基于遗传算法的CVT夹紧力模糊-PID控制优化

针对国产金属带式无级变速器(CVT)夹紧力控制中存在金属带及带轮磨损严重、效率低下等问题,建立了夹紧力模糊-PID控制器模型.考虑到模糊控制存在主观性强、粗糙、不完善等问题,进一步引入遗传算法对模糊-PID控制器的隶属函数和模糊规则同时进行离线优化,最后将优化后的模糊-PID控制器进行实车道路试验,并与优化前的试验结果进行对比.结果表明:优化后的模糊-PID控制器可以使实际夹紧力对目标夹紧力具有更好的跟踪效果及稳态精度,较好地实现了CVT夹紧力的控制目标;另外,基于控制精度的改善,将夹紧力储备系数由1.3降至1.24,有效提高了CVT的经济性.

Fuzzy-PID Controller Optimization of Clamping Force for CVT Based on Genetic Algorithm

Aiming at improving the performance of clamping force control of metal V-belt Continuously Variable Transmission (CVT), such as metal belt abrasion and low efficiency, a clamping force fuzzy-PID controller model was established. Considering the defect of fuzzy-PID controller, such as subjective, rough and imperfect, genetic algorithm was applied to off-line optimize both membership function and fuzzy rules. At last, road tests were done to verify the performance of the optimized fuzzy-PID controller. The test results have shown that the improved fuzzy-PID controller has a satisfactory effect on the clamping force control with better tracing precision and less steady state error. Additionally, the CVT''s fuel economy was enhanced by reducing the safety factor of clamping force from 1.3 down to 1.24 on the basis of control precision.