Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

Lin, Guohuai; Li, Hongyi; Ma, Hui; Yao, Deyin; Lu, Renquan

doi:10.1109/jas.2020.1003596

Cited by 163 publications

(49 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[1][2][3][4] The basic idea is to use the universal approximator to approximate the nonlinear cost function, then the nonlinear Hamilton-Jacobi-Bellman (HJB) equation can be solved, which provides an effective optimal control design method for nonlinear systems. [5][6][7] Therefore, ADP-based optimal control method has attracted much attention and has been applied in a variety of practical engineering systems, for example, power system, 8 unmanned intelligent system, 9 wave energy converter, 10 and so forth. It should be pointed out that one of the most contributions of ADP is that it can effectively overcome the difficulty of traditional dynamic programming in solving complex high-dimensional HJB equation of nonlinear system and avoid the problem of "curse of dimensionality".…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Guo

Yao

Bai

et al. 2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

In this article, an event-triggered guaranteed cost optimal tracking control problem is investigated for a class of uncertain nonlinear system with partial loss of actuator effectiveness faults. To begin with, an augmented system consisted of error system and reference system is constructed to simplify the tracking controller design. Then, in order to consider both actuator faults and system uncertainties in optimal tracking control, an improved discounted cost function is developed. Furthermore, a single critic neural network adaptive dynamic programming algorithm is utilized to implement the event-based approximate optimal controller design. Different from static event-triggered mechanism, the dynamic event-triggered mechanism proposed in this article with an internal dynamic signal can further improve execution efficiency. A Lyapunov-based stability analysis is given to demonstrate the uniformly ultimately bounded stability of the closed-loop system. Finally, two typical nonlinear simulations are presented to verify the developed control method.

show abstract

Section: Introductionmentioning

confidence: 99%

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Guo

Yao

Bai

et al. 2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, a quantized scheme was developed for linear systems in Reference 5, and an adaptive quantizer was designed for stochastic nonlinear systems in Reference 6. Recently, multi‐agent consensus problems have attracted much attention in view of their applications in various disciplines, such as cooperative surveillance, 7 distributed sensor networks, 8 and others 9‐14 …”

Section: Introductionmentioning

confidence: 99%

Adaptive event‐triggered consensus control of multi‐agent systems with prescribed performance and input quantization

Wang

MaHui

Liang

2021

Adaptive Control & Signal

Self Cite

View full text Add to dashboard Cite

Summary This paper focuses on the adaptive fuzzy event‐triggered consensus control problem for multi‐agent systems (MAS) with prescribed performance and input quantization. Based on a prescribed performance function and an input quantization decomposition method, a new adaptive fuzzy event‐triggered consensus protocol is presented. The instances in the event‐triggered mechanism are triggered only when the event‐triggered error exceeds a specified threshold, which can save limited communication resources. It is demonstrated that the event‐triggered control protocol ensures that all signals in the MAS are semi‐globally uniformly ultimately bounded. As a result, the consensus tracking errors converge to prescribed limits. Finally, simulation examples are provided to validate effectiveness of the proposed event‐triggered control methods.

show abstract

“…Several state observers, such as extended state observer (ESO) [36][37][38][39], high gain observer (HGO) [40][41], neural networkbased observer (NNO) [42][43] and fuzzy logic-based observer (FLO) [44][45], have been widely applied to control systems with state estimation problems. In [39], an ESO was designed for the control of an underwater robot with unknown disturbances and uncertain nonlinearities, while a HGO was constructed to observe the speed signals for the output feedback control of an autonomous mobile robot in order to prevent the use of high-cost speed sensors in [41].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Robust Control of a Bionic Mechanical Leg With a High Gain Observer

Liao

Wang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In this paper, an observer-based adaptive fuzzy robust controller is proposed for trajectory tracking control of a bionic mechanical leg (BML) with unmeasured system states, dynamic uncertainties and external disturbances. A high gain observer (HGO) is constructed to estimate the unavailable joint velocities using the joint position feedback signals, while an adaptive fuzzy logic system (AFLS) is employed to address the lumped uncertainties. The nonlinear robust controller is then synthesized via backstepping method to improve the position tracking performance. The stability of the closed loop system is mathematically demonstrated via the Lyapunov's stability theory. It is proven that under the proposed controller all the closed-loop signals are bounded and the trajectory tracking errors converge to a small neighborhood of the origin with appropriate design parameters. The effectiveness of the proposed control scheme is illustrated by simulation studies.

show abstract

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

Cited by 163 publications

References 54 publications

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Adaptive event‐triggered consensus control of multi‐agent systems with prescribed performance and input quantization

Adaptive Fuzzy Robust Control of a Bionic Mechanical Leg With a High Gain Observer

Contact Info

Product

Resources

About