Active Control Algorithm For Energy-regenerative Suspension

As one of the most popular research direction in the vehicle chassis control, active suspensions can be well adapted to various operating conditions, and increase vehicle ride comfort and body attitude significantly. But the high-performance control algorithm requires a lot of energy, which limits the application of active suspensions. The energy-regenerative suspension can change the damper’s energy into electrical power for recycling to use for the active suspension. In this paper, an energy-regenerative active suspension with an electromagnetic damper was presented. This new design can usually improve vehicle system dynamics.Firstly, the author combined the ball screw structure with brushless DC motor as the energy-regenerative damper and also analyzed its structure and operation principle. Then, a mathematic model was built which includes active suspension sub-model and control circuit sub-model. In order to evaluate the active suspension’s energy consumption, two indexes, eg., self-powered energy efficiency and the feed efficiency were proposed.Based on the dynamic model above, three algorithms, the optimal control algorithm, fuzzy control algorithm and variable universe fuzzy control algorithm, are designed. The road input model and the energy-regenerative suspension model are established using Matlab/Simulink and the body vertical acceleration, suspension working space and dynamic tire deflection are chosen to evaluate suspension performance. After that, the influences on the control effects and the energy-regenerative effects choosing different road inputs and different control algorithms are analyzed respectively. The simulation results show that different road inputs have similar control effects for LQG controller. But for the fuzzy controller and variable universe fuzzy controller, the worse the road conditions, the better the control effect of body acceleration and dynamic tire deflection; But the suspension working space has a reverse conclusion. For these three controllers, the energy-regenerative efficiency didn’t vary with road inputs. With the same road input, the control effects of the LQG controller are the best, and those of the variable universe fuzzy controller are better than the fuzzy controller. The energy-regenerative effects of the LQG controller are the best, and those of the fuzzy controller are better than the variable universe fuzzy controller. Furthermore, in order to ensure the consistency of the actual control force and ideal control force, PI, PD and PID algorithms are applied to the circuit respectively. Choosing LQG controller as an example, these three circuits are added to mathematic model using Matlab. The results show similar improvements for them.