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In this paper, an observer with exponential time-varying gain is proposed for a class of nonlinear systems in Takagi–Sugeno form with both continuous-time and aperiodic sampled outputs. The proposed observer consists of two parts: one related to the continuous-time outputs and one related to the discrete outputs. Sufficient conditions involving a tuning parameter and the maximum allowed sampling time are derived in terms of quasi linear matrix inequality with respect to a scalar to ensure asymptotic convergence of the observer on the basis of Lyapunov–Krasovskii functional and H ∞ approach. Numerical simulations are provided to show the benefits of the proposed observer. Some comparisons with the high gain continuous-discrete observer and fuzzy observer with constant gain are provided to illustrate the interest of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of leaderless consensus control for the multiagent systems whose nonlinear dynamics satisfying incremental quadratic constraints. A distributed dynamic consensus protocol, decided by communication among neighboring agents, is presented to render nonlinear agent consensus with appropriate coupling weights. Next, an observer-based distributed protocol is considered to ensure consensus of nonlinear system without knowing full state information. Further, extensions to consensus strategies with nonlinear dynamics for the leader-following fashion are also addressed. By comparison to the traditional nonlinear consensus control methodologies, the proposed approach generalizes the Lipschitz nonlinearity as well as the combined nonlinearity of one-sided Lipschitz condition and quadratic inner-boundness condition towards a more generalized type of nonlinearity, which shows us a less conservative result in the Lyapunov proof. Finally, the numerical simulations for six agents are illustrated to show the feasibility and performance of the proposed control protocol with or without the presence of the observer. [ABSTRACT FROM AUTHOR]

In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the consensus tracking problem is studied for a group of nonlinear heterogeneous multiagent systems with asymmetric state constraints and input delays. Different from the existing works, both input delays and asymmetric state constraints are assumed to be nonuniform and time-varying. By introducing a nonlinear mapping to handle the problem caused by state constraints, not only the feasibility condition is removed, but also the restriction on the constraint boundary functions is relaxed. The time-varying input delays are compensated by developing an auxiliary system. Furthermore, by utilizing the dynamic surface control method, neural network technology and the designed finite-time observer, the distributed adaptive control scheme is developed, which can achieve the synchronization between the followers' output and the leader without the violation of full-state constraints. Finally, a numerical simulation is provided to verify the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of stabilizing event-triggered controllers for nonlinear systems subject to dynamic quantization. The plant state is assumed to be partially known, and the feedback signal is transmitted to the controller at discrete-time instants via a digital channel. To that end, an event-triggered controller is synthesized based solely on the available plant measurement. In addition, to better adapt it to practical implementation, the event-triggering law we construct is only verified at periodic time instants, i.e., periodic event-triggering mechanism. Since the network is digital, the transmitted output signal must be quantized to be encoded using a finite number of bits. Furthermore, the initial output value is assumed to be unknown, and the plant is affected by external disturbance, which can result in quantizer saturation. To overcome these challenges, a dynamic quantization policy is developed for the output measurement such that the quantization regions are dynamically updated according to the value of the quantized signal. The combined approach of periodic event-triggering mechanism and dynamic quantization ensures the stability of the networked control system under mild conditions. The stability is studied using appropriate Lyapunov functions. Numerical simulations have been conducted to justify the proposed technique. [ABSTRACT FROM AUTHOR]

Practical time-varying formation-containment tracking (TVFCT) issue for nonlinear multi-agent systems with general noise is researched in this paper. A neuro-adaptive practical TVFCT protocol is put forward, where neural networks (NNs) and parameter adaptive laws are developed for estimating the nonlinearities and compensating the upper bound of the time-varying formation (TVF) condition, respectively. Leaders are expected to achieve the predetermined TVF and followers are driven to move into the formation structure confined by leaders simultaneously. The whole formation position depends on the desired trajectory produced by a virtual-leader. General noise rather than white noise is investigated in the multi-agent systems. The noise-to-state-practically stability in probability (NSPS-P) criteria can be guaranteed by employing the Lyapunov theories combined random differential equations (RDEs). Numerical simulation results reveal the effectiveness of the developed protocols. [ABSTRACT FROM AUTHOR]