Adaptive output feedback tracking control of robot manipulators using position measurements only
Adaptive output feedback tracking control of robot manipulators using position measurements only
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DOI：
10.1016/j.eswa.2007.05.030
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
S. Purwar;I. Kar;A. N. Jha
中科院分区：
文献类型：
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作者：
S. Purwar;I. Kar;A. N. Jha
针对无速度测量的机器人，考虑执行器约束，提出了一种新的轨迹跟踪自适应神经网络控制器。该控制器是基于结构知识的动态的机器人和关节位置的测量。系统不确定性（可能包括有效载荷变化、未知非线性和扭矩干扰）通过切比雪夫神经网络（CNN）估计。自适应控制器表示滤波技术的融合，以产生伪滤波跟踪误差信号（用于消除速度测量）和使用CNN的函数逼近理论。所设计的控制器保证了系统的局部渐近稳定性和位置误差收敛到零。所设计的控制器不仅对载荷变化等结构化不确定性具有鲁棒性，而且对扰动等非结构化不确定性具有鲁棒性。此外，所提出的控制器的计算复杂度降低相比，多层神经网络控制器。两连杆机器人的仿真结果表明了该控制方案的有效性。仿真结果还提供了比较所提出的控制器与控制器的速度估计有限差分方法只使用位置测量。
In this paper, a new adaptive neuro controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of joint positions only. The system uncertainty, which may include payload variation, unknown nonlinearities and torque disturbances is estimated by a Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to generate pseudo filtered tracking error signals (for the elimination of velocity measurements) and the theory of function approximation using CNN. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload variation but also to unstructured one such as disturbances. Moreover the computational complexity of the proposed controller is reduced as compared to the multilayered neural network controller. The validity of the control scheme is shown by simulation results of a two-link robot manipulator. Simulation results are also provided to compare the proposed controller with a controller where velocity is estimated by finite difference methods using position measurements only.