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This paper proposes a control strategy for the design of an automated steering control for an autonomous electric vehicle. The proposed Active Disturbance Rejection Control (ADRC) with Model Predective Control (MPC) is not only capable of alleviating the disturbance but also shows robustness against structured uncertainties which may arise due to models that represent the vehicle dynamics. Simulations have been carried out to assess the effectiveness of the proposed control strategy. Simulation results show that the proposed scheme is better in terms of tracking performance than MPC and ADRC. The steering control system, with the proposed strategy, can achieve faster response, higher tracking accuracy, and improved robustness performance in dealing with model uncertainties and external disturbances.

This paper presents an adaptive sliding mode-based active disturbance rejection control (ASM-ADRC) strategy for the altitude and attitude control of an unmanned quadcopter with disturbances. The quadcopter is an underactuated system subject to parametric perturbations, nonlinearity, unmodeled dynamics, strong coupling, and external disturbances. The proposed algorithm is based on the quadcopter’s dynamic model, where the effects of noise and wind are considered additive disturbances. The central concept is to combine the advantages of adaptive sliding mode control (SMC) to accurately track the reference trajectory with the ability of ADRC to reject the parameter uncertainties and external disturbances. The proposed control scheme is verified in simulation studies with sensor noise and external disturbances. The simulation results show that the proposed algorithm can significantly reduce the chattering phenomenon, owing to the estimation capability of the extended state observer (ESO). The proposed method also improves the robustness against modeling errors and disturbances and smoothly tracks the reference trajectory.

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