Machines

To achieve precise tracking control under the hardware of electronic throttle digital control, this paper proposes a novel implicit discrete-time fast terminal sliding mode control method for electronic throttles. Specifically, a recursive discrete-time fast terminal sliding surface is adopted, which can ensure that the system error converges to a smaller error bandwidth. To further eliminate the chattering problem in actual systems, a new generalized implicit discrete reaching law is designed. Compared with the traditional explicit Euler reaching law, the proposed implicit reaching law can effectively suppress the chattering caused by discretization through backward discretization (implicit discretization). Meanwhile, compared with existing implicit discrete reaching laws, the proposed reaching law has an explicit recursive equation, avoiding the problems of high computational complexity and difficult engineering implementation. Subsequently, strict theoretical analysis proves the existence of the quasi-sliding mode (QSM) and the convergence of the system tracking error. Moreover, experimental results demonstrate that the proposed controller achieves faster transient response, smaller steady-state tracking error, and significantly reduced chattering compared with DFSM, DSTWISTING, and DLSMC, highlighting its clear performance advantages over existing sliding mode methods.

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these issues, this paper proposes a globally optimized path planning method based on energy consumption minimization. The proposed method introduces a multi-factor coupled energy consumption model for pure electric commercial vehicles, integrating slope gradients, load capacity, motor efficiency, and energy recovery. Using this vehicle energy consumption model and the park road network topology map, an energy consumption topology map representing energy consumption between any two nodes is constructed. An energy-optimized improved ant colony optimization algorithm (E-IACO) is proposed. By introducing an exponential energy consumption heuristic factor and an enhanced pheromone update mechanism, it prioritizes energy-saving path exploration, thereby effectively identifying the optimal energy consumption path within the constructed energy consumption topology map. Simulation results demonstrate that in typical three-dimensional industrial park scenarios, the proposed energy-optimized path planning method achieves maximum reductions of 10.57% and 4.90% compared to the A* algorithm and ant colony optimization (ACO), respectively, with average reductions of 5.14% and 1.97%. It exhibits excellent stability and effectiveness across varying load capacities. This research provides a reliable theoretical framework and technical support for reducing logistics operational costs in industrial parks and enhancing the economic efficiency of pure electric commercial vehicles.

In this work, the yaw control of large wind turbines is studied. The objective is to analyse how to maximise energy conversion by yawing the rotor in response to wind direction while minimising yaw activity. In order to improve the control performance, three algorithms are used and compared: the classic PI controller, the nonlinear PI controller, and the fractional-order nonlinear PI controller. An adaptive dead-zone and anti-windup procedure for amplitude- and rate-limited actuators are also considered, which helps to reach the main objective. Simulation experiments are carried out on a 20 MW reference wind turbine. The results are very promising, showing clear performance improvements.