Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multiagent Systems

Abstract

Combined with backstepping techniques, an observer-based adaptive consensus tracking control strategy is developed for a class of high-order nonlinear multiagent systems, of which each follower agent is modeled in a semi-strict-feedback form. By constructing the neural network-based state observer for each follower, the proposed consensus control method solves the unmeasurable state problem of high-order nonlinear multiagent systems. The control algorithm can guarantee that all signals of the multiagent system are semi-globally uniformly ultimately bounded and all outputs can synchronously track a reference signal to a desired accuracy. A simulation example is carried out to further demonstrate the effectiveness of the proposed consensus control method.

On finite-time consensus objectives in time-varying interconnected discrete linear dynamic systems under internal and external delays

“…1 In Saber et al, 2 the concept of cooperation is interpreted as giving consent to providing a state and following a common protocol that serves the group objectives. In Philip Chen et al, 5 the consensus objective is basically asymptotic and the related design is based on observers. However, the problem of designing optimal protocols, associated uniqueness and asymptotic reachability is focused on in Li et al 7 Also, an interpretation of consensus to the light of the synchronization of several coupled Kuramoto's modeled oscillators is focused on in Strogatz.…”

“…2,3 However, a quantized consensus problem of undirected time-varying graphs is proposed while a protocol with fast convergence time to consensus points is used for that purpose. 4 In Philip Chen et al, 5 an observer-based adaptive back-stepping scheme is proposed for multiagent consensus, while a nonlinear consensus protocol for optimal distributed networks of dynamic agents is proposed and discussed in Bauso et al 6 In Li et al, 7 each agent within the multi-agent structure shares information with a subset of the remaining agents. In Zhu and colleagues, [8][9][10] a consensus of fractional-order multi-agent systems is reached via observer-type protocols while the consensus objective is formulated for multi-agent fuzzy dynamic systems with dropout compensation in Han et al 11 It can be pointed out that there are apparent conceptual differences between the consensus objective and the master-slave objective.…”

Dynamic surface tracking controller design for a constrained hypersonic vehicle based on disturbance observer

“…In the studies by Liu et al, 41,42 the discrete-time Lyapunov function is chosen for the stability analysis of discrete-time system. In the literature, [43][44][45] for interconnected system, the Lyapunov function including adaptive estimation error and approximation error is chosen to analyze stability. And in the study by Chen et al, 46 for nonlinear stochastic nonlinear pure feedback systems, using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set.…”

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

“…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.…”

“…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.…”