In the design of a path-tracking controller for autonomous vehicles, mediating the conflicting objectives among path-tracking, vehicle stabilisation, and low computational cost is a challenging issue. Accordingly, this paper proposes a model predictive control (MPC)-based path-following controller with steering angle envelopes. In contrast to previous MPC-based structure, in which the constraints in terms of the road sides and lateral stabilisation directly impose on the algorithm, in the proposed approach, these aspects are formulated as the steering angle envelopes. This feature enables the controller to simultaneously ensure the tracking performance and computational feasibility. Moreover, considering the trade-off between path-tracking and vehicle stabilisation, the full-length prediction horizon pertaining to the steering angle envelopes are divided into two different parts: the safe road envelope is applied in the short-term, and the stable handling envelope is applied in other part. By modelling vehicles driving under different road conditions with various driving speeds and road adhesion values, the developed controller is compared with several existing control schemes via simulations and hardware-in-the-loop (HIL) experiments. The comparison results demonstrate the effectiveness of the proposed controller in realising path-tracking, lateral stabilisation, and real-time computational capabilities in the best possible manner.

This paper proposes a new safety system named emergency steering assist (ESA), which consists of the autonomous steering subsystem and differential braking subsystem. The control system is developed to mediate the conflict objectives of vehicle stabilisation and rear-end collision avoidance in highway driving. Instead of predefining a collision-free trajectory in the entire process of collision avoidance, the process of collision avoidance is divided into two stages. In stage 1, where the vehicle is guided to enter the adjacent lane with a constant centripetal acceleration to avoid collision, the steering manoeuvre is determined by the feedforward controller based on the steering dynamics. In stage 2, where the vehicle is guided along the centreline of the adjacent lane, the steering manoeuvre is determined by the controller based on the theory of model predictive control. In addition, the differential braking subsystem is designed with a comprehensive treatment to the requirements of collision avoidance and vehicle stabilisation. Finally, the simulation results demonstrate that the proposed ESA system can effectively achieve better balance between an emergency collision avoidance manoeuvre and vehicle stabilisation at high speeds in different conditions. The hardware-in-the-loop experiment developed by the authors is used to validate the real-time performance and effectiveness of the proposed control scheme.

A multi-objective active front steering (AFS) control system considering the road adhesion constraint on vehicle stability is developed using the sliding mode control (SMC) method. First, an identification function combined with the relationship between the yaw rate and the steering angle is developed to determine whether the tyre state is linear or nonlinear. On this basis, an intervention criterion for the AFS system is proposed to improve vehicle handling and stability in emergent conditions. A sideslip angle stability domain enveloped by the upper, lower, left, and right boundaries, as well as the constraint of road adhesion coefficient, is constructed based on the phase-plane method. A dynamic weighting coefficient to coordinate the control of yaw rate and sideslip angle, and a control strategy that considers changing control objectives based on the desired yaw rate, the desired sideslip angle, and their proportional weights, are proposed for the SMC controller. Because road adhesion has a significant effect on vehicle stability and to meet the control algorithm's requirement of real-time access to vehicle states, a unscented Kalman filter-based state observer is proposed to estimate the adhesion coefficient and the required states. Finally, simulations are performed using high and low road adhesion conditions in a Matlab/Simulink environment, and the results show that the proposed AFS control system promptly intervenes according to the intervention criterion, effectively improving vehicle handling and stability.

In order to meet the demand of the intelligent and efficient picking of fresh citrus fruit in a natural environment, a flexible and independent picking method of fresh citrus fruit based on picking pattern recognition was proposed. The convolutional attention (CA) mechanism was added in the YOLOv7 network model. This makes the model pay more attention to the citrus fruit region, reduces the interference of some redundant information in the background and feature maps, effectively improves the recognition accuracy of the YOLOv7 network model, and reduces the detection error of the hand region. According to the physical parameters of the citrus fruit and stem, an end-effector suitable for picking citrus fruit was designed, which effectively reduced the damage during the picking of citrus fruit. According to the actual distribution of citrus fruits in the natural environment, a citrus fruit-picking task planning model was established, so that the adaptability of the flexible handle can make up for the inaccuracy of the deep learning method to a certain extent when the end-effector picks fruits independently. Finally, on the basis of integrating the key components of the picking robot, a production test was carried out in a standard citrus orchard. The experimental results show that the success rate of the citrus-picking robot arm is 87.15%, and the success rate of picking in the natural field environment is 82.4%, which is better than the success rate of 80% of the market picking robot. In the picking experiment, the main reason for the unsuccessful positioning of citrus fruits is that the position of citrus fruits is beyond the picking range of the end-effector, and the motion parameters of the robot arm joint will produce errors, affecting the motion accuracy of the robot arm, leading to the failure of picking. This study can provide technical support for the exploration and application of the intelligent fruit-picking mode.

Purpose: The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking. Design/methodology/approach: To deal with dynamic obstacles for autonomous vehicles during parking, a long- and short-term mixed trajectory planning algorithm is proposed in this paper. In long term, considering obstacle behavior, A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory. In short term, this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model. Moreover, the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver. Findings: Compared with the spline optimization method, the results show that the proposed method can generate efficient obstacle avoidance strategies, safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units. Originality/value: It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.

To improve a vehicle's anti-roll performance while consuming less energy, an active/passive dual-mode switched control method based on a hydraulically interconnected suspension (HIS) is proposed. This method has two key components as follows: modelling the “mechanical-liquid-gas” coupled dynamic system consisting of hydraulic cylinders, a hydraulic pump and accumulators; and robustly controlling a time-delayed system with uncertain model and lag time. In this study, the nonlinear characteristics of the underlying actuators were considered, and a four degrees-of-freedom (4DOF) roll-plane half-car model was employed. The backstepping algorithm was adopted to maintain the body in the desired posture. Subsequently, a modified Smith predictor and a Taylor expansion of lateral acceleration were applied simultaneously to compensate for the time delay. Finally, simulations with uncertain parameters and lag time were performed. The results indicate that the proposed method is robust in the case of an uncertain control system and can effectively improve the vehicle's anti-roll performance.