Distributed command filtered backstepping consensus tracking control of nonlinear multiple-agent systems in strict-feedback form

Shen, Qikun; Shi, Peng

doi:10.1016/j.automatica.2014.12.046

Cited by 254 publications

(144 citation statements)

References 10 publications

Supporting

Mentioning

143

Contrasting

Order By: Relevance

“…Among them, cooperative backstepping adaptive control design via fuzzy logic systems or neural networks (NNs) has received considerable attention due to its ability to deal with MASs in low-triangular structures and high uncertainties. Hence, numerous significant works have been obtained for MASs, for instance, see [1][2][3][4][5]. Distributed adaptive backstepping NN control schemes were proposed for first-order and second-order multiagent systems in [1,2], respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], distributed adaptive fuzzy backstepping controller was developed for high-order multi-agent systems. Since then, various studies have contributed due to the popularity and vast applications of backstepping technique, for example, distributed outputfeedback control of strict-feedback MASs [4] and distributed command filtered backstepping control of strict-feedback MASs [5]. Despite these advances, the control methods in [1][2][3][4][5] suffer from the main problem as 'explosion of complexity', which is caused by repeated differential of intermediate control functions at each step in conventional backstepping method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative adaptive neural partial tracking errors constrained control for nonlinear multi‐agent systems

Shahvali

Askari

2016

Adaptive Control & Signal

View full text Add to dashboard Cite

This paper considers the problem of partial tracking errors constrained for high-order nonlinear multi-agent systems in strict-feedback form. In the control design, radial-based function neural networks are utilized to identify uncertain nonlinear functions, and a cooperative adaptive dynamic surface control is proposed to avoid the explosion of complexity in the backstepping technique. Based on the minimal learning parameter technique and the predefined performance approach, a novel cooperative adaptive neural network control method is developed. The proposed controller is able to guarantee that all the closed-loop network signals are cooperative semi-globally uniformly ultimately bounded, and partial tracking errors confine all times within the predefined bounds. Finally, simulation example and comparative example with previous methods are given to verify and clarify the effectiveness of the new design procedure.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cooperative adaptive neural partial tracking errors constrained control for nonlinear multi‐agent systems

Shahvali

Askari

2016

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…Denote η as a positive constant satisfying η > ε/λ min , where ε is given in (8) and λ min is the smallest non-zero eigenvalue of L + G. The auxiliary control functions K i2 (t) can be designed as follows (12) wherek ij are the estimation values of unknown parameters of k ij , j = 2, . .…”

Section: Distributed Adaptive Fault-tolerant Protocol Designmentioning

confidence: 99%

“…However due to the existence of time -varying norm bounded uncertainties A and R m (t), the gain η should be chosen to satisfy η > ε/λ min . We can adjust ε sufficiently small to satisfy (8), and choose η sufficiently large to make η > ε/λ min hold.…”

Section: Theoremmentioning

confidence: 99%

“…Thus, various types of effective approaches have been developed to deal with consensus problem in the leader-follower framework [6][7][8][9][10][11][12][13][14][15][16]. Since in some practical circumstances the agents dynamics may be subject to uncertainties or external disturbances, the assumption that the considered model precisely represents the actual system and is exactly known in [12][13][14] is restrictive to some extent.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed adaptive fault‐tolerant consensus tracking of multi‐agent systems against time‐varying actuator faults

Zhao

Cao

2016

IET Control Theory & Applications

View full text Add to dashboard Cite

This study considers the distributed fault-tolerant consensus problem for uncertain multi-agent systems using adaptive protocol. A more general time-varying actuator fault model is given, which includes loss of effectiveness, stuck, bias and outage fault. A new distributed adaptive control scheme is developed to compensate the effect of fault, multiple delayed state perturbations, mismatched parameter uncertainties and external disturbances on leader-follower multiagent systems. Based on the local state information of neighbouring agents, the adaptive updating protocol gains are adjusted online, which remove the assumption that the upper bounds of unknown uncertainty, delayed state perturbation and external disturbances should be known. Moreover, the consensus errors of leader-follower systems can asymptotically converge to zero. Finally, a simulation example is given to show the effectiveness of the theoretical analysis.

show abstract

Hybrid online learning control in networked multiagent systems: A survey

Poveda

Benosman

Teel

2018

Adaptive Control & Signal

View full text Add to dashboard Cite

This survey paper studies deterministic control systems that integrate three of the most active research areas during the last years: (1) online learning control systems, (2) distributed control of networked multiagent systems, and (3) hybrid dynamical systems (HDSs). The interest for these types of systems has been motivated mainly by two reasons: First, the development of cheap massive computational power and advanced communication technologies, which allows to carry out large computations in complex networked systems, and second, the recent development of a comprehensive theory for HDSs that allows to integrate continuous-time dynamical systems and discrete-time dynamical systems in a unified manner, thus providing a unifying modeling language for complex learning-based control systems. In this paper, we aim to give a comprehensive survey of the current state of the art in the area of online learning control in multiagent systems, presenting an overview of the different types of problems that can be addressed, as well as the most representative control architectures found in the literature. These control architectures are modeled as HDSs, which include as special subsets continuous-time dynamical systems and discrete-time dynamical systems. We highlight the different advantages and limitations of the existing results as well as some interesting potential future directions and open problems. KEYWORDSadaptive control, hybrid dynamical systems, learning, multiagent systems, networked systems 228One of the fields where control systems with learning and adaptation have recently seen increased attention is in the area of networked multiagent systems (MAS), 16,17 which are systems comprised of multiple interacting subsystems, each subsystem having individual dynamics and computational capabilities as well as limited sensing, communication, and actuation. Depending on the structure of the underlying communication graph between the agents of the system, MAS can be centralized or decentralized. In a centralized MAS, there exists one central agent which has access to the state information of all other agents and which is able to compute the control action for all agents of the system. On the other hand, in a decentralized or distributed MAS, agents individually compute their own control signals, which are shared with a subset of the other agents characterized by a communication graph. Figure 1 illustrates three MASs with different communication graphs. Centralized MASs are typically not scalable, and they present the disadvantage of having a single point of failure that could potentially shut down the complete system. 16 Decentralized and distributed systems, on the other hand, are scalable and more robust, and in nonstationary environments, they allow for adaptation and self-organization of the system. 18 Recent technological advances in communication and computation have made MAS ubiquitous, and today, they can be found in several engineering systems such as in power generation and distribution systems, 19-21 teams of ...

show abstract

Distributed command filtered backstepping consensus tracking control of nonlinear multiple-agent systems in strict-feedback form

Cited by 254 publications

References 10 publications

Cooperative adaptive neural partial tracking errors constrained control for nonlinear multi‐agent systems

Cooperative adaptive neural partial tracking errors constrained control for nonlinear multi‐agent systems

Distributed adaptive fault‐tolerant consensus tracking of multi‐agent systems against time‐varying actuator faults

Hybrid online learning control in networked multiagent systems: A survey

Contact Info

Product

Resources

About