This paper investigates the issue of decentralized control for interconnected semi-Markovian systems with partially accessible transition rates (TRs). Firstly, a dynamic system model with a memory event-triggered mechanism (METM) is designed, which can effectively improve the fault tolerance of the event-triggering mechanism by employing the historical trigger data. Then a state feedback control model with dynamic METM is constructed, in which the semi-Markovian parameters with completely unknown and partially known transition probabilities are considered. Some sufficient conditions that insure the stochastic stability of the interconnected semi-Markovian systems can be obtained by utilizing the Lyapunov function and suitable model transformations method. Meanwhile, the parameters and the controller gain matrices of dynamic METM are also solved simultaneously by applying the linear matrix inequalities (LMIs). Finally, a simulation example is given to verify the effectiveness of the proposed method.

The consensus tracking problem is investigated for a class of multi-agent systems (MASs) under communication constraints. In particular, as a result of the impact of amplitude attenuation and random interference, communication among followers may inevitably suffer from the fading phenomenon. Meanwhile, the controllers may also be subject to malicious deception attacks, which will disrupt the correct operation of the MASs. Thus, the agents can only update their states based on fading information exchanged with their neighbors and the false control input under attacks. The consensus tracking error variables are first designed via the fading signal received from neighbors. Then, an online estimation strategy is introduced to estimate the unknown attacks, based on which the adaptive sliding mode controller is designed to attenuate the effect of the time-varying attacks on MASs. Convergence analysis of the MASs under the designed control strategy is provided by using the Lyapunov stability theory and adaptive sliding mode control method. Finally, the effectiveness of the theoretical results is verified via numerical simulations.

As under-constrained systems, four-wheel-independent-drive (4WID) electric vehicles have more driving degrees of freedom. In this context, reasonable control and distribution of driving or braking torque to each wheel is extremely important from the vehicle safety perspective. However, it is difficult to provide the optimal wheel torque because of the time-varying characteristics and typical over-actuated nature of the system. In light of these challenges, a novel hierarchical control scheme comprising a top- and bottom-level controller is proposed herein. First, for the top-level controller, a time-varying model-predictive-control (TV-MPC) controller is designed based on an extended 3-degree-of-freedom (3-DOF) reference vehicle model. The total driving force and additional yaw moment can be obtained using the TV-MPC. Second, for the bottom-level controller, the torque expression of each wheel is determined using the equal-adhesion-rate-rule -based algorithm. The co-simulation results obtained herein indicate that the proposed control scheme can effectively improve vehicle safety.

We propose reinforcement learning (RL) architectures for producing performant Takagi-Sugeno-Kang (TSK) fuzzy systems. The first employs an actor-critic algorithm to optimize existing TSK systems. An evaluation of this approach with respect to the Explainable Fuzzy Challenge (XFC) 2022 is given. A second proposed system applies Deep Q-Learning Network (DQN) principles to the Adaptive Network-based Fuzzy Inference System (ANFIS). This approach is evaluated in the CartPole environment and demonstrates comparability to the performance of traditional DQN. In both applications, TSK systems optimized via RL performed well in testing. Moreover, the given discussion and experimental results highlight the value of exploring the intersection of RL and fuzzy logic in producing explainable systems.

This paper presents a fixed-time integral sliding mode control scheme for a nonholonomic wheeled mobile robot (WMR). To achieve the trajectory tracking mission, the dynamic model of a WMR is first transformed into a second-order attitude subsystem and a third-order position subsystem. Two novel continuous fixed-time disturbance observers are proposed to estimate the external disturbances of the two subsystems, respectively. Then, trajectory tracking controllers are designed for two subsystems by utilizing the reconstructed information obtained from the disturbance observers. Additionally, an auxiliary variable that incorporates the Gaussian error function is introduced to address the chattering problem of the control system. Finally, the proposed control scheme is validated by a wheeled mobile robotic experimental platform.