear factors and uncertain disturbances, the stability and control accuracy of the system are greatly reduced. Meanwhile, approximation errors can be generated by adopting a neural network control algorithm, so that the system cannot converge rapidly, the calculated amount is increased, and the burden is brought to hardware.
In specific implementation, the system type of the control system to be compensated is judged, the nonlinear parameter and the uncertain disturbance parameter of the control system to be compensated are determined according to the system type of the control system to be compensated, the system model of the control system to be compensated is determined according to the nonlinear parameter and the uncertain disturbance parameter, and the system model of the control system to be compensated is built through the system model to be compensated, so that the actual control system to be compensated is subjected to dynamic compensation according to the compensation effect of the system model, and the compensation effect and the compensation precision of the control system to be compensated are greatly improved.
Further, the system model of the system to be compensated in this embodiment is:
wherein x is i U is the input of the system, g is the state variable of the system i Is a nonlinear parameter d i For uncertain interference parameters.
In the present embodiment, the input u of the system, the nonlinear parameter g, is taken into account by taking into account the state of the system itself to be compensated and the interference factors i Uncertain disturbance parameter d i State variable x of AND system i The system to be compensated can be dynamically compensated accurately through the running state and the compensation effect of the system model, resources are saved, the compensation effect and precision are improved, adverse effects caused by nonlinear factors and uncertain interference are avoided, the stability of the system is limited, the system is uncertain, the control precision is reduced, and the development of high-precision tip technology is restricted.
Step S102, determining whether to dynamically compensate the system to be compensated according to the operation result of the system model.
As described above, by establishing a corresponding system model for the control system to be compensated so as to determine whether to dynamically compensate the control system to be compensated according to the operation result of the system model, it can be understood that the system model obtains the operation environment, the operation parameters and the interference factors of the control system to be compensated in real time, thereby improving the reliability, accuracy and timeliness of compensating the control system to be compensated.
Referring to fig. 2, the method for determining whether to dynamically compensate the system to be compensated according to the operation result of the system model includes the following steps:
and S1021, constructing a low-order expansion state observer according to the uncertain disturbance parameters and approximation errors generated during the construction of the neural network model.
As described above, in order to improve the reliability and traceability of the dynamic compensation of the system to be compensated according to the operation result of the system model, it is necessary to construct a low-order extended state observer according to the uncertain disturbance parameters and the approximation error generated when the neural network model is constructed, wherein the model of the low-order extended state observer is as follows:
further, in order to obtain an interference result of the system for tracking the uncertain interference, the uncertain interference parameter needs to be led into a low-order extended state observer to obtain an interference estimated value, wherein the formula of the interference estimated value is as follows:
step S1022, tracking the uncertain disturbance of the system to be compensated in real time by the extended state observer, and generating a corresponding tracking effect diagram from the tracking result.
As described above, in order to accurately obtain the long-term or staged interference state of the system to be compensated by the uncertain interference, the uncertain interference of the system to be compensated needs to be tracked in real time by the extended state observer, and the tracking result is generated into a corresponding tracking effect diagram, so that the interference result and the interference rule of the system to be compensated by the uncertain interference can be intuitively obtained.
Step S1023, calculating the best matching subgraph with highest matching degree between the tracking effect graph and the standard effect graph.
As described above, in implementation, the trace effect graph is convolved to obtain a matching matrix, and the physical meaning of the matching matrix refers to the matrix of the matching relationship of the trace effect graph relative to the standard effect graph; and then calculating the coordinate corresponding to the maximum value of the matching matrix, and determining a matching subgraph with the highest matching degree in the tracking effect graph according to the coordinate.
And step S1024, determining whether to dynamically compensate the system to be compensated according to the correlation degree of the best matching subgraph and the standard effect graph.
As described above, whether to compensate the band compensation system is determined according to whether the correlation degree between the best matching sub-graph and the standard effect graph is smaller than 90% of the preset correlation degree, and in other embodiments of the present invention, the correlation degree between the best matching sub-graph and the standard effect graph may be adjusted according to actual requirements, which is not limited herein.
Step S103, dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to a compensation controller.
And the corresponding compensation equipment dynamically compensates the system to be compensated according to the operation result of the system model and a preset standard, and feeds back the compensated operation result to the compensation controller so that the compensation controller can analyze, early warn and compensate and adjust according to the compensated operation result.
The step of dynamically compensating the system to be compensated according to the operation result and the preset standard comprises the following steps:
determining a neural network control model according to the nonlinear parameters, wherein the neural network control model is as follows:
calculating a virtual control model according to the neural network control model, wherein the calculating step comprises the following steps:
and determining the linearity of the operation result and a preset standard, and dynamically compensating the system to be compensated through the sliding mode control model.
It can be understood that the system to be compensated is dynamically compensated by the linearity of the operation result and the preset standard, so that adverse effects caused by nonlinear factors and uncertain interference are avoided, the stability of the system is limited, the uncertainty of the system is caused, the control precision is reduced, and the development of high-precision tip technology is restricted. In specific implementation, the effect diagram of the operation result and the effect diagram of the preset standard can be divided into a plurality of areas so as to judge the linearity.
According to the control system compensation method provided by the invention, firstly, nonlinear parameters and uncertain disturbance parameters of a system to be compensated are obtained, a system model of the system to be compensated is determined according to the nonlinear parameters and the uncertain disturbance parameters, and whether the system to be compensated is dynamically compensated is determined according to the operation result of the system model; if yes, dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to a compensation controller. According to the control system compensation method provided by the invention, the nonlinear factors and the uncertain disturbances are dynamically compensated, so that the stability and the control precision of the system are improved; meanwhile, the problems that the system cannot be converged rapidly, the calculated amount is increased and the load is brought to hardware due to approximation errors generated by the conventional neural network control algorithm can be solved, and the actual application requirements are met.
Referring to fig. 3, as a specific embodiment, the mechanical arm has great application value in practical engineering application, and all control systems can be converted into a second-order system for analysis. The effect of the system nonlinear factor and uncertain disturbance dynamic compensation control method provided by the invention is verified by adopting a single-joint mechanical arm second-order system. The system model is as follows:
where J is moment of inertia, u is system input, g 1 And g 2 Is an unknown random interference term, f 1 And f 2 Is external interference x 1 Is the angle, x of the single-joint mechanical arm 2 Is the angular velocity of a single joint mechanical arm.
Determining the extended state observer models ESO-1, ESO-2, interference estimates
And->
Neural network controller->
And->
Virtual controller alpha 1 And u, a sliding mode controller S.
ESO-1:
ESO-2:
Acquiring angle x of single-joint mechanical arm 1 And its expected value x 1r Tracking effect graph (a) of (a), single joint mechanical arm angular velocity x 2 Tracking effect graph (b) of (a) and expected value x thereof 2r And the extended state observer effectively tracks the interference signal through the tracking effect diagram, so as to dynamically compensate the system, thereby effectively ensuring the stability of the system and improving the performance and control precision of the system.
Referring to fig. 4, based on the same inventive concept, a control system compensation device provided in a second embodiment of the present invention includes:
the acquiring module 10 is configured to acquire a nonlinear parameter and an uncertain disturbance parameter of the system to be compensated, and determine a system model of the system to be compensated according to the nonlinear parameter and the uncertain disturbance parameter.
The system model of the system to be compensated is as follows:
wherein x is i U is the input of the system, g is the state variable of the system i Is a nonlinear parameter d i For uncertain interference parameters.
And the judging module 20 is used for determining whether to dynamically compensate the system to be compensated according to the operation result of the system model.
In this embodiment, the judging module 20 includes:
and a construction unit 21, configured to construct a low-order extended state observer according to the uncertain disturbance parameters and an approximation error generated during the construction of the neural network model.
Further, according to the uncertain disturbance parameters and approximation errors generated during the construction of the neural network model, the step of constructing the low-order extended state observer is to guide the uncertain disturbance parameters into the low-order extended state observer so as to obtain a disturbance estimated value. The model of the low-order expansion state observer is as follows:
the formula of the interference estimation value is as follows:
and the generating unit 22 is configured to track the uncertain disturbance of the system to be compensated in real time through the extended state observer, and generate a corresponding tracking effect graph according to the tracking result.
And a calculating unit 23, configured to calculate an optimal matching subgraph with the highest matching degree between the tracking effect graph and the standard effect graph.
And the determining unit 24 is used for determining whether to dynamically compensate the system to be compensated according to the correlation degree between the optimal matching subgraph and the standard effect graph.
And the compensation module 30 is used for dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to the compensation controller.
The formula for determining the neural network control model according to the nonlinear parameters is as follows:
the virtual control model is as follows:
the sliding mode control model is as follows:
according to the control system compensation device provided by the invention, firstly, nonlinear parameters and uncertain disturbance parameters of a system to be compensated are obtained, a system model of the system to be compensated is determined according to the nonlinear parameters and the uncertain disturbance parameters, and whether the system to be compensated is dynamically compensated is determined according to the operation result of the system model; if yes, dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to a compensation controller. According to the control system compensation method provided by the invention, the nonlinear factors and the uncertain disturbances are dynamically compensated, so that the stability and the control precision of the system are improved; meanwhile, the problems that the system cannot be converged rapidly, the calculated amount is increased and the load is brought to hardware due to approximation errors generated by the conventional neural network control algorithm can be solved, and the actual application requirements are met.
The technical features and technical effects of the control system compensation device provided by the embodiment of the present invention are the same as those of the method provided by the embodiment of the present invention, and are not described herein.
Furthermore, an embodiment of the present invention proposes a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described method.
In addition, the embodiment of the invention also provides intelligent equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the method when executing the program.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method of compensating a control system, the method comprising the steps of:
acquiring nonlinear parameters and uncertain disturbance parameters of a system to be compensated, and determining a system model of the system to be compensated according to the nonlinear parameters and the uncertain disturbance parameters; the system model of the system to be compensated is as follows:
wherein x is i U is the input of the system, g is the state variable of the system i Is a nonlinear parameter d i For uncertain disturbance parameters;
determining whether to dynamically compensate the system to be compensated according to the operation result of the system model;
if yes, dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to a compensation controller;
the step of determining whether to dynamically compensate the system to be compensated according to the operation result of the system model comprises the following steps:
constructing a low-order expansion state observer according to the uncertain disturbance parameters and approximation errors generated during the construction of the neural network model;
real-time tracking the uncertain disturbance of the system to be compensated through the extended state observer, and generating a corresponding tracking effect diagram according to the tracking result;
calculating the best matching subgraph with highest matching degree between the tracking effect graph and the standard effect graph;
and determining whether to dynamically compensate the system to be compensated according to the correlation degree of the optimal matching subgraph and the standard effect graph.
2. The control system compensation method of claim 1 wherein the step of constructing a low-order extended state observer based on the uncertain disturbance parameters and an approximation error generated when the neural network model is constructed comprises:
and leading the uncertain interference parameters into a low-order expanded state observer to obtain an interference estimated value, wherein the model of the low-order expanded state observer is as follows:
the formula of the interference estimation value is as follows:
3. the method for compensating a control system according to claim 1, wherein the step of dynamically compensating the system to be compensated according to the operation result and a preset standard comprises:
determining a neural network control model according to the nonlinear parameters;
calculating a virtual control model according to the neural network control model;
calculating a sliding mode control model according to the virtual control model;
and determining the linearity of the operation result and a preset standard, and dynamically compensating the system to be compensated through the sliding mode control model.
4. A control system compensation method according to claim 3, wherein the formula for determining a neural network control model from the nonlinear parameter is:
5. a control system compensation method according to claim 3, wherein the virtual control model is:
the sliding mode control model is as follows:
6. a control system compensation device, the system comprising:
the acquisition module is used for acquiring nonlinear parameters and uncertain disturbance parameters of the system to be compensated and determining a system model of the system to be compensated according to the nonlinear parameters and the uncertain disturbance parameters; the system model of the system to be compensated is as follows:
wherein x is i U is the input of the system, g is the state variable of the system i Is a nonlinear parameter d i For uncertain disturbance parameters;
the judging module is used for determining whether to dynamically compensate the system to be compensated according to the operation result of the system model;
the compensation module is used for dynamically compensating the system to be compensated according to the operation result and a preset standard, and feeding back the compensated operation result to the compensation controller;
the step of determining whether to dynamically compensate the system to be compensated according to the operation result of the system model specifically comprises the following steps:
constructing a low-order expansion state observer according to the uncertain disturbance parameters and approximation errors generated during the construction of the neural network model;
real-time tracking the uncertain disturbance of the system to be compensated through the extended state observer, and generating a corresponding tracking effect diagram according to the tracking result;
calculating the best matching subgraph with highest matching degree between the tracking effect graph and the standard effect graph;
and determining whether to dynamically compensate the system to be compensated according to the correlation degree of the optimal matching subgraph and the standard effect graph.
7. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the control system compensation method according to any one of claims 1 to 5.
8. A smart device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the control system compensation method of any of the preceding claims 1 to 5 when executing the program.
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| CN107662208B (en) * | 2017-08-24 | 2020-07-31 | 浙江工业大学 | Flexible joint mechanical arm finite time self-adaptive backstepping control method based on neural network |
| CN108107719A (en) * | 2017-11-10 | 2018-06-01 | 大连民族大学 | The adaptive sliding-mode observer system of nonlinear system |
| CN108673502A (en) * | 2018-05-18 | 2018-10-19 | 昆明理工大学 | A kind of mechanical arm motion control method based on Unknown Model Dynamic Observer |
| CN108803324B (en) * | 2018-06-06 | 2021-06-04 | 黄山学院 | Multi-joint industrial mechanical arm backstepping finite time sliding mode control method |
| CN109343549A (en) * | 2018-10-09 | 2019-02-15 | 北京航空航天大学 | A kind of Spacecraft Attitude Control, system, medium and equipment |
| CN109465825B (en) * | 2018-11-09 | 2021-12-10 | 广东工业大学 | RBF neural network self-adaptive dynamic surface control method for flexible joint of mechanical arm |
| CN109709807B (en) * | 2018-12-27 | 2020-09-18 | 中科院计算技术研究所南京移动通信与计算创新研究院 | Self-adaptive neural network control method and device based on friction compensation |
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