Data learning‐based model‐free adaptive control and application to an NAO robot

Liu, Shida; Li, Zhen; Ji, Honghai; Hou, Zhongsheng; Chen, Lu

doi:10.1002/rnc.6537

Cited by 7 publications

(2 citation statements)

References 56 publications

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“…In fact, offline data of controlled objects can also be used reasonably, as described in relevant literature such as reference 34 and. 36 The application of offline data can be added according to the actual situation to increase the control effect of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

An improved compact-form antisaturation model-free adaptive control algorithm for a class of nonlinear systems with time delays

Wu,

Li,

Liu

et al. 2023

Science Progress

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To solve the time-delay problem and actuator saturation problem of nonlinear plants in industrial processes, an improved compact-form antisaturation model-free adaptive control (ICF-AS-MFAC) method is proposed in this work. The ICF-AS-MFAC scheme is based on the concept of the pseudo partial derivative (PPD) and adopts equivalent dynamic linearization technology. Then, a tracking differentiator is used to predict the future output of a time-delay system to effectively control the system. Additionally, the concept of the saturation parameter is proposed, and the ICF-AS-MFAC controller is designed to ensure that the control system will not exhibit actuator saturation. The proposed algorithm is more flexible, has faster output responses for time-delay systems, and solves the problem of actuator saturation. The convergence and stability of the proposed method are rigorously proven mathematically. The effectiveness of the proposed method is verified by numerical simulations, and the applicability of the proposed method is verified by a series of experimental results based on double tanks.

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Section: Discussionmentioning

confidence: 99%

An improved compact-form antisaturation model-free adaptive control algorithm for a class of nonlinear systems with time delays

Wu,

Li,

Liu

et al. 2023

Science Progress

Self Cite

View full text Add to dashboard Cite

show abstract

“…The coefficient may decrease when the bacteria progress from the exponential growth phase to the stationary phase, as shown in the plot at time 60 hours to 80 hours. In addition, considering the control problems of batch processes that may exist in the cases of expectation setting, repetitive operation, and under incomplete process information, further research directions include parameter optimization for data-driven setting tuning MFAC [35], data-driven control in a two-dimensional framework [36], control methods under incomplete information [37], control methods based on active learning [38] dynamic data reconciliation [39], etc.…”

Section: Fermentation Process Of Pso-mfac Controllermentioning

confidence: 99%

Particle Swarm Optimization-Based Model-Free Adaptive Control for Time-Varying Batch Processes

Wang,

Sadun,

Jalaludin

et al. 2024

Int. J. Automot. Mech. Eng.

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The batch process is a production process with strong nonlinearity, which usually suffers from time-varying parameters and uncertainty of disturbances. Concerning the mentioned problems, this study proposes to investigate the application of the particle swarm optimization-based model-free adaptive control (PSO-MFAC) method for time-varying batch processes. Model-Free Adaptive Control (MFAC) is a data-driven control method, which is one of the promising methods to solve the nonlinear process. Firstly, a Full Form Dynamic Linearization Model-Free Adaptive Control method has been adopted for the control of batch processes. Further, considering that the adopted model-free adaptive control involves seven control parameters, such as cognitive scaling factor (φ1), social scaling factor (φ2), inertia weight (φ3), learning rate (η), control parameter update rate, exploration rate and learning rate for MFAC obtained by a particle swarm optimization (PSO) algorithm in combination with a criterion function performance index. Finally, by comparing it with the existing methods, a typical batch fermentation was applied to verify that PSO-MFAC had a good control effect. The findings indicate that the PSO-MFAC controller exhibits a preference for exploiting the optimal option due to its φ3 value less than 0.1. The efficacy and feasibility of the PSO-MFAC control effect have been proven by obtaining the lowest integral square error (ISE) value of 1.1192 regarding the nonlinearity of the batch process due to time-varying challenges.

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A novel hybrid observer‐based model‐free adaptive high‐order terminal sliding mode control for robot manipulators with prescribed performance

Zha,

Wang,

Tian

et al. 2024

Intl J Robust & Nonlinear

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Although widely used in industrial applications, strong nonlinearity and coupling, high computational complexity prevent high precision tracking control of manipulator. In this paper, to overcome the rely on system model and achieve prescribed convergence, a novel hybrid observer‐based model‐free adaptive high‐order fast terminal sliding model control scheme (HO‐MHTSMC) with prescribed performance is proposed for trajectory tracking control of robot manipulators in the existence of friction and external disturbance. The ultra‐local model is used to approximate the original complex system in a model free form in a short sliding time window, which avoid the accurate modeling of the manipulator system. To compensate for the lumped uncertainties, a hybrid observer based on adaptive time‐delay estimation and adaptive second order sliding mode observer (SOSM) is proposed to achieve finite‐time observation and zero estimation error. Besides, a transformation using prescribed performance function is applied to the system to ensure the transient and steady‐state performance of the trajectory tracking in joint space. Furthermore, a high‐order fast terminal sliding mode control algorithm with backstepping control strategy is used to stabilize the whole system and reduce the chattering problem in conventional sliding mode control. The stability analysis of the system is provided by Lyapunov theorem. Finally, numerical study and co‐simulations show that the proposed control scheme has better performance in tracking accuracy and robustness compared with conventional control schemes.

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Data learning‐based model‐free adaptive control and application to an NAO robot

Cited by 7 publications

References 56 publications

An improved compact-form antisaturation model-free adaptive control algorithm for a class of nonlinear systems with time delays

An improved compact-form antisaturation model-free adaptive control algorithm for a class of nonlinear systems with time delays

Particle Swarm Optimization-Based Model-Free Adaptive Control for Time-Varying Batch Processes

A novel hybrid observer‐based model‐free adaptive high‐order terminal sliding mode control for robot manipulators with prescribed performance

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