Search Results

The vehicle chassis integrated control system can improve the stability of vehicles under extreme conditions using tire force allocation algorithm, in which, the nonlinearity and uncertainty of tire-road contact condition need to be taken into consideration. Thus, An MPC (Model Predictive Control) controller is designed to obtain the additional steering angle and the additional yaw moment. By using a robust optimal allocation algorithm, the additional yaw moment is allocated to the slip ratios of four wheels. An SMC (Sliding-Mode Control) controller is designed to maintain the desired slip ratio of each wheel. Finally, the control performance is verified in MATLAB-CarSim co-simulation environment with open-loop manoeuvers.

An investigation is made to the multiobjective suspension control problem for a heavy off-road vehicle based on mixed H2/H ∞ optimal control synthesis. A design procedure is explained based on a performance trade-off curve in order to solve this problem. In comparison with other control synthesis results we have obtained, it is shown that by combining both techniques into one mixed norm optimization framework, it is possible to exploit the strengths of each norm to provide better performance for the given hydropneumatic suspensions.

The paper describes the idea of constructing the rapid development system of vehicle electric control subsystem in details by using hardware-in-the-loop (HiL) simulation technology for cost reduction in today's competitive business environment. This rapid development system is probably most effectively used in parallel with a new vehicle electronic control product. Once a desired simulation results are achieved, it can be verified by setting up the same configurations in the new product and comparing results. The feasibility of using such rapid development system is demonstrated through vehicle ABS algorithm development. Significant reduction of developing cycle time and cost has been achieved with the aid of this powerful tool and promising effectiveness of ABS algorithm has been validated in vehicle field test.

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, a new control strategy is proposed for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. The PID controller is used to minimize vehicle body vertical acceleration and the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to achieve optimal vehicle performances and adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, gain values and the membership function of fuzzy logic controller on-line.

Multi-body system dynamics is an efficient approach for vehicle dynamic simulation and performance analysis. The more sophisticated multi-body system softwares provide powerful tools for vehicle designers. The integration technique of CAD/CAM/CAE is being developed and applied more widely in automotive engineering. In this paper, considering the limits of a traditional multi-rigid body method for vehicle modeling and because of the fact that the non-linearity and typical flexible properties physically exist in many vehicle components, a rigid-elastic coupling multi-body system dynamic model is established for a light bus. The full vehicle model of the bus is built by CAD software in a 3D solid fashion. Hence, the necessary modeling parameters of the bus are obtained for simulations. The effects of the non-linearity and flexible properties of the components are investigated in detail.

Since the nonlinearity which inherently exists in vehicle system need to be considered in active suspension control law design, a new control strategy is proposed for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a PID controller and a fuzzy logic controller in two loops. In this paper, the investigation is mainly focused on vehicle ride comfort performance and simulations in straight running operating condition are presented. The control goal is to minimize vehicle body vertical and pitch accelerations for passenger comfort. The control system consists of two parallel control loops. One loop, using PID control, is to minimize vehicle body vertical acceleration; and the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors.

Automatic identification of road profiles ensures primarily right working condition of an anti-lock braking system (ABS). This paper presents a practical strategy to identify road conditions according to braking pressure, wheel slip ratio and angular deceleration. A fuzzy controller is designed to follow the target wheel slip ratio determined by road conditions. Simulation tests have been undertaken respectively on single and variable adhesion road surfaces by using a 7-DOF nonlinear vehicle model, accounting for nonlinearity of suspension and tire. It is shown that the ABS fuzzy controller can identify changes of road conditions precisely and therefore modulate the control scheme, so as to obtain the maximum road friction and good lateral stability. In comparison with a conventional PID controller, the present fuzzy controller possesses of more robustness and better performance.

For a semi-active suspension design, an important subject is to determine the control law which can achieve good performance both in ride and handling performance. Because of its superiority in non-linear control systems and capability of learning on-line, the fuzzy neural networks (FNNs) control scheme is proposed in this paper for a semi-active suspension system with dynamic absorber. The quarter vehicle model is described by a nonlinear system with three DOF subject to irregular excitation from a road surface and FNNs control scheme is employed. The on-line learning of FNNs to optimize fuzzy inference system is presented. Four kinds of methods, including passive suspension respectively with and without dynamic absorber, semi-active suspension respectively using fuzzy control and FNNs control, are investigated by computer simulation and comparison is made.