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Using Adaptive Gain Scheduling LQR Method Control of Arm Driven Inverted Pendulum System Based on PIC18F4431 被引量:1

Using Adaptive Gain Scheduling LQR Method Control of Arm Driven Inverted Pendulum System Based on PIC18F4431
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摘要 The arm driven inverted pendulum system is a highly nonlinear model, muhivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum with variable position of the ann. To solve this problem, this paper presents a mathematical model for arm driven inverted pendulum in mid-position configuration and an adaptive gain scheduling linear quadratic regulator control method for the stabilizing the inverted pendulum. The proposed controllers for arm driven inverted pendulum are simulated using MATLAB-SIMULINK and implemented on an experiment system using PIC 18F4431 mieroeontroller. The result of experiment system shows the control performance to be very good in a wide range stabilization of the arm position. The arm driven inverted pendulum system is a highly nonlinear model,multivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum with variable position of the arm.To solve this problem,this paper presents a mathematical model for arm driven inverted pendulum in mid-position configuration and an adaptive gain scheduling linear quadratic regulator control method for the stabilizing the inverted pendulum.The proposed controllers for arm driven inverted pendulum are simulated using MATLAB-SIMULINK and implemented on an experiment system using PIC 18F4431 microcontroller.The result of experiment system shows the control performance to be very good in a wide range stabilization of the arm position.
出处 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2013年第4期85-92,共8页 哈尔滨工业大学学报(英文版)
关键词 Arm Driven Inverted Pendulum (ADIP) adaptive gain scheduling LQR control LQR control swing up pendlum 倒立摆系统 模型驱动 增益调度 控制臂 自适应 LQR方法 线性二次型调节器 人工神经网络
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