为解决无人驾驶车辆轨迹跟踪精度和控制稳定性问题,提出了一种考虑前馈控制和动态调整速度比例、积分、微分(Proportional Integral Derivative,PID)控制器参数的方法。采用七次多项式进行变道轨迹规划,改进纵向位置和速度双PID控制器,...为解决无人驾驶车辆轨迹跟踪精度和控制稳定性问题,提出了一种考虑前馈控制和动态调整速度比例、积分、微分(Proportional Integral Derivative,PID)控制器参数的方法。采用七次多项式进行变道轨迹规划,改进纵向位置和速度双PID控制器,动态调整纵向位移误差,并采用模糊控制对PID控制器参数进行实时整定;同时,结合基于“前馈+反馈”的线性二次型调节器(Linear Quadratic Regulator,LQR)控制算法求解横向位移误差和车身横摆角速度误差,使跟踪误差收敛,最终通过电机模型将控制量转化为期望前轮转角,解决了模型失配导致的横向位移误差较大的问题。进行仿真验证,当车辆以60 km/h的速度在城市道路场景下变道行驶时,横向位移误差控制在0.015 m范围内,纵向位移误差控制在毫米级别,误差范围控制在[0.002,0.006]m,车身横摆角速度变化平稳且横摆角速度误差不超过0.83 rad/s。在此基础上,进一步完成了实车实验,仿真与实车实验结果均表明,所设计的控制器可以达到轨迹跟踪中对高精度的要求,能够保证无人驾驶车辆在变道工况平稳行驶。展开更多
为提高自动驾驶车辆的路径跟踪精度,针对自动驾驶车辆横纵向耦合控制问题,提出了带有前馈控制的PID+LQR联合控制策略。首先,利用二自由度车辆动力学模型构建路径跟踪误差状态方程,制定横纵向控制流程。随后,设计了用于横向控制的线性二...为提高自动驾驶车辆的路径跟踪精度,针对自动驾驶车辆横纵向耦合控制问题,提出了带有前馈控制的PID+LQR联合控制策略。首先,利用二自由度车辆动力学模型构建路径跟踪误差状态方程,制定横纵向控制流程。随后,设计了用于横向控制的线性二次型调节(linear quadratic regulator,LQR)控制策略和用于纵向控制的比例积分微分(proportional integral differential,PID)控制策略,将横纵向控制器进行整合,使得车辆在接收到决策规划系统给出的期望指令后可以进行跟踪行驶。借助CarSim和MATLAB/Simulink联合仿真平台,在连续工况下对该控制策略进行测试。结果表明,提出的横纵向耦合控制策略可控制车辆沿着规划的轨迹行驶,且可将跟踪误差控制在理想的范围内。展开更多
Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical sy...Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions.The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.展开更多
A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model P...A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model Predictive Controller(MPC)design.MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix.In this paper,the optimal closed loop feedback gain matrix was calculated using Kautz function.Being an Orthonormal Basis Function(OBF),Kautz function has an advantage of solving complex pole-based nonlinear system.Genetic Algorithm(GA)was applied to optimally tune the Kautz function-based MPC.A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC.The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral(PI)controller and Linear Quadratic Regulation(LQR).展开更多
In this paper, an optimal control scheme for wind turbine output torque and power regulation under the influence of wind disturbances is presented. The system considered is a dynamic mechanical-based model with pitch ...In this paper, an optimal control scheme for wind turbine output torque and power regulation under the influence of wind disturbances is presented. The system considered is a dynamic mechanical-based model with pitch and generator torque actuators for controlling the pitch and generator torque. The performance of linear matrix inequality (LMI) formalism of linear quadratic regulator (LQR);linear quadratic regulator with integral action (LQRI) and model predictive control (MPC) were compared in response to a step change in wind disturbance. It is shown by Matlab simulation that the LQRI outperformed both LQR and MPC controllers.展开更多
文摘为提高自动驾驶车辆的路径跟踪精度,针对自动驾驶车辆横纵向耦合控制问题,提出了带有前馈控制的PID+LQR联合控制策略。首先,利用二自由度车辆动力学模型构建路径跟踪误差状态方程,制定横纵向控制流程。随后,设计了用于横向控制的线性二次型调节(linear quadratic regulator,LQR)控制策略和用于纵向控制的比例积分微分(proportional integral differential,PID)控制策略,将横纵向控制器进行整合,使得车辆在接收到决策规划系统给出的期望指令后可以进行跟踪行驶。借助CarSim和MATLAB/Simulink联合仿真平台,在连续工况下对该控制策略进行测试。结果表明,提出的横纵向耦合控制策略可控制车辆沿着规划的轨迹行驶,且可将跟踪误差控制在理想的范围内。
文摘Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions.The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.
文摘A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model Predictive Controller(MPC)design.MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix.In this paper,the optimal closed loop feedback gain matrix was calculated using Kautz function.Being an Orthonormal Basis Function(OBF),Kautz function has an advantage of solving complex pole-based nonlinear system.Genetic Algorithm(GA)was applied to optimally tune the Kautz function-based MPC.A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC.The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral(PI)controller and Linear Quadratic Regulation(LQR).
文摘In this paper, an optimal control scheme for wind turbine output torque and power regulation under the influence of wind disturbances is presented. The system considered is a dynamic mechanical-based model with pitch and generator torque actuators for controlling the pitch and generator torque. The performance of linear matrix inequality (LMI) formalism of linear quadratic regulator (LQR);linear quadratic regulator with integral action (LQRI) and model predictive control (MPC) were compared in response to a step change in wind disturbance. It is shown by Matlab simulation that the LQRI outperformed both LQR and MPC controllers.
