Prescribed performance control (PPC) provides new insights into the path tracking problem for autonomous ground vehicles (AGVs), yet conventional algorithms struggle with fluctuating road conditions and limitations for communication bandwidth. To this end, this article develops a novel road-adaptive path tracking scheme that combines flexible prescribed performance with an event-triggered mechanism, without relying on vehicle model or parameter identification. Initially, the path tracking control is abstracted as an unknown nonlinear system, sidestepping model nonlinearity, parameter variations, and external disturbances. The runnel-shaped boundary with appointed-time prescribed performance functions (PPFs) is then introduced, managing initial constraints and mitigating overshoot simultaneously. Subsequently, the flexible PPC (F-PPC) is designed to ensure proximate appointed-time stability, where the auxiliary system adjusts performance boundaries according to road width. Moreover, an adaptive event-triggered mechanism is integrated to minimize communication frequency and bit rate, requiring only 2-bit data transmission and adjusting the trigger threshold dynamically in response to road curvature. Experimental results validate the effectiveness, robustness, and efficiency of the proposed road-adaptive path tracking scheme.

Distributed drive electric systems have gained widespread use in the automotive industry with increasing intensity of motor energy recovery. Addressing issues of wheel lock-up and vehicle instability during energy recovery on low-adhesion surfaces, a regenerative torque control strategy for four-wheel distributed drives under low-adhesion conditions is proposed, suitable for energy recovery control in scenarios like low-adhesion, joint road, etc. Initially, an adhesion estimation method based on tire longitudinal dynamics and fuzzy logic is proposed. By considering slip rate and adhesion, combined with motor characteristics and the magic formula, a tire dynamics model during regenerative braking is established. Using this model, a model predictive control (MPC-RTC) strategy for regenerative torque control is implemented, compared with traditional logic threshold control strategies. Tests in joint Carsim-Simlink simulations and real-vehicle validation have been conducted. Experimental results show that the proposed low-adhesion regenerative braking anti-lock strategy improves the intensity of regenerative braking by 10.4% and increases energy recovery efficiency by 10%, maximizing energy recovery efficiency and vehicle range while ensuring vehicle stability.

Integrated-Electronic-Parking-Braking (EPBI) system is an important component of vehicles. Intelligent wheel speed sensor is the essential basic sensor of intelligent vehicle. The stable wheel speed signal is the key factor to realize EPBI control. Based on uncertain algorithm, a new type of circuit and fusion signal processing (FSP) is proposed, which solves the effective signal screening and signal fluctuation problem in traditional process system of intelligent wheel speed sensor. The wheel speed processing circuit includes an adjustable voltage regulator supply module (RSM) and signal processing module (SPM). On the basis of the circuit, FSP is designed, which can reduce the sensitivity of wheel speed signal to environmental noise to get a stable signal. The results show that this method processes the intelligent wheel speed signal effectively, improves the stability of wheel speed signal and ensure the basic braking function of EPBI.

The steer-by-wire (SbW) system has gained recognition as the future of intelligent vehicles due to its attributes, such as safety, simplification, and flexibility. However, the elimination of mechanical linkage necessitates the provision of artificial steering feedback torque (SFT), which is crucial for the potential driver to manipulate the vehicle. To enhance the steering feel, this article extends the SFT design range and proposes an SFT prediction scheme based on the sequence-to-sequence (S2S) network with the switcher-assisted (SA) training algorithm. The models of the electric power steering (EPS) and SbW are first established to analyze the input features. The S2S network with gated recurrent units (GRU) is then presented, where the encoder scheme incorporates the squeeze-and-excitation (SE) operation to achieve adaptive feature recalibration. Subsequently, the SA algorithm is proposed to alleviate the exposure bias based on the principle of multitask learning (MTL), wherein online training is regarded as the main task and offline training is treated as the auxiliary task. The weighting coefficients between tasks are optimized using an assisted network named switcher, facilitating a gradual transition from MTL to the single main task, thereby avoiding complex tuning processes. The validation results indicate the proposed scheme outperforms existing methods regarding estimation and prediction. The ablation experiments are further conducted to illustrate the effectiveness of SE blocks and the SA algorithm. Finally, the SFT construction simulation, involving target prediction and torque tracking, is conducted, validating that variable-length prediction can adapt to various conditions and improve tracking performance.

In this article, the trajectory generation and motion control of autonomous driving oversized cargo cooperative transportation systems (CTS) in static but bounded environment is investigated. Different from common vehicle systems, the challenges lie on the safety-guaranteed cooperation of independently controlled carriers with inherent connections brought by the rigid payload, which results in complex system dynamics and multiple time-variant uncertainties. A constraint-oriented “leader-follower” modeling and control framework is introduced, and a trajectory generation method based on the diffeomorphism is creatively proposed to generate closed-form collision-free trajectory for the payload in the bounded environment. To achieve safety-guaranteed trajectory following under uncertainties, a transformed adaptive robust control strategy (TARC) is designed through constraint relaxation, and the coordination of the carriers is realized. An implementation with comprehensive ablation studies demonstrates the effectiveness of our trajectory generation and tracking control framework. The collision-free trajectory set is efficiently generated, and the CTS can be kept strictly inside the safe corridor with high tracking accuracy, which is extremely hard for the baseline methods.

A novel distributed cooperative formation control method inspired by the aggregate behaviors of fish groups is proposed for plug-in hybrid electric vehicle (PHEV) formation. Firstly, a hierarchical control architecture is established for formation keeping with fuel consumption (FC) optimization simultaneously. The top layer is to generate a leader with the optimal performance based on the nonlinear model predictive control (MPC) technique. A fish swarm optimization (FSO) algorithm is proposed to solve the nonlinear MPC problem by imitating the predation behaviors of the fish swarm. The middle layer is a decentralized intelligent cruise control (ICC) for follower vehicles to track their leader imitated the behaviors of fish swarm, and some design criteria are presented based on the Lyapunov stability theory. The under layer is to achieve a satisfying performance for the hybrid powertrain systems of followers. Finally, the bio-inspired method applied for PHEV formation is verified with a satisfying robustness, fuel economy, car-following and also real-time processing performances. According to the results, the PHEV formation using the proposed method represents a better car-following performance compared with the normal adaptive cruise control (ACC) method and a 21.26% improvement of FC compared with the rule-based energy management strategy (EMS). The computational burden is also reduced by the bio-inspired method.

Due to increasing complexity of systems in industrial applications, decoupling control of interconnected or cascaded systems has been discussed a lot recent years. A traditional way to deal with the system is to decouple the system states so that the states of each subsystem can be dealt with independently. This works theoretically well in most cases, however, for systems with high dimensions or with constraints on specific subsystems, this method may not be efficient or flexible enough in practical applications. In view of this, this paper proposes a backpropagation algorithm for decoupling control of a linear time invariant cascaded system with inner feedback loops, which simplifies the complicated system into several simpler ones, so that the system can be dealt with more efficiently and flexibly. Accordingly, the stability of the system is analyzed in a feedforward way, establishing the sufficient as well as necessary conditions for asymptotic stability of the system. Simulation experiment on the steering module of a steer-by-wire system verifies the effectiveness of the proposed algorithm. Finally, future researches following this paper are pointed out to perfect the algorithm proposed.

Gear engagement is the most important part in gear-shift process of automated manual transmission (AMT). However, it is practical to encounter complicated nonlinearities, uncertainties, and multistage characteristics in the system model, so the controller design for the AMT gear engagement becomes challenging. This paper proposes a dual-loop self-learning fuzzy control framework. In the outer loop, the self-learning rules based on fuzzy logic is designed to adjust desired trajectory of actuator motor. In the inner loop, the gear engagement is divided into three stages, and a fuzzy controller with model reference self-learning algorithm is designed, which controls the actuator motor to track the desired trajectory. Besides, the control parameters could be adjusted to be optimal automatically when the parameters change. Results of simulations and experiments indicate that the proposed method is able to realize the smooth and fast control of gear engagement. In addition, the self-learning fuzzy controller can be extended to deal with other nonlinear systems with uncertain and even unknown parameters.