Traction Control Method Research Articles

A combined control method of traction and ballast for an electric tractor in ploughing based on load transfer

Agricultural tractors are significant agricultural power machines that must be equipped with different implements according to tillage requirements and operate in a complex field environment. Aiming at the problem that the traditional traction control system could not balance the agronomic requirements of tillage and high-efficiency operation, this study proposed a combined control method of traction and ballast based on load transfer. This method was used for electric tractors with a battery position adjustment (BPA) structure. The tractor-implement system model was built to calculate the dynamic loads of front and rear axles accurately. Then, a multi-objective optimization function for traction performance was constructed, and the multi-objective particle swarm algorithm was employed to solve the optimal battery pack target position. The tractor controller real-time measured the draught force and battery position data. Based on the combined control strategy, the battery pack position and the tillage depth were adjusted to accomplish the cooperative control of the active ballast system and the electro-hydraulic hitch system. Finally, a field test platform for the electric tractor was developed. Ploughing tests under the traction control method and combined control method were performed. The test results showed that the tillage depth uniformity and traction performance under the combined control method were superior to the traction control method. In particular, compared with the traction control method, the tillage depth fluctuation range in the combined control method was reduced by 30.6%, the wheel slip was decreased by 15.1%, the traction efficiency was enhanced by 3.7% and the total motor energy loss was decreased by 4.9%. This study provides a technical reference for improving the economy of energy consumption and the uniformity of tillage depth of tractors in ploughing.

Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains

Considering that the speed control system of the suspended permanent magnetic maglev train is more complicated and the parameters are more unstable than those of other trains, the traditional speed-tracking algorithm has large tracking errors, frequent controller output changes, high energy consumption, and decreasing the passengers’ riding comfort. To improve the shortcomings of the traditional automatic train operation (ATO) control algorithm, this paper proposes a predictive fuzzy proportional-integral-derivative control algorithm with weights (WM-F-PID). The main contribution of this work is to propose a cascaded predictive fuzzy PID (F-PID) control algorithm architecture with weights and use an improved steepest descent method to calculate online the weight of the F-PID controller input occupied by the predictive controller output. Compared with the proportional-integral-derivative (PID), F-PID, model predictive control (MPC), and simple cascade predictive fuzzy PID (M-F-PID) control algorithms, this control algorithm effectively improves train tracking accuracy and comfort and reduces train energy consumption and stopping errors.

Coordinative traction control of vehicles based on identification of the tyre–road friction coefficient

Traction control, which can be performed by different types of chassis control system, plays an important role in vehicle motion control. Since the propulsive force is actually produced by the friction between the tyre and road, information on the tyre–road friction is crucial for traction control. In this paper, a robust and effective tyre–road friction coefficient identification algorithm for straight acceleration is proposed, and a coordinative traction control method is designed by integrated usage of gear shifting control, engine control and braking control. For different driving conditions, the tyre forces were observed by a sliding-mode observer or calculated from the states of the vehicle directly, and the tyre–road friction coefficients were estimated by the recursive least-squares method or calculated from the linear characteristics between the friction coefficient and the slip ratio consequently. Based on the estimated tyre–road information, a practical and systematic coordinative traction control algorithm was designed to integrate shifting control, engine torque control and braking pressure control. Finally, the proposed methods are verified by both simulations and road tests. The results show that the estimation algorithms can identify the variation in the road conditions with considerable accuracy and response speed, and the controller successfully adjusted the slip ratios of the driving wheels in the stable region with good performances on different types of road.

Traction Control Method of Hybrid Electric Vehicle based on Multi-Objective Dynamic Coordination Control

The control method of the conventional traction control system on split-μsurfaces improves vehicle acceleration performance, but influences its stability performance. To solve this problem, a hierarchical traction control system for ISG hybrid electric vehicles based on multi-objective dynamic coordination control (MHEVTCS) is proposed. In the upper level controller, a target driving torque calculating strategy based on dynamical sliding mode control is developed. In the lower level controller, such strategies as multi-objective dynamic coordination control strategy, brake torque control strategy based on an inverse model, target engine torque design strategy and torque dynamic coordinate control strategy are proposed. Detailed simulation and hardware-in-loop experiment results show that slipping wheels are controlled quickly, accurately and smoothly by MEHVTCS. MHEVTCS solves the problem of merely pursuing acceleration performance and neglecting stability performance of conventional traction control system.

Fuzzy Tranction Control.

The driving force of an automobile depends ultimately upon the relationship between the tire and road surface. Therefore, traction control which prevents tire slip is becoming an impotrant issue from the point of view of driving performance and the stability of automobiles. Reported here is the traction control method using fuzzy control theory. Fuzzy control is "rule governed", and is said to be suitable for strong nonlinear objects such as this. Another special feature of this traction control is the use of the acceleration signal of the automobile, and this optimizes the engine performance even under the conditions of different slip characteristics. The investigation was carried out by experiment as well as by simulation. In the experiment, an electrical motor scooter on a chassis dynamometer was used instead of an actual automobile and road. Relatively good results were obtained.