This article proposes an observer-based reinforcement learning (RL) control approach to address the optimal attitude-tracking problem and application for hypersonic vehicles in the reentry phase. Due to the unknown uncertainty and nonlinearity caused by parameter perturbation and external disturbance, accurate model information of hypersonic vehicles in the reentry phase is generally unavailable. For this reason, a novel synchronous estimation is proposed to construct a composite observer for hypersonic vehicles, which consists of a neural-network (NN)-based Luenberger-type observer and a synchronous disturbance observer. This solves the identification problem of nonlinear dynamics in the reference control and realizes the estimation of the system state when unknown nonlinear dynamics and unknown disturbance exist at the same time. By synthesizing the information from the composite observer, an RL tracking controller is developed to solve the optimal attitude-tracking control problem. To improve the convergence performance of critic network weights, concurrent learning is employed to replace the traditional persistent excitation condition with a historical experience replay manner. In addition, this article proves that the weight estimation error is bounded when the learning rate satisfies the given sufficient condition. Finally, the numerical simulation demonstrates the effectiveness and superiority of the proposed approaches to attitude-tracking control systems for hypersonic vehicles.

This paper proposes a multiple saturation levels framework to investigate the semi-global tracking cooperative control problem of multi-agent systems with switching communication topologies. This problem is solving by proposing a switching parametric Lyapunov approach and constructing a three-layer nodes model with mirroring nodes to reduce the complexity of controller design in which control inputs for different layers of nodes are with different saturation levels. A metamorphic low-gain feedback consensus protocol is developed, based only on the interaction information of different nodes, to ensure that the states of the followers approach that of the leader, if the dwell time can be reasonably selected and the switching derived topologies have a directed spanning tree. Moreover, the protocol is extended to the derived topology that frequently contains a directed spanning tree. Simulations are presented to verify the effectiveness of the new design techniques.

This study focuses on the global asymptotic stability and observer‐based stabilisation problems of a class of fractional order non‐linear systems with the fractional order 0 < α < 2. Based on the decay properties of the Mittag–Leffler function and the Gronwall–Bellman inequality, two sufficient conditions on the global asymptotic stability of fractional order non‐linear systems are derived for the fractional order 0 < α < 1 and 1 < α < 2, respectively. According to the proposed conditions, an iterative algorithm is presented to design the fractional order non‐linear observer and estimated state feedback control law. Finally, two numerical examples illustrate the advantages of the proposed iterative algorithm.

This paper proposes an accurate cooperative control strategy to address the distributed adaptive consensus problem for multiple subsystems of the process industrial plants by constructing a two-layer node-to-node communication framework. In the present framework, each subsystem is modeled by an agent, and all the subsystems and the information flow are regarded as a multi-agent uncertain system. By introducing proper assumptions, a class of distributed adaptive consensus protocol for accurate cooperative control is designed by adaptive weighting factors, appropriate feedback gains, and limited state information. It shows that distributed adaptive consensus of accurate cooperative control can be achieved for closed-loop multi-agent uncertain systems with the two-layer node-to-node communication framework, if each follower is affected by at least one leader for some uniformly bounded communication time intervals. The results are further extended to nonlinear situations. Two application examples are presented to verify the effectiveness of the proposed approaches.