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This paper presents a general control module to control the speed of an electric vehicle (EV). This module consists of a microprocessor and several C-programmable micro-controllers. It uses an identification algorithm to estimate the system parameters on-line. With the estimated parameters, control gains are calculated via pole-placement. In order to compensate for the internal errors, a cross-coupling control algorithm is included. To estimate the true velocity and acceleration from measurements, a discrete-time Kalman filter was utilized. The experimental results validate the general control module for EVs.

This paper presents a comprehensive model to represent the complex interaction between tracks of a tracked vehicle and the ground. Based on this model, a simulation was developed to solve both the forward and inverse kinematics. For straight line motion, which is a special case of the comprehensive model, a novel traction control technique for tracked vehicle is introduced. This control law is based on sliding mode control. Traction control should enhance tracked vehicle performance and maximize vehicle traction by preventing the vehicle from becoming locked up during braking and from spinning during acceleration. Both theoretical analysis and simulations confirm the effectiveness of our approach.

This paper introduces a new traction control method that assures vehicle operational stability on a low friction road. On a low friction road, the vehicle becomes unstable as the drive wheels spin under excess engine torque. Instead of using friction coefficient versus slip-ratio curve and exact vehicle speed, this paper uses a friction coefficient detector. Accordingly, the concept of Sign of Torque Change relative to Friction Coefficient (STCFC) is introduced and applied to a rule-based traction controller. In order to perform simulations, a formula that represents the traction force versus longitudinal slip was proposed. Simulation results verify this approach.