This article proposes a neural-network-based adaptive asynchronous event-triggered design strategy for the distributed consensus tracking of uncertain lower triangular nonlinear multi-agent systems under a directed network. Compared with the existing event-triggered recursive consensus tracking designs using multiple neural networks for each follower and continuous communications among followers, the primary contribution of this study is the development of an asynchronous event-triggered consensus tracking methodology based on a single-neural network for each follower under event-driven intermittent communications among followers. To this end, a distributed event-triggered estimator using neighbors’ triggered output information is developed to estimate a leader signal. Subsequently, the estimated leader signal is used to design local trackers. Only a triggering law and a single-neural network are used to design the local tracking law of each follower, irrespective of unmatched unknown nonlinearities. The information of each follower and its neighbors is asynchronously and intermittently communicated through a directed network. Thus, the proposed asynchronous event-triggered tracking scheme can save communicational and computational resources. From the Lyapunov stability theorem, the stability of the entire closed-loop system is analyzed and the comparative simulation results demonstrate the effectiveness of the proposed control strategy.

This study investigates a quantized feedback design problem for distributed adaptive leader-following consensus of uncertain strict-feedback nonlinear multiagent systems with state quantizers. It is assumed that all system nonlinearities of followers are unknown and heterogeneous, all state variables of each follower are quantized by a uniform state quantizer, and quantized states of followers are only communicated under a directed network. Compared with previous approximation-based distributed consensus tracking methods for uncertain lower triangular multiagent systems, the main contribution of this article is addressing the distributed quantized state communication problem in the adaptive leader-following consensus tracking field of uncertain lower triangular multiagent systems. A quantized-states-based local adaptive control law for each follower is derived by designing quantized-signals-based weight tuning laws for neural-network-based function approximators. By analyzing the boundedness of the local quantization errors, it is shown that the total closed-loop signals are uniformly ultimately bounded and the consensus tracking errors converge to a sufficiently small domain around the origin. Finally, simulation examples, including multiple ship steering systems, are considered to verify the effectiveness of the proposed theoretical approach.

This paper investigates the oxygen starvation and parasitic loss prevention problem of the adaptive oxygen excess ratio (OER) control system of nonlinear proton exchange membrane fuel cells (PEMFCs). Asymmetric OER constraints are considered to avoid oxygen starvation and parasitic loss in the air supply system of PEMFCs. An approximation-based adaptive control strategy is established to ensure robust regulation of the OER while not violating the OER constraints, regardless of unknown system parameters, nonlinearities, and the abrupt changes of the load current. A dynamic surface design technique using an asymmetric barrier Lyapunov function is employed for a recursive control design. Compared with existing control approaches for uncertain nonlinear air supply systems of PEMFCs, this paper first considers the oxygen starvation and parasitic loss prevention problem for the regulation of optimal OER in the control field of nonlinear PEMFCs. Using the Lyapunov stability theorem, the boundedness of all closed-loop signals and the convergence of the output tracking error to the vicinity of zero are proved.

This letter presents a distance-based formation control with the goal assignment for multiple nonholonomic mobile robots. One of the most challenging issues in the distance-based formation control is the convergence to undesirable formations caused by the gradient-based control law. Even though the target formation is globally rigid, only the local stability is guaranteed. In order to overcome this restriction, we present a novel goal assignment algorithm switching the goal positions of two agents in the target formation. This assignment algorithm helps to avoid the convergence to undesirable formations and thus the global asymptotic convergence can be achieved. To show the effectiveness of the proposed distance-based formation controller with the goal assignment algorithm, simulation and experimental results are provided.

This paper presents a new approximation-based control approach for uncertain nonlinear pure-feedback systems. The main idea of this paper is to estimate unknown continuous nonlinear functions through a linear combination of first-order filtered signals of state variables and a control input in the nonadaptive control framework, instead of using conventional adaptive neural or fuzzy function approximators. Based on the proposed filter-driven approximation technique, we first present a state-feedback control scheme for pure-feedback systems with unknown nonaffine nonlinearities and a dead-zone input. Then, a filter-driven-approximation-based output-feedback control scheme is proposed via a system transformation and an observer to estimate unmeasurable state variables. Based on the Lyapunov stability theorem, the control errors and the filter-driven approximation errors are considered to prove that the controlled closed-loop system is semi-globally uniformly ultimately bounded. Finally, simulation results are provided to show that the proposed filter-driven-approximation-based controller and the existing function-approximation-based adaptive controllers have similar control performance for nonlinear pure-feedback systems.

An adaptive output‐feedback control approach is proposed for uncertain non‐linear systems with unknown time delays in strict‐feedback form. A linear observer is designed to estimate unmeasurable state variables. In contrast to the existing controllers for strict‐feedback systems, the adaptive control design using only single fuzzy approximation (SFA) is presented. In the controller design procedure, unknown non‐linear functions including unknown non‐linearities caused by compensating time‐delay functions are derived to design virtual controllers. They are not approximated by using fuzzy logic systems at each step of the backstepping design, but just passed down to the next step. Finally, a lumped unknown non‐linear function including non‐linearities of observer error dynamics is defined at the last step and estimated by SFA in order to design the actual control law. Thus, the actual control law using SFA can be implemented, without calculating virtual control laws. Compared with existing output‐feedback control approaches for strict‐feedback time‐delay systems, the proposed control system has a relatively simple structure in that it overcomes the problem of using multiple approximators as well as the problem of calculating the repeated differentiations of virtual controllers. In addition, we show that all signals in the closed‐loop system are uniformly ultimately bounded and the tracking error converges to an adjustable neighbourhood of the origin.