Event-Triggered Adaptive Dynamic Programming for Unmatched Uncertain Nonlinear Continuous-Time Systems

Xue, She‐Sheng; Luo, Biao; Liu, Derong

doi:10.1109/tnnls.2020.3009015

Cited by 68 publications

(33 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since ||E ( k )|| = 0, we have  = 0. Then, (46) becomes ||E (t)|| ≤ 1 2 || ||(e 2 (t− k ) − 1). If the event-triggering condition ( 30) is violated, that is, Ê (t) < ||E (t)||, it can be deduced as follows…”

Section: Nonexistence Of the Zeno Behaviormentioning

confidence: 99%

“…Generally, the design rules are adaptive, which are closely related to the current state, sampling state and input of the system, rather than relying on the sampling time just like time-triggered control. Significant work has also emerged in the field of ADP research, such as optimal regulation, [36][37][38][39][40] tracking control, [41][42][43] and robust control, [44][45][46] etc. [47][48][49][50] However, there is less work to combine IRL with event-triggered control, and some existing works are on optimal regulation, 39 robust control, 45 and non-zero-sum games.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive dynamic programming‐based event‐triggered optimal tracking control

Xue

Luo

Liu

2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

In this article, an event-triggered constrained optimal tracking control algorithm using integral reinforcement learning (IRL) is developed. First, the constrained optimal tracking control problem is transformed into an optimal regulation problem by employing an augmented system with a discounted value function. Then, IRL is introduced to solve the Hamilton-Jacobi-Bellman equation, where the drift dynamics and reference dynamics are not required. The learning of neural network weights is event-triggered and there is no restriction on the initial control to be admissible. The involvement of event-triggering mechanism alleviates the pressure of data transmission on the network to some extent, which is suitable for control systems with limited computational and communication resources. Moreover, the nonexistence of Zeno behavior and the stability of the impulsive system are proved, respectively. Finally, the application of the algorithm on a mass-spring-damper system verifies its effectiveness.

show abstract

Section: Nonexistence Of the Zeno Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive dynamic programming‐based event‐triggered optimal tracking control

Xue

Luo

Liu

2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…erefore, compared with the control algorithms based on time-triggered scheme, the event-trigger control algorithms can efficiently save the computation resources [25]. In the past years, ETC is introduced to solve the optimal control problem under the limited computing resources [26][27][28][29]. In [26], an ETC method based on ADP is developed for continuous-time MASs.…”

Section: Introductionmentioning

confidence: 99%

“…e multiplayer zero-sum differential games are considered in [28] and an optimal consensus tracking control based on event-triggered is designed to solve this problem. In [29], an event-triggered optimal control algorithm is designed for unmatched uncertain nonlinear continuous-time systems. In [30], to save the limited network resources, an event-triggered mechanism was introduced to address the consensus problem of linear discrete-time MASs.…”

Section: Introductionmentioning

confidence: 99%

Event‐Triggered Adaptive Dynamic Programming Consensus Tracking Control for Discrete‐Time Multiagent Systems

et al. 2022

View full text Add to dashboard Cite

This paper proposes a novel adaptive dynamic programming (ADP) approach to address the optimal consensus control problem for discrete-time multiagent systems (MASs). Compared with the traditional optimal control algorithms for MASs, the proposed algorithm is designed on the basis of the event-triggered scheme which can save the communication and computation resources. First, the consensus tracking problem is transferred into the input-state stable (ISS) problem. Based on this, the event-triggered condition for each agent is designed and the event-triggered ADP is presented. Second, neural networks are introduced to simplify the application of the proposed algorithm. Third, the stability analysis of the MASs under the event-triggered conditions is provided and the estimate errors of the neural networks’ weights are also proved to be ultimately uniformly bounded. Finally, the simulation results demonstrate the effectiveness of the event-triggered ADP consensus control method.

show abstract

“…In order to balance the desired performance and limited resources, event-triggered control (ETC) and self-triggered control (STC) have received great interests in the past few years [4,7,28]. For ETC scheme, the additional hardware is utilized to detect the current system state so that the next event time can be triggered, while for STC scheme, the next triggering instant is precomputed in advance based on sampling information.…”

Section: Introductionmentioning

confidence: 99%

Triggering and Control Co-design of Nonlinear Systems with External Disturbances Using Adaptive Dynamic Programming

Zou

2021

Circuits Syst Signal Process

View full text Add to dashboard Cite

This paper proposes a joint optimization strategy for nonlinear systems subject to unknown external disturbances. The co-design problem is formulated as a two-player zero-sum game, where the control policy is treated as the first player and the control input error caused by aperiodic feedback is treated as the second player. Besides, a robust term is incorporated to the performance index to suppress the negative effect of external disturbances. Based on the game theory, the optimal performance index and the solution to the associated Hamilton-Jacobi-Isaacs (HJI) equation can be obtained. Then, the sampling intervals are optimized by designing an event-triggered condition according to Lyapunov direct method. Furthermore, a self-triggered strategy is introduced to predict the next triggering instant in advance, avoiding the requirement of continuous state measurements. Through critic-only neural network (NN) implementation, the event-based HJI equation is approximated by using adaptive dynamic programming technique. The closed-loop nonlinear system and the weight estimation error for the critic NN are both guaranteed to be uniformly ultimately bounded under the proposed aperiodic sampling mechanism. Finally, simulation results and comparison studies demonstrate the effectiveness of the proposed co-design approach.

show abstract

Event-Triggered Adaptive Dynamic Programming for Unmatched Uncertain Nonlinear Continuous-Time Systems

Cited by 68 publications

References 40 publications

Adaptive dynamic programming‐based event‐triggered optimal tracking control

Adaptive dynamic programming‐based event‐triggered optimal tracking control

Event‐Triggered Adaptive Dynamic Programming Consensus Tracking Control for Discrete‐Time Multiagent Systems

Triggering and Control Co-design of Nonlinear Systems with External Disturbances Using Adaptive Dynamic Programming

Contact Info

Product

Resources

About