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Difficulty of the Automatic Transmission(AT) is to properly control its clutch while the vehicle starts. An intelligent hierarchical controller is proposed in this paper. The upper of it is an intelligent PID controller based on a driver's experience and an expert's knowledge. It gives real time signals to the lower controller and coordinates the control between engine and clutch. The lower Variable Structure Fuzzy Controller (VSFC) executes the upper controller's direction signal.

The city buses start and stop frequently, therefore significant amount of kinetic energy is wasted on friction plates. In order to retrieve this amount of energy, various feasible and efficient energy storage systems have been suggested by researchers to reuse the energy to improve the efficiency of the city bus. Due to small size, high energy density, cleanliness and easiness to absorb/reuse the energy, the storage system of flywheel is hopeful to be coming into wide use. In this paper, a dynamic model is analyzed to study the characteristics of the system using time series. Finally, comparison between simulation and experiment is given.

In this paper, the intelligent control theory and method using neural networks are presented, the multilayer feedforward networks employed, the back propagation learning algorithm proposed. Finally the angular velocity control result of the hydrostatic drive system of secondary regulation with such type of control is provided. Relative studies revealed that the intelligent control for the system using neural networks can be effective.

In this paper the nonlinear system identification theory and method using neural networks are presented, the multilayer feedforward networks employed, the backpropagation learning algorithm proposed. The inputs of the networks are consisted of angular velocity and throttle angle, and outputs torque of the engine, finally the comparision of simulation result with that of experiment and other results that embody the effect of system identification are given. Relative studies revealed that the nonlinear system identification for the engine of automated automobiles using neural networks can be effective.

The cruise control as one of promising vehicle active control has been receiving a considerable attention in recent years, so the related problems are discussed in this paper. Mathematical model of the engine under the steady state and general dynamics equation of the Continuously Variable Transmission(CVT) are given, the regulating principle of the operating state of the engine is analyzed, and the conjugate gradient method employed to solve the optimal control problem of the vehicle in the cruise control is described, so the operating state of the engine could be controlled. At end of paper, some results are provided to verify the approach based on the vehicular powertrain with a hydromechanical CVT.

In this paper, the system identification theory and method using dynamic neural networks are presented, the multilayer feedforward networks employed, the backpropagation with adaptive learning rate algorithms proposed. Finally the comparision of network output with that of the hydrostatic drive system of secondary regulation is given, and output error, sum-squared error et al, or the results that embody the effect of system identification given sine input to it are provided.