Improved High-Order Model Free Adaptive Control

Xu, Jing; Lin, Na; Chi, Ronghu

doi:10.1109/ddcls52934.2021.9455488

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…The efficacy of the model-free adaptive learning solution is tested using: (i) a linear system with state and input delays and (ii) a nonlinear system. Further, two model-following approaches based on sliding mode and high-order modelfree adaptive control schemes are considered for comparison purposes [10], [20].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this case, our RL solution will be compared to an improved high-order Model Free Adaptive Control (MFAC) approach [20]. This is simulated using a nonlinear dynamical process described by…”

Section: B Case 2: Nonlinear Systemmentioning

confidence: 99%

“…Reinforcement Learning (RL) mechanisms have not been fully investigated to develop model-following adaptive control strategies [20]. RL is concerned with guiding the agent towards the best strategies after interactions with the environment to maximize (minimize) a cumulative reward (cost) [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

“…Gradient approaches are used to tune the actor and critic weights. The adaptation schemes can vary depending on the desired function approximation structure and the underlying solutions employ supporting conditions such as in [20], where a pre-designed strategy that requires resetting conditions is considered. In [21], the tracking problem is solved for linear-time invariant systems, where it employed a Q-learning method for an overall augmented system.…”

Section: Introductionmentioning

confidence: 99%

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An Online Model-Following Projection Mechanism Using Reinforcement Learning

Abouheaf,

Hashim,

Mayyas

et al. 2023

Preprint

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In this paper, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control solution. It utilizes a moving finite-horizon of modelfollowing error measurements. In addition, the control strategy is designed by using a projection mechanism that employs Lagrange dynamics. It allows for real-time tuning of derived actor-critic structures to find the optimal model-following strategy and sustain optimized adaptation performance. Finally, the efficacy of the proposed framework is emphasized through a comparison with sliding mode and high-order model-free adaptive control approaches.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: B Case 2: Nonlinear Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Online Model-Following Projection Mechanism Using Reinforcement Learning

Abouheaf,

Hashim,

Mayyas

et al. 2023

Preprint

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show abstract

“…Hence, the MFAC can not only avoid occurrence of potential poor control performance caused by mismatching between the pre-selected controller and the system dynamic characteristics, but also enhance both adaptability and robustness of the closed-loop system significantly. Due to the incomparable advantages, the MFAC has been successfully applied in various fields such as liquid level control [12], parafoil systems [13], high-order systems [14], windintegrated power system [15], and multi-area power systems [16] and so forth. Control performance of the closed-loop systems based on the MFAC also has been proved to be better than the ones of some approaches in the first catalog such as the conventional PID [17], the VRFT [18], and the IFT [18].…”

Section: Introductionmentioning

confidence: 99%

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

Wang

et al. 2022

Preprint

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This paper proposes a novel model-free adaptive predictive control approach with low online computational load for of a class of unknown discretetime nonlinear multiple-input multiple-output systems. First, historical measurable input/output data is utilized to online construct equivalent explicit data models for control design, making the controllers model-free. Second, input structuralization where system inputs are expressed by linear combination of a few basis functions is integrated into predictive controller design for reducing online computational load. Third, a novel Kalman Filter-based regression factor computation approach is developed to predict data model parameters for realizing predictive function of controllers such that closed-loop system robustness can be enhanced when sudden changes from prescribed references are encountered. Last, numerical simulations validate that, by using much less online computational load, the proposed approach can achieve equivalent control performance compared with the existing approaches.

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General regression neural network-based data-driven model-free predictive functional control for a class of discrete-time nonlinear systems

Wang

Zhang

2021

Nonlinear Dyn

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Improved High-Order Model Free Adaptive Control

Cited by 10 publications

References 18 publications

An Online Model-Following Projection Mechanism Using Reinforcement Learning

An Online Model-Following Projection Mechanism Using Reinforcement Learning

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

General regression neural network-based data-driven model-free predictive functional control for a class of discrete-time nonlinear systems

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