Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adap...Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.展开更多
This paper considers the application of robust control methods(μ-and H∞-synthesis)to the speed and acceleration control problem encountered in electric vehicle powertrains.To this end,we consider a two degree of fre...This paper considers the application of robust control methods(μ-and H∞-synthesis)to the speed and acceleration control problem encountered in electric vehicle powertrains.To this end,we consider a two degree of freedom control structure with a reference model.The underlying powertrain model is derived and combined into the corresponding interconnected system required forμ-and H∞-synthesis.The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects.It is observed that theμ-controller offers performance advantages in particular for the acceleration control problem,but at the price of a high-order controller.展开更多
文摘Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.
文摘This paper considers the application of robust control methods(μ-and H∞-synthesis)to the speed and acceleration control problem encountered in electric vehicle powertrains.To this end,we consider a two degree of freedom control structure with a reference model.The underlying powertrain model is derived and combined into the corresponding interconnected system required forμ-and H∞-synthesis.The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects.It is observed that theμ-controller offers performance advantages in particular for the acceleration control problem,but at the price of a high-order controller.
