In this article, we consider the leader-follower consensus control problem of uncertain multiagent systems, aiming to achieve the improvement of system steady state and transient performance. To this end, a new adaptive neural control approach is proposed with a novel design of the Lyapunov function, which is generated with a class of positive functions. Guided by this idea, a series of smooth functions is incorporated into backstepping design and Lyapunov analysis to develop a performance-oriented controller. It is proved that the proposed controller achieves a perfect asymptotic consensus performance and a tunable L₂ transient performance of synchronization errors, whereas most existing results can only ensure the stability. Simulation demonstrates the obtained results.

In this paper, an adaptive neural network control for stochastic nonlinear systems with uncertain disturbances is proposed. The neural network is considered to approximate an uncertain function in a nonlinear system. And computational burden in operation is reduced by handling the norm of the neural-network vector. However, it will arise chattering issue, which is a challenge to avoid it from the symbolic operation. Further, traditional schemes often view error of estimate as bounded constant, but it is a time-varying function exactly, which may lead control schemes cannot conform to practical situation and guarantee stability of systems. Thus, backstepping technology and the neural network technology combined to stabilize stochastic nonlinear systems together to handle the aforementioned issues. It is proved that the proposed control scheme can guarantee the satisfactory asymptotic convergence performance and predetermined transient tracking error performance. From simulation results, the proposed control scheme is verified that can guarantee the satisfactory effectiveness.

In this paper, we consider the fuzzy controller design problem for output tracking of uncertain strict-feedback nonlinear systems. For comparison, a basic fuzzy control scheme is firstly constructed based on an existing approach representative in the field. The scheme is advantaged to reduce the number of parameter estimates, and this advantage indeed benefits from the operation of handling the square of the norm of fuzzy weight vector. However, as a tradeoff of such an operation, the asymptotic tracking performance cannot be ensured and the $L_2$ -norm transient performance of tracking error cannot be established. To eliminate these performance costs and not lose the advantage of the basic fuzzy control scheme, we further propose a performance-oriented fuzzy control scheme in this paper. It is shown that the closed-loop signal boundedness and the asymptotic convergence of tracking error to a prescribed interval are ensured with the proposed performance-oriented fuzzy control design. Moreover, the $L_2$ -norm transient performance of tracking error is also established, which explicitly indicates that the transient performance of tracking error can be improved through changing controller design parameters with the performance-oriented fuzzy control scheme. Simulation results will be given to verify the obtained results.

As the control tasks become complex, fulfilling such tasks cooperatively is the first choice in practice. In this article, the output consensus problem of heterogeneous multiagent systems is studied by deploying distributed observers in follower agents. Each observer in the follower only measures part of the leader’s output, which relieves the burden of a simple agent when the leader’s output is of large-scale dimensions. Then, all followers in the system work cooperatively to estimate the full state of the leader. By using parameterized Riccati equation and output regulation theory, sufficient conditions are given to design the distributed observers and the output consensus protocol. Finally, a numerical example is conducted to verify the obtained result.

Most of the available results on adaptive control of uncertain nonlinear systems with input dead-zone characteristics are for canonical nonlinear systems whose relative degrees are explicit and for which a Lyapunov-based backstepping design is directly applicable. However, those results cannot be applied to noncanonical form nonlinear systems whose relative degrees are implicit and for which a Lyapunov-based backstepping design may not be applicable. This article solves the adaptive control problem of a class of noncanonical neural-network nonlinear systems with unknown input dead-zones. A complete solution framework is developed, using a new gradient-based design which is applicable to noncanonical nonlinear systems with input dead-zones. Signal boundedness of the closed-loop system and the desired tracking performance are ensured with the developed control schemes. Their effectiveness is illustrated by an application example of speed control of dc motors. This article can be readily extended to handle general parametrizable noncanonical nonlinear systems with unknown dynamics and input dead-zones, to solve such an open problem.