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Over the past several decades, the automobile industry has devoted much significant research efforts to developing autonomous ground vehicles (AGVs). One of the most fundamental issues for AGVs is path following, which is concerned with the control strategy for AGVs to follow the scheduled paths. This paper presents a new path following control approach, where an output-feedback triple-step controller is designed to realize coordinated lateral and longitudinal control without a measurement of lateral velocity. The main contribution of this paper is the integrated design of the observer and control gains in the framework of Lyapunov stability and input-to-state stability (ISS) theory. The effectiveness of the proposed control system has been evaluated through simulations. [ABSTRACT FROM AUTHOR]

This study proposes a novel integrated local trajectory planning and tracking control (ILTPTC) framework for autonomous vehicles driving along a reference path with obstacles avoidance. For this ILTPTC framework, an efficient state-space sampling-based trajectory planning scheme is employed to smoothly follow the reference path. A model-based predictive path generation algorithm is applied to produce a set of smooth and kinematically-feasible paths connecting the initial state with the sampling terminal states. A velocity control law is then designed to assign a speed value at each of the points along the generated paths. An objective function considering both safety and comfort performance is carefully formulated for assessing the generated trajectories and selecting the optimal one. For accurately tracking the optimal trajectory while overcoming external disturbances and model uncertainties, a combined feedforward and feedback controller is developed. Both simulation analyses and vehicle testing are performed to verify the effectiveness of the proposed ILTPTC framework, and future research is also briefly discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller. [ABSTRACT FROM AUTHOR]

This paper presents a robust H ∞ output-feedback control strategy for the path following of autonomous ground vehicles (AGVs). Considering the vehicle lateral velocity is usually hard to measure with low cost sensor, a robust H ∞ static output-feedback controller based on the mixed genetic algorithms (GA)/linear matrix inequality (LMI) approach is proposed to realize the path following without the information of the lateral velocity. The proposed controller is robust to the parametric uncertainties and external disturbances, with the parameters including the tire cornering stiffness, vehicle longitudinal velocity, yaw rate and road curvature. Simulation results based on CarSim–Simulink joint platform using a high-fidelity and full-car model have verified the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

• A cascaded ACF scheme is proposed to accurately estimate longitudinal and lateral velocities by considering tire force coupling. • Experiments on a scaled FWIA in-wheel-motor-driven EAV have been conducted to verify the effectiveness of the proposed ACF approach. • The ACF estimator-based path planning and following control with MPC strategies are originally designed for PIC and for FWIA EAVs. This paper investigates a post-impact control (PIC) method for four-wheel independently actuated (FWIA) electric autonomous vehicles (EAVs) after an initial impact. Differential steering angles actuated by differential torques generated from the left and right wheels have been utilized as an inherent redundant control strategy when the steering motor totally fails, to realize the PIC and secondary collision mitigation. To this end, a vehicle state estimation-based PIC strategy is developed in this work, and three contributions have been made as follows: 1) A novel cascaded estimation approach using the adaptive complementary filters (ACF) is proposed to estimate the longitudinal and lateral velocities with low-cost measurement; 2) Experiments on a scaled FWIA EAV with differential steering mechanism have been conducted to verify the proposed ACF approach, indicating that ACF can accurately estimate the longitudinal and lateral velocities; 3) The vehicle velocity estimation-based path planning and following with model predictive control (MPC) strategy are proposed for PIC and for FWIA EAVs, both for the first time. Finally, the verifiable simulation based on the high-fidelity CarSim-Simulink conjoint platform with the experimentally identified vehicle parameters has been conducted, which has verified the proposed ACF-based PIC strategy can effectively avoid the secondary collision and guarantee vehicle stability and control performance. [ABSTRACT FROM AUTHOR]