In this paper, the problem of steering control is investigated for vehicle path tracking in presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the change of passenger number or payload, while the vehicle velocity also changes during normal cruising, which significantly influence the vehicle dynamics. Moreover, the vehicle dynamics is strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T-S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under fuzzy Lyapunov function framework. By employing some matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed $\mathcal{H}_{\infty}$ level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of T-S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.

In this article, a nonsingular fast terminal sliding mode (NFTSM) control strategy is proposed based on the high-order super-twisting observer (HOSTO) to acquire a fast and precise tracking performance for position regulation of permanent magnet linear synchronous motor. First, the NFTSM control is introduced to achieve fast convergence of the position tracking error. However, the lumped disturbances result in incompleteness of the system state information, and the robustness of the system is guaranteed through a large switching gain for the NFTSM, which causes system chattering. Therefore, the HOSTO is utilized to estimate the state information. Then, the NFTSM controller is designed based on the estimated state variables. The system’s stability under the proposed control is demonstrated using Lyapunov stability theory. Finally, real-time experiments are carried out to illustrate the efficiency and superiority of the proposed control strategy.

In this paper, a terminal sliding mode control strategy based on the nonlinear disturbance observer and prescribed performance method is proposed to deal with the speed tracking problem for permanent magnet linear synchronous traction systems in urban rail transit. First, to improve the dynamic and static performance, a prescribed performance control characterizing the tracking performance employed for error transformation. This limits the tracking error of the original system to a prescribed bound if the transformed system is stable. Then, a terminal sliding mode control is designed with backstepping, which guarantees the states converge to equilibrium point in finite time, realizing fast transient response of the system. Considering the existence of the uncertain disturbance, a nonlinear disturbance observer is utilized to estimate perturbations, and then the estimation is used for the feedforward compensation, further improving system stability. Finally, the effectiveness and advantages of the proposed scheme are illustrated by computer simulations and experiments in dSPACE.

This article proposes a novel state of charge (SoC) balancing control strategy based on multi-agent control between distributed the battery energy storage systems (BESSs) in super-UPS. The proposed control strategy has plug and play capability. Batteries with different capacities are considered in the control system. The battery capacity degradation under long term operation is further taken into account. In addition, a model-free iPI control strategy is designed for primary current control to achieve the tracking response of BESS to the reference current. Firstly, the distributed voltage and per-unit current dynamic consensus algorithm is introduced to maintain the DC bus voltage and achieve the battery current sharing. Then, a SoC balancing algorithm based on multi-agent control strategy is proposed. Furthermore, an adaptive capacity estimation algorithm is proposed considering the battery capacity degradation. The efficiency of the proposed control strategy is demonstrated in MATLAB/Simulink.

A two-layer stochastic model predictive control framework is proposed in this paper to address uncertainties associated with electric vehicles (EVs) in microgrid energy management. At the upper layer, a multi-scenario model predictive control (MS-MPC) approach is considered, which utilizes multi-scenario sampling to address the inherent uncertainties with respect to the number of available EVs and their states of charge. The proposed approach effectively minimizes prediction errors while ensuring the cost-efficient operation of the energy management system. To mitigate concerns related to increased convergence time due to scenario combinations, a two-stage scenario reduction technique is introduced to improve the computational efficiency. At the lower layer, an EV aggregator allocation model is developed that considers the power requirements of each individual EV for both charging and discharging operations. Simulation results demonstrate the effectiveness of the proposed MS-MPC approach, revealing its high cost-effectiveness performance and affordable computational burden.

The bridge crane system is a typical under‐actuated system that is widely used in production and life. Although various scholars have conducted extensive research on the bridge crane system in recent years, there are still many problems, such as the trajectory planning of the cart and the anti‐sway control of the cargo. In order to tackle the problem of the anti‐sway control of the cargo, a hierarchical global fast terminal sliding‐mode control (H‐GFTSMC) is developed in this work. First, the Lagrange equations are used to model the system dynamics. Then, an appropriate hierarchical global fast terminal sliding‐mode controller is designed to achieve anti‐sway control of the cargo, and it is proved that each sliding‐mode surface is progressively stable. A series of simulations were implemented to verify the effectiveness of the control method. The simulation results show that the H‐GFTSMC has better control performance compared with the proportional–integral–derivative control method. When changing the cable length or adding non‐negligible noise to the system, the H‐GFTSMC still has good robustness.