The control of an automotive suspension system using hydraulic actuators is a highly complex nonlinear control task dealing with system nonlinearities, external disturbances, and uncertainties. In this work, an output feedback active suspension control scheme is proposed to achieve a ride comfort while maintaining the road holding for the vehicle. To design the controller, the states of the nonlinear system are first estimated using a highgain observer where the suspension stroke is the only measurable output. The controller is then designed using a recursive derivative nonsingular higher order terminal sliding mode approach that avoids singularity. The practical stability for the closed-loop observer-controller pair is established. Simulation results for the quarter-wheel vehicle over various road conditions demonstrate the effectiveness of the proposed control in improving the suspension performance in both the time and frequency domains.

This paper is concerned with a new control method for path tracking of autonomous ground vehicles. We exploit the fuzzy model-based control framework to deal with the time-varying feature of the vehicle speed and the highly uncertain behaviors of the tire-road forces involved in the nonlinear vehicle dynamics. To avoid using costly vehicle sensors for feedback control while favoring the simplest control structure for real-time implementation, a new fuzzy static output feedback (SOF) scheme is proposed. In particular, though the robust set-invariance property and Lyapunov-based arguments, the physical constraints on the steering input saturation and the vehicle state can be taken into account in the control design to improve the driving safety and comfort. The theoretical development relies on the use of fuzzy Lyapunov functions and the non-parallel distributed compensation control concept to reduce the design conservatism. Exploiting some specific convexification techniques, the control design is reformulated as an optimization problem under linear matrix inequalities with a single line search, which are efficiently solved via semidefinite programming techniques. The proposed fuzzy path tracking controller is evaluated through various dynamic driving tests conducted with high-fidelity CarSim/Matlab co-simulations. Moreover, to emphasize the advantages of the new fuzzy SOF controller, a performance comparison with the CarSim driver model is also performed.

The nonlinear active suspension control design can be analyzed as a multi-objective control problem that caters to the enhancement in ride comfort levels while ensuring that road holding is maintained with the constrained suspension displacement movement. In this paper, a robust constrained output feedback approach employing sliding mode controllers is proposed for the nonlinear active suspension system equipped with hydraulic actuators. Adhering to mechanical design limitations, the suspension displacement and the corresponding displacement rate are constrained using asymmetric barrier Lyapunov functions. Consequently, a nonlinear control law that incorporates the first order sliding mode control is then formulated to regulate the hydraulic valve and thereby provide an active control effort. The robust control performance with high-performance improvements for ride comfort in the presence of parametric uncertainties, sensor noise, and over variable driving velocities for different road conditions is established using simulation studies.

The longitudinal motion control of automotive vehicles is heavily reliant on information about the time-varying tire road friction coefficient. In the presence of varying road roughness profiles, the effective vertical load on each wheel varies dynamically, influencing the tire friction. In this paper, we integrated the vertical and longitudinal dynamics of a quarter wheel to form an integrated nonlinear model. In the modeled dynamics, the time-varying random road profile and the tire friction are treated as unknown inputs. To estimate these unknown inputs and states simultaneously, a combination of nonlinear Lipschitz observer and modified super-twisting algorithm (STA) observer is developed. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is established. Simulation results performed with the high-fidelity vehicle simulation software CarSim demonstrate the effectiveness of the proposed scheme in the estimation of states and unknown inputs.

The estimation of friction coefficient for a vehicle when it traverses on different surfaces has been an important issue. In this work, the longitudinal vehicle dynamics, the torsional tyre dynamics and the non‐linear LuGre friction dynamics are integrated to model the quarter vehicle dynamics. The road adhesion coefficient in the vehicle dynamics is unknown and varies with the contact surface. To address this issue, the authors consider a class of non‐linear uncertain systems that covers the vehicle dynamics and develop a higher‐order sliding mode observer based on supertwisting algorithm for state and unknown input estimations. Under Lipschitz conditions for the non‐linear functions, the convergence of the estimation error is established. By estimating the road adhesion coefficient, the coefficient of friction can be estimated. Simulation results demonstrate the effectiveness of the proposed observer for state and unknown input estimation.