Off-road vehicles always experience serious uncertain longitudinal and lateral slips when running on soft and slope terrains, and the parameters of the vehicle, such as the cornering stiffness and the slip of wheel, are always not constants. In this paper, the control strategies for the torque of each wheel and the steering angle of rear wheels are proposed to maintain a stable velocity and approach an ideal reference model for the off-road vehicle by using second-order sliding mode (SOSM) techniques. An observer is constructed to estimate the actual sideslip angle of the vehicle with the consideration of the uncertainties and disturbances of the system. Then, with the conditions of the bounded derivative of these uncertainties and disturbances, the composite super-twisting (ST) controllers combined with a velocity controller of SOSM are designed to generate the total longitudinal traction, differential traction of two sides, and steering angle of rear wheels. On this basis, the proposed controllers have been verified to lead good robustness for maintaining the stable velocity and approaching the ideal reference model. In comparison with the conventional yaw moment controllers without the rear wheel steering, the proposed controllers are shown to be more effective.

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.

As tire forces approach road friction limits, vehicles may always exhibit performance degradation and even instability. The actual capacity of the available road friction imposes coupled limits on a vehicle's longitudinal and lateral accelerations. In this paper, a varying speed method is proposed to design feasible speeds and accelerations, which ensure that the autonomous vehicle will not reach the tire-road friction limits during traversing a clothoid-based path. With the consideration of uncertain traction losses and vehicle parameters, a second-order super-twisting (ST) based speed control strategy is proposed to track above feasible speeds based on varying speed method, and to stabilize the sliding-mode variable of the speed tracking error with relative degree 1. Meanwhile, a second-order quasi-continuous (QC) based path-following control strategy is proposed to follow a desired transition path, and to stabilize the sliding-mode variable of the composite path-following errors with relative degree 2. On this basis, the proposed controllers have been verified to lead good robustness for tracking the ideal speeds and following the desired paths. As compared with the boundary of the autonomous vehicle running at a constant speed, the feasible speed boundary using varying speed method without exceeding the tire-road friction limits can be enlarged up to about 1.59 times, which is decided by the direction change between the entry and exit points of the desired path.

This paper proposes a prescribed performance function (PPF)-based constrained fractional-order model predictive controller (CFMPC) for the robust path following of the four-wheel-independent-drive autonomous ground vehicle (FWID-AGV) subject to the model mismatch and external disturbances. First, a PPF-based model predictive controller (MPC) is developed to guarantee the transient-state performance by dealing with the model mismatch. Second, a contraction constraint of the MPC is constructed with an adaptive neural network backstepping method, aiming to ensure the optimal control inputs fall into the region of attraction. Third, a fractional-order cost function of the MPC is discretized to reject the external disturbances and improve the robustness of the controller. Besides, a sufficient condition is given to prove the recursive feasibility of the PPF-based CFMPC, and a Lyapunov function candidate is derived for the asymptotic stability of the closed-loop path following system. The effectiveness and practicability of the proposed method are validated by hardware-in-the-loop tests, respectively

With the consideration of actuator faults, including the unknown steering mechanism misalignments and motor traction losses, this article presents a fixed-time control protocol to follow reference paths and velocities for autonomous ground vehicles (AGVs) with preset performance constraints. To provide sufficient large boundaries for the initial states, the hyperbolic tangent function is employed to predefine the constraints with respect to the path-following and velocity control performance. Based on the homeomorphic mapping and barrier Lyapunov theorem, the fixed-time prescribed performance control (PPC) objective-integrated fault-tolerant scheme can be achieved for the controlled AGV. In comparison to three different fixed-time controllers without the fault-tolerant or PPC scheme, the hardware-in-the-loop (HIL) test results demonstrate that the proposed control protocol can always provide superior control performance for the AGV under various maneuvering conditions.