The neural-approximation-based adaptive nonlinear containment control issue for multiagent systems with full-state constraints is studied by invoking the backstepping approach. First, the barrier Lyapunov functions are established to deal with the state constraining issue in the multiple leaders/followers control scenarios. Then, by introducing the first-order filter, the system communication burden is substantially reduced. Moreover, the event-triggered controller is constructed by utilizing the switching-based mechanism so that the system security, control accuracy, resource consumption, and imposed state constraints are neatly balanced. We prove the output of each follower can converge to the desired hull formulated by leaders under the premise that the imposed state constraints are never violated. Besides, the considered closed-loop signals are uniformly bounded. We finally present a simulation example to show the validity of the developed approach.

Based on radial basis function neural networks (RBF NNs) and backstepping techniques, this brief considers the consensus tracking problem for nonlinear semi-strict-feedback multiagent systems with unknown states and disturbances. The adaptive event-triggered control scheme is introduced to decrease the update times of the controller so as to save the limited communication resources. To detect the unknown state, external disturbance, and reduce calculation workload, the state observer and disturbance observer as well as the first-order filter are first jointly constructed. It is shown that all the output signals of followers can uniformly track the reference signal of the leader and all the error signals are uniformly bounded. A simulation example is carried out to further prove the effectiveness of the proposed control scheme.

This brief considers the adaptive tracking problem for a class of uncertain nonlinear systems. First, the disturbance observer (DO) is established, and the neural network (NN) technique is adopted to deal with unmeasured disturbances and system dynamic uncertainties. Then, the switching threshold event-triggered mechanism is utilized, and the adaptive tracking controller is further designed based on backstepping techniques. We show that the tracking errors simultaneously converge to an adjustable compact set of origin, and all the closed-loop signals are semi-globally uniformly bounded. Finally, the practicability and effectiveness of the proposed control approach are well illustrated by a simulation case.

In this brief, an one-to-one asymmetric NM based auxiliary system is put forward to deal with the state transformation problem for nonstrict full state-constrained systems under the adaptive fixed-time control. Then one proposed a novel switched-self-triggered mechanism (SSTM) in the controller update, which is helpful to obtain a better system performance. In addition, one can show that under the condition of state-constraints, semiglobal practical fixed-time stability (SPFTS) standard can also preserved both the tracking performance and the stability of the closed-loop system in a specified time. Finally, the effectiveness of the controller design is thoroughly shown via an illustrative simulation example.

This article focuses on the adaptive control issue for uncertain nonlinear systems with time-varying full-state constraints. First, a novel integral barrier Lyapunov functions (IBLFs)-based neural backstepping control approach is designed, which circumvents the trouble of conversion in the traditional used BLFs. And then, the sliding-mode disturbance observers (SMDOs) are established to deal with the immeasurable disturbances in each order of the state-constrained uncertain nonlinear systems. Besides, the dynamic threshold-based event-sampling mechanism is constructed to deal with the sparsity of resources and system controlling burden. Finally, according to the given design approach, an event-triggered adaptive controller is developed and ensures disturbance observation errors uniformly converge to the origin in finite time, and all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. A developed numerical simulation case verifies the validity of the proposed approach.

The challenge of the exploration–exploitation dilemma persists in off-policy reinforcement learning (RL) algorithms, impeding the improvement of policy performance and sample efficiency. To tackle this challenge, a novel historical decision-making regularized maximum entropy (HDMRME) RL algorithm is developed to strike the balance between exploration and exploitation. Built upon the maximum entropy RL framework, the historical decision-making regularization method is proposed to enhance the exploitation capability of RL policies. The theoretical analysis involves proving the convergence of HDMRME, investigating the tradeoff between exploration and exploitation of HDMRME, examining the disparity between the Q-function learned through HDMRME and the classic one, and analyzing the suboptimality of the trained policy. The performance of HDMRME is evaluated across various continuous-action control tasks from Mujoco and OpenAI Gym platforms. Comparative experiments demonstrate that HDMRME exhibits superior sample efficiency and achieves more competitive performance compared with other state-of-the-art RL algorithms.