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This article focuses on solving the optimal control problem for magnetorheological fluid (MRF)-based semiactive suspension (SAS) systems with input saturation and time-varying delay. A robust switched H$\infty$ method is proposed in this work based on the Takagi–Sugeno (T–S) fuzzy theory. A novel hybrid model that incorporates both the fluid flow mechanism (FFM) and hysteresis phenomenon model (HPM) is adopted to separate the passive and active components of the MRF damper. Under such a framework, the convex and reciprocally convex approaches are used to transform the features of input saturation and time-varying delay into linear matrix inequality conditions. Furthermore, a Lyapunov–Krasovskii function is employed to guarantee the stability of the MRF–SAS system subject to input saturation and time-varying delay. Finally, the effectiveness of the proposed method is validated via a numerical example of an MRF–SAS system. The results show that the proposed controller indicates good dynamic performance for the MRF–SAS system with input saturation and time-varying delay.

This article investigates the issue of fixed-time path-following control for autonomous ground vehicles (AGVs) with completely unknown parameters subject to preset error constraints. Before converging the path-following errors, a fixed-time prescribed performance control (PPC) protocol is developed to guarantee the selected nonsingular sliding manifold evolving within the preset boundaries. Then, the fixed-time PPC target for the preview error of the AGV can be achieved by satisfying the parameter inequality. Furthermore, the designed adaptive laws can ensure the control accuracy without knowledge of vehicle parameters. Finally, the hardware-in-the-loop tests of multiple maneuvering conditions demonstrate that the proposed fixed-time PPC protocol can provide superior comprehensive path-following performance for the AGV over the conventional fixed-time controller, and the preview error can never violate the prescribed performance constraints.