Adaptive optimal output regulation of linear discrete-time systems based on event-triggered output-feedback

Zhao, Fuyu; Gao, Weinan; Liu, Tengfei; Jiang, Zhong‐Ping

doi:10.1016/j.automatica.2021.110103

Cited by 22 publications

(15 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, there has been extensive studies on the event-triggered mechanism. [27][28][29][30][31][32][33][34][35][36][37][38] In Reference 27, Zhong et al introduced a classical triggering condition into the ADP algorithm, for addressing the optimal regulation problem for unknown nonlinear continuous-time plants. In Reference 28, an event-based self-learning controller was investigated for unknown nonlinear plants and the detailed stability proof was given.…”

Section: Introductionmentioning

confidence: 99%

“…As an advanced sampling approach, the essence of the event‐triggered mechanism is to decide the controller update by choosing an appropriate triggering condition, which achieves the purpose of saving energy. To the best of our knowledge, there has been extensive studies on the event‐triggered mechanism 27‐38 . In Reference 27, Zhong et al introduced a classical triggering condition into the ADP algorithm, for addressing the optimal regulation problem for unknown nonlinear continuous‐time plants.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

Wang

Qiao

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

In this article, an event-based intelligent critic algorithm is developed to address the optimal tracking control problem for a type of discrete-time nonlinear systems. The nonlinear optimal tracking control design is replaced by solving the optimal regulation problem of the error system. Then, the generalized value iteration algorithm is employed to obtain the admissible tracking control law with off-line learning. Next, a novel triggering condition is designed to reduce the update times of the controller and improve the resource utilization. It is emphasized that this triggering condition is established based on the iteration of the time-triggered mechanism. Moreover, in order to realize the cost guarantee of the error system, the real cost function is proved to possess a predetermined upper bound. By analysis, it is shown that the error system is asymptotically stable while the tracking error and the sampling signal are uniformly ultimately bounded during the process of training neural networks. Finally, two examples are conducted to demonstrate the effectiveness of the proposed algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

Wang

Qiao

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…12,13 As stated in the survey, 14 ADP algorithms in terms of iteration can be divided into two categories: value iteration 15,16 and policy iteration. 17,18 So far, a large number of results based on ADP have been obtained to solve various control problems, such as optimal control with constrained control inputs, [19][20][21] optimal tracking control, [22][23][24] networked control, 25 robust control, 26,27 and event-triggered control, [28][29][30] which strongly show the applicability and great potential of ADP algorithms. In Reference 16, the convergence of the adaptive critic algorithm was proven and the algorithm procedure was given.…”

Section: Introductionmentioning

confidence: 99%

Multi‐event‐triggered adaptive critic control with guaranteed cost for discrete‐time nonlinear nonzero‐sum games

Wang

Qiao

2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, a new event‐based adaptive critic algorithm with multiple triggering conditions is investigated to address multi‐player nonzero‐sum game problems for discrete‐time nonlinear dynamics. In order to improve resource utilization while ensure mutual independence among players, the corresponding novel triggering conditions are designed for each player. The corresponding control input is updated only when the relevant triggering condition is violated. It is emphasized that these triggering conditions are established based on the iteration of the time‐triggered mechanism. Then, according to the setting triggering conditions, we prove that the real cost function possesses a predetermined upper bound, which realizes the cost guarantee of the controlled system. Additionally, the multi‐player closed‐loop system is proved to be asymptotically stable and the multi‐event‐triggered control method is implemented by constructing three kinds of neural networks. Finally, the effectiveness of the developed multi‐event‐triggered control approach is verified through conducting two simulation examples.

show abstract

“…Due to its universal approximation and self‐learning, RL has become a popular tool in dealing with optimal control problem 20‐22 . Up to now, many highlighted results have been published to develop the OORP 23‐31 . For example, the linear OORP was addressed in References 23 and 24, and its discrete version was discussed in Reference 25.…”

Section: Introductionmentioning

confidence: 99%

“…Up to now, many highlighted results have been published to develop the OORP 23‐31 . For example, the linear OORP was addressed in References 23 and 24, and its discrete version was discussed in Reference 25. In Reference 26, the RL‐based nonlinear OORP was discussed, where the feedforward design approach and RL techniques were integrated for the first time.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning‐based robust optimal output regulation for constrained nonlinear systems with static and dynamic uncertainties

Jin

Zhou

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article investigates the robust optimal output regulation problem for the constrained uncertain nonlinear systems. A two‐step design framework is proposed to overcome the difficulties brought by uncertainties, constraint and performance optimization. First, the feedforward controller is designed by the internal model principle, and the robust output regulation problem is transformed into the robust stabilization problem. Then, the optimal control problem with input saturation is further considered in the robust feedback controller design process. With the help of a non‐quadratic cost functional, actor‐critic algorithm and robust redesign technique are brought together to design the constrained robust optimal feedback controller. Finally, stability analysis based on Lyapunov method shows that all the signals of the closed‐loop system remain bounded, and the tracking error is uniformly ultimately bounded with arbitrarily small ultimate bound. Simulation results illustrate the effectiveness of the proposed methodology.

show abstract

Adaptive optimal output regulation of linear discrete-time systems based on event-triggered output-feedback

Cited by 22 publications

References 41 publications

Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

Multi‐event‐triggered adaptive critic control with guaranteed cost for discrete‐time nonlinear nonzero‐sum games

Reinforcement learning‐based robust optimal output regulation for constrained nonlinear systems with static and dynamic uncertainties

Contact Info

Product

Resources

About