This study investigates the robust non-fragile controller design for vehicle lateral stability, with considering the uncertainties of tire cornering stiffness and actuator saturation. The vehicle longitudinal velocity is assumed to be time varying and the velocity-dependent parameters are described within a trapezoid polytope. The norm-bounded perturbation of controller gain is taken into account which is more practical. By solving the linear matrix inequalities, the non-fragile linear parameter-varying controller is obtained to satisfy the mixed and performance of the vehicle dynamics system. With the designed controller, the sideslip angle and yaw rate of vehicle are restricted effectively to guarantee the lateral stability and handling performance. Two simulations are carried out to validate the effectiveness of the designed controller.

This study investigates the issue of dissipative synchronisation for inertial neural networks with Markovian jumping parameters and reaction–diffusion terms via the event-based control strategy. First, for study convenience, by employing a suitable variable substitution, the original synchronisation error system is transformed to a first-order differential one, and then it is reformed as a compact form. Second, an innovative event-triggered communication scheme that consists of the system's state and its first-order derivatives is proposed, with which the current sampling signal is judged whether should be released. Third, by constructing a novel Lyapunov–Krasovskii functional, and integrating the reciprocally convex combination method, free-weighting matrix approach and Wirtinger-based integral inequality, the dissipative synchronisation criterion that has less conservatism is derived. Finally, two examples are provided, one is a simple numerical example to account for the effectiveness of the main results, the other realises the application of this study in establishing a spatiotemporal chaotic cryptosystem to transmit the encrypted image. Furthermore, the proposed cryptosystem has been illustrated to have obvious advantages of large key space and high security by simulation results.

This study investigates an integrated wheel slip, yaw rate, and sideslip angle control via torque vectoring to improve both the longitudinal and lateral stability of electric vehicles (EVs) with four in-wheel motors. The algorithm is developed based on model predictive control (MPC) and thus can optimally reach a balance among different objectives while considering actuation and state constraints. Firstly, to deal with tyre non-linearity and variations in the lateral tyre forces due to changes in tyre slip ratios, the mechanism of using torque vectoring to improve vehicle stability is analysed. Then, a non-linear tyre model is introduced into the predictive model to characterise the tyre force coupling relationship. Here, a linear-parameter-varying (LPV) model is employed, which is derived by linearising the nonlinear vehicle model online. Moreover, the stability control of EVs with in-wheel motors is transformed into a constrained online optimisation problem and solved using the proposed LPV-MPC method. Finally, the proposed LPV-MPC is compared with some existing well-established techniques from literature in different test scenarios. The obtained results demonstrate that the LPV-MPC approach could reduce the computational burden and shows a precise longitudinal control and obviously improves the lateral stability.

Shared control is a promising approach that can facilitate the mutual understanding and cooperative control between human and machine. In this study, a novel reference-free framework for shared control of automated vehicles is proposed with the aim of mitigating conflicts between the human driver and the automatic system during their interactions. Position constraint and time to collision are deployed to prevent collision and guarantee stability by limiting the yaw rate and sideslip angle of the vehicle. The design of the shared controller is formulated as a model predictive control problem. It determines vehicle state based on the current driver command and implements control actions only if the vehicle state induced by the human driver violates the pre-defined constraints. The system models, safety constraints and the proposed shared controller are then integrated. The algorithm is validated through computer simulation under two different driving scenarios. The simulation results show that the proposed reference-free approach can offer the human driver more degrees of freedom during shared control, effectively mitigating human–machine conflicts, compared to the previous work. In addition, the algorithm ensures the safety and stability of the system under risky driving conditions, validating its feasibility and effectiveness.