论文部分内容阅读
Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.
Discrete linear quadratic control has been efciently applied to linear systems as an optimal control. Host, a robotic system is highly nonlinear, heavily coupled and uncertain. To overcome the problem, the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks. This modeling motivates us to develop an optimal repetitive control. The contribution of this paper is twofold. For the frst time, it presents discrete linear quadratic repetitive control for electrically driven robots using the proposed control approach is based on the voltage control strategy. Second, uncertainty is efectively compensated by employing a robust time-delay controller. The uncertainty can include parametric uncertainty, unmodeled dynamics and external disturbances. To highlight its ability in overcoming the uncertainty, the dynamic equation of an articulated robot is introduced and used for the simulation, modeling and control purposes. stability analysis verifes the proposed control approach and simulation results show its efectiveness.