Event‐triggered decentralized optimal fault tolerant control for mismatched interconnected nonlinear systems through adaptive dynamic programming

This article presents a decentralized neuro‐control scheme for a class of continuous‐time nonlinear systems with mismatched interconnections through an optimal control method. The decentralized control problem of the studied nonlinear‐interconnected systems is transformed into a group of optimal control problems of auxiliary subsystems. First, the value functions of auxiliary subsystems are designed, which are related to the boundaries of interconnected terms. It is proved that, under certain conditions, the decentralized control consisting of optimal control policies of auxiliary subsystems can stabilize the entire interconnected system. Then, an adaptive dynamic programming algorithm is presented to solve the Hamilton–Jacobi–Bellman equations. Critic neural networks (NNs) are employed to approximate the value functions of auxiliary subsystems in order to acquire the optimal control policies. Moreover, the auxiliary subsystems' states and the critic NNs' weight estimation errors are proved to be uniformly ultimately bounded. Finally, a numerical example and an interconnected power system are provided to validate the proposed decentralized neuro‐control strategy.

Section: Discussionmentioning

confidence: 99%

“…Recently, the event‐triggering mechanism has been introduced to design decentralized controllers 45‐47 . A core characteristic of the event‐triggered control is that it can reduce the computational burden and communication cost 48 .…”

Section: Discussionmentioning

confidence: 99%

Adaptive dynamic programming for decentralized neuro‐control of nonlinear systems subject to mismatched interconnections

Tang

Hong

Dong

2022

“…[1][2][3] Optimal control means that the performance index can be minimized while obtaining the optimal control objective, has aroused extensive attentions and researches. [4][5][6][7][8][9][10] Theoretically, a core challenge for nonlinear systems with optimal control problems is for solving the Hamilton-Jacobi-Bellman (HJB) equation. 11 Fortunately, the reinforcement learning (RL) is an effective way to overcome this difficulty.…”

Section: Introductionmentioning

confidence: 99%

“…Based on adaptive fuzzy control strategy, many pioneering works have been presented for nonlinear systems subject to unmeasured states 1–3 . Optimal control means that the performance index can be minimized while obtaining the optimal control objective, has aroused extensive attentions and researches 5‐10 . Theoretically, a core challenge for nonlinear systems with optimal control problems is for solving the Hamilton–Jacobi–Bellman (HJB) equation 11 .…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning‐based optimized backstepping control for strict‐feedback nonlinear systems subject to external disturbances

Yan

Cao

et al. 2023

This article investigates a reinforcement learning‐based optimal backstepping control strategy for strict‐feedback nonlinear systems, which contain output constraints, external disturbances and uncertain unknown dynamics. The simplified reinforcement learning algorithm with the identifier‐critic‐actor architecture is constructed in the control design to build optimal virtual and actual controllers. To compensate for the disturbance, a lemma is adopted to transform external disturbances into an unknown “bounding functions‘’, which satisfy a triangular condition. Moreover, the unknown nonlinear functions, which composed of unknown dynamics and external disturbances, approximated by neural networks. Meanwhile, in order to avoid violating output constraints, a barrier‐type Lyapunov function approach is integrated into the optimal control strategy to satisfy output constraints requirements under the framework of backstepping technique. Furthermore, the presented optimal control strategy guarantees that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded. Finally, the effectiveness of the proposed optimal control approach is performed by a numerical example.

“…There are also available ADP techniques [36][37][38][39][40][41][42][43] to find solutions of optimal tracking problems with partially unknown, completely unknown or uncertain system dynamics. Based on ADP algorithms, there has already existed many related industrial applications like: coal gasification, 44 residential energy systems, 45 microgrid, 46 air conditioning systems, 47 shield tunneling machine 48 and mismatched interconnected nonlinear systems 49 in recent years. In this article, inspired by the boiler-turbine control system established by Astrom and Bell 3 and tracking control ADP technique for CT systems by, 21,24 an optimal controller for boiler-turbine systems using policy iteration-based IRL method is designed to solve the tracking problem of boiler turbine systems.…”

Section: Introductionmentioning

confidence: 99%

A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler‐Turbine Systems

Wei

Liu

et al. 2021

Optimal control theory and reinforcement learning are gradually being used in the field of industrial control. In this article, a new optimal tracking control scheme for 160 MW boiler-turbine systems is proposed based on an online policy iteration integral reinforcement learning (IRL) method. Firstly, a self-learning state tracking control with a cost function is developed to deal with the optimal tracking control problems for the boiler-turbine nonlinear system. Then with a modified cost function, a policy iteration-based IRL method is introduced to obtain the optimal control law. Finally, the monotonicity and the convergence of the cost function is analyzed and the detailed implementation of the policy iteration-based IRL method is provided via neural networks. The simulation results show that the control of the boiler-turbine system can converge in a short time by using this online iterative method. Through a theoretical simulation case, it can be concluded that the proposed method is more advanced compared with the MPC method.