Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility.

Real-time status monitoring is an important prerequisite for coral reef ecological protection. Existing equipment does not provide an ocean observation platform with adequate mobility and efficiency. This paper describes the design considerations of a proposed autonomous underwater helicopter (AUH) dedicated for ecological observation of coral reefs, including the system architecture, electronic devices, sensors and actuators, and explains the path control algorithm and controller to follow a specific path for ocean exploration. The structure and dynamic model of the AUH are first introduced, and then the corresponding simplification is made for motion analysis. Furthermore, computational fluid dynamics (CFD) simulation is carried out to evaluate the dynamic performance of the AUH. Fuzzy-PID control algorithm is utilized to achieve a good antidisturbance effect. In order to validate the performance of the proposed underwater vehicle, a field test was performed, and results confirmed the feasibility of the proposed prototype.

Aiming at the issue of the sliding mode observer (SMO) being sensitive to the motor model parameters at low speeds, a rotor position observation method based on resistance adaptive SMO and considering the influence of inverter dead time is designed in this paper. In this method, the online resistance identification is introduced into the conventional SMO, the resistance parameters of the SMO are modified in real-time, and the online resistance identification algorithm is designed by using the Lyapunov function. An enhanced linear dead time compensation method is proposed to improve the resistance adaptive SMO and eliminate the voltage error between the estimated and the actual motor models in order to address the impact of inverter dead time on the voltage parameters in the estimation model at low speeds. Simulation results show that the online resistance identification and dead time compensation can significantly improve the accuracy of sensorless speed control at low speeds, and the dead time compensation can also improve the accuracy of online resistance identification.

A wheel motor simplifies the chassis structure of an electric bus, greatly improving its response speed and controllability. How to improve the lateral stability of the vehicle under complex and changeable driving conditions is a major problem in the motion control of electric buses. This study proposed an adaptive distributed drive control strategy for a rear-wheel drive electric bus. An adaptive fuzzy controller was designed to obtain the additional yaw moment of the vehicle and then combined with a rule distribution method to modify the steering characteristics of the vehicle to obtain the optimal driving torque distribution. Hardware-in-the-loop test results showed that under adaptive fuzzy control, the yaw rate deviations under low- and high-speed conditions were reduced from 18% and 42% without control to 10% and 23% with control, respectively. Under sine wave conditions, the deviation of the yaw rate and the vehicle’s sideslip angle were reduced from 83% and 852% without control to 12% and 15% with control, respectively. It was verified that the electric bus with adaptive fuzzy control could maintain good vehicle stability at full speed.

A suitable and effective control strategy is a prerequisite for achieving the stable driving of a distributed drive electric bus. In order to effectively utilize the advantage of the independent controllability of each rear wheel, this paper designs and compares two direct transverse moment control strategies of sliding mode control and self-correcting fuzzy control and distributes the drive torque in combination with the vehicle steering torque constraint. Moreover, based on the established seven-degrees-of-freedom vehicle model, the simulation was verified in the MATLAB/Simulink and TruckSim co-simulation platforms. The simulation results show that, compared with the sliding mode control, the self-correcting fuzzy control strategy can reduce the maximum sideslip angle deviation by 19%, 6% and 9.7%, respectively, under the double shift line condition, the high-speed small steering angle step condition and the sinusoidal line shift condition and can more effectively reduce the vehicle lateral acceleration and improve the vehicle yaw rate tracking ability, significantly improving the lateral stability of the vehicle.

This paper proposes a control strategy that combines an improved flux-based sliding mode observer with a Radial Basis Function Proportional-Integral-Derivative (RBF-PID) controller for the control of Permanent Magnet Synchronous Motors (PMSM). The strategy aims to address the issues of electrical angle estimation errors and torque fluctuations in traditional sliding mode observer control. The improved sliding mode observer utilizes the flux model of the PMSM to enhance the accuracy of electrical angle estimation, thereby reducing the estimation errors and improving the control of the current loop and speed. The RBF-PID controller ensures system stability while achieving faster response and reduced torque fluctuations. Simulation and experimental results demonstrate that compared to traditional PI control and sliding mode observer control methods, the proposed strategy improves the performance of electrical angle estimation by 7.05% and reduces overshoot in the q-axis current by 28.6%, exhibiting better control performance and smaller errors.