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
Uncertainty is inherent and unavoidable in almost all engineering systems. It is of essential significance to deal with uncertainties by means of reliability approach and to achieve a reasonable balance between reliab...Uncertainty is inherent and unavoidable in almost all engineering systems. It is of essential significance to deal with uncertainties by means of reliability approach and to achieve a reasonable balance between reliability against uncertainties and system performance in the control design of uncertain systems. Nevertheless, reliability methods which can be used directly for analysis and synthesis of active control of structures in the presence of uncertainties remain to be developed, especially in non-probabilistic uncertainty situations. In the present paper, the issue of vibration con- trol of uncertain structures using linear quadratic regulator （LQR） approach is studied from the viewpoint of reliabil- ity. An efficient non-probabilistic robust reliability method for LQR-based static output feedback robust control of un- certain structures is presented by treating bounded uncertain parameters as interval variables. The optimal vibration con- troller design for uncertain structures is carried out by solv- ing a robust reliability-based optimization problem with the objective to minimize the quadratic performance index. The controller obtained may possess optimum performance un- der the condition that the controlled structure is robustly re- liable with respect to admissible uncertainties. The proposed method provides an essential basis for achieving a balance between robustness and performance in controller design ot uncertain structures. The presented formulations are in the framework of linear matrix inequality and can be carried out conveniently. Two numerical examples are provided to illustrate the effectiveness and feasibility of the present method.展开更多
This paper designs a novel controller to improve the path-tracking performance of articulated dump truck(ADT). By combining linear quadratic regulator(LQR) with genetic algorithm(GA), the designed controller is used t...This paper designs a novel controller to improve the path-tracking performance of articulated dump truck(ADT). By combining linear quadratic regulator(LQR) with genetic algorithm(GA), the designed controller is used to control linear and angular velocities on the midpoint of the front frame. The novel controller based on the error dynamics model is eventually realized to track the path high-precisely with constant speed. The results of simulation and experiment show that the LQR-GA controller has a better tracking performance than the existing methods under a low speed of 3 m/s. In this paper, kinematics model and simulation control models based on co-simulation of ADAMS and Matlab/Simulink are established to verify the proposed strategy. In addition, a real vehicle experiment is designed to further more correctness of the conclusion. With the proposed controller and considering the steering model in the simulation, the control performance is improved and matches the actual situation better. The research results contribute to the development of automation of ADT.展开更多
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.
基金supported by the National Natural Science Foundation of China(51175510)
文摘Uncertainty is inherent and unavoidable in almost all engineering systems. It is of essential significance to deal with uncertainties by means of reliability approach and to achieve a reasonable balance between reliability against uncertainties and system performance in the control design of uncertain systems. Nevertheless, reliability methods which can be used directly for analysis and synthesis of active control of structures in the presence of uncertainties remain to be developed, especially in non-probabilistic uncertainty situations. In the present paper, the issue of vibration con- trol of uncertain structures using linear quadratic regulator （LQR） approach is studied from the viewpoint of reliabil- ity. An efficient non-probabilistic robust reliability method for LQR-based static output feedback robust control of un- certain structures is presented by treating bounded uncertain parameters as interval variables. The optimal vibration con- troller design for uncertain structures is carried out by solv- ing a robust reliability-based optimization problem with the objective to minimize the quadratic performance index. The controller obtained may possess optimum performance un- der the condition that the controlled structure is robustly re- liable with respect to admissible uncertainties. The proposed method provides an essential basis for achieving a balance between robustness and performance in controller design ot uncertain structures. The presented formulations are in the framework of linear matrix inequality and can be carried out conveniently. Two numerical examples are provided to illustrate the effectiveness and feasibility of the present method.
基金the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-15-023A1)the National Key R&D Program Project(Nos.2016YFC0802905 and 2018YFC0604403)
文摘This paper designs a novel controller to improve the path-tracking performance of articulated dump truck(ADT). By combining linear quadratic regulator(LQR) with genetic algorithm(GA), the designed controller is used to control linear and angular velocities on the midpoint of the front frame. The novel controller based on the error dynamics model is eventually realized to track the path high-precisely with constant speed. The results of simulation and experiment show that the LQR-GA controller has a better tracking performance than the existing methods under a low speed of 3 m/s. In this paper, kinematics model and simulation control models based on co-simulation of ADAMS and Matlab/Simulink are established to verify the proposed strategy. In addition, a real vehicle experiment is designed to further more correctness of the conclusion. With the proposed controller and considering the steering model in the simulation, the control performance is improved and matches the actual situation better. The research results contribute to the development of automation of ADT.
