Path tracking control of 4-wheel-steering autonomous ground vehicles based on linear parameter-varying system with experimental verification

In this study, a novel 4-wheel-steering electric vehicle is proposed as an autonomous ground vehicle. It aims to study the path tracking control algorithm of the 4-wheel-steering autonomous ground vehicle for intelligent driving.. Path tracking model is built for path tracking controller design based on a single track model. Besides, the linear parameter-varying system model is constructed to make the path tracking controller adaptive to different longitudinal velocities and road friction coefficients. Furthermore, a linear quadratic regulator controller for path tracking is designed and stability analysis is carried out. To eliminate the error caused by disturbance, feedforward control is combined with a linear quadratic regulator controller. To verify the path tracking performance of the designed controller, numerical simulations are carried out based on a high-fidelity and full-vehicle model constructed in CarSim. Moreover, real road experiments are performed. Both the simulation results and experiment results show that the designed controller has good path tracking performance. In addition, the path tracking controller shows good robustness to deal with different longitudinal velocities and road friction coefficients.

Derivative-Based Sampled Data Control for Continuous Linear Parameter Varying System With Unknown Parameters

This paper deals with the robust stabilization of a class of linear parameter varying (LPV) systems in the sampled data control case. Instead of using a state observer or searching for a dynamic output feedback, the considered controller is based on output derivatives estimation. This allows the stabilization of the plant with very large parameter variations or uncertainties. The proof of stability is based on the polytopic representation of the closed-loop under Lyapunov conditions and system transformations. The result is a control structure with only one parameter tuned via very simple conditions. Finally, the effectiveness of the proposed method is verified via a numerical example of a second-order LPV system.

On the fractional closed-loop linear parameter varying system identification under noise corrupted scheduling and output signal measurements

It is well known that, in some industrial process identification situations, measurements can be collected from closed-loop experiments for several reasons such as stability, safety, and performance constraints. In this paper, we investigate the problem of identifying continuous-time fractional closed-loop linear parameter varying systems. The simplified refined instrumental variable method is developed to estimate both coefficients and differentiation orders. This method is established to provide consistent estimates when the output and the scheduling variable are contaminated by additive measurements noise. The proposed scheme is evaluated in comparison with other approaches in terms of a simulation example.