For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated....For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.展开更多
A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer syste...A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.展开更多
文摘为了消除自动导航车(AGV)麦克纳姆驱动轮间负载不均衡引起的控制系统不确定性问题,提出基于模型参考自适应控制(MRAC)的伺服控制方法.建立驱动轮伺服电机的数学模型,根据李雅普诺夫稳定理论求解MRAC控制律和自适应律,并确定自适应律算法的正定矩阵.根据AGV实际运行过程中伺服电机的频率特性,引入低通滤波器.综合AGV定位精度和MRAC系统稳定性,确定二阶巴特沃兹低通滤波器的截止频率.在保证控制系统稳态精度的同时,有效抑制高频振荡.实验结果表明:基于MRAC的AGV在复杂工况下的控制性能保持了较高的稳定性,每个电机的速度波动不超过3%,定位精度误差小于3.6 mm.
基金supported by National Natural Science Foundation of China (No. 60774010, 10971256, 60974028)Natural Science Foundation of Jiangsu Province (No. BK2009083)+2 种基金Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province(No. 07KJB510114)Shandong Provincial Natural Science Foundation of China (No. ZR2009GM008)Natural Science Foundation of Jining University (No. 2009KJLX02)
文摘For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.
文摘A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.
