Adaptive Nonlinear PD Learning Control for Robot Manipulators

2004 ◽  
Author(s):  
P. R. Ouyang ◽  
W. J. Zhang ◽  
M. M. Gupta
Keyword(s):  
Control Structure ◽  
Control Method ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Learning Control ◽  
Asymptotic Convergence ◽  
Simulation Studies ◽  
Pd Control ◽  
On Line ◽  
On Line Learning

A new control method, called adaptive nonlinear PD learning control (NPD-LC), is proposed for robot manipulator applications in this paper. The proposed control structure is a combination of a nonlinear PD control structure and a directly learning structure. Consequently, this new control method possesses both adaptive and on-line learning properties. One of the unique features of the NPD-LC algorithm is that the learning is based on the previous torque profile of the repetitive task. It is proved that the NPD-LC enjoys the asymptotic convergence for both tracking positions and tracking velocities. Simulation studies were conducted by comparing the proposed method with many other existing methods. As a result, it was demonstrated that the NPD-LC method can achieve a faster convergence speed. The proposed NPD-LC is robust and can be implemented for the control of robot manipulators.

scholarly journals Analysis and Synthesis of Global Nonlinear H∞ Controller for Robot Manipulators

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Carlos Alberto Chavez Guzmán ◽  
Luis Tupak Aguilar Bustos ◽  
Jován Oseas Mérida Rubio
Keyword(s):  
Lyapunov Function ◽  
Degrees Of Freedom ◽  
Gravitational Force ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Global Asymptotical Stability ◽  
Simulation Studies ◽  
External Disturbances ◽  
Analysis And Synthesis ◽  
Force Compensation

The H∞ regulation problem for robot manipulators using gravitational force compensation or precompensation has been solved locally while global asymptotical stability (or global stability) has been demonstrated using other methodologies. A solution to the global nonlinear H∞ regulation problem for l-degrees-of-freedom (l-DOF) robot manipulators, affected by external disturbances, is presented. We showed that the Hamilton-Jacobi-Isaacs (HJI) inequality, inherited in the solution of the H∞ control problem, is satisfied by defining a strict Lyapunov function. The performance issues of the nonlinear H∞ regulator are illustrated in experimental and simulation studies made for a 3-DOF rigid links robot manipulator.

Application of learning to high-speed robotic manipulators

1998 ◽  
Vol 212 (4) ◽  
pp. 315-323
Author(s):  
A Kirecci ◽  
M J Gilmartin
Keyword(s):  
High Speed ◽  
Actuator Saturation ◽  
Learning Control ◽  
Advanced Technique ◽  
Control Techniques ◽  
High Speeds ◽  
Advanced Control ◽  
On Line ◽  
Physical Features ◽  
On Line Learning

When a desired signal is applied to a servo system it responds in a characteristic fashion and follows the required trajectory with an error. The physical features of the actuators and the gain setting of the controller are the main parameters that determine the response of the system. Controllers with fixed gain values are effective for many conventional processes using slow-speed manipulators. However, there are several cases where the precise tracing of a fast trajectory under different payloads requires more advanced control techniques. When the motion is cyclical, learning control is one advanced technique which is appropriate to use. Depending solely on measurements of data from the preceding cycle, its implementation in real time is both fast and efficient. In practice, however, it has been observed that learning can induce high-frequency ripples on the tuned command curve which with increasing iterations result eventually in the saturation of the system's actuators. In this study, the use of on-line learning control techniques is discussed and a new approach using digital filters is implemented to prevent actuator saturation from occurring when learning is applied. A planar robotic manipulator has been designed and built to investigate the practical problems of learning control, particularly when the system runs at high speeds.

Robust Iterative Learning Control for Vibration Suppression of Industrial Robot Manipulators

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Cong Wang ◽  
Minghui Zheng ◽  
Zining Wang ◽  
Cheng Peng ◽  
Masayoshi Tomizuka
Keyword(s):  
Iterative Learning Control ◽  
Vibration Suppression ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Gaussian Process Regression ◽  
Learning Control ◽  
Industrial Applications ◽  
Iterative Learning ◽  
Cost Constraints

Vibration suppression is of fundamental importance to the performance of industrial robot manipulators. Cost constraints, however, limit the design options of servo and sensing systems. The resulting low drive-train stiffness and lack of direct load-side measurement make it difficult to reduce the vibration of the robot's end-effector and hinder the application of robot manipulators to many demanding industrial applications. This paper proposes a few ideas of iterative learning control (ILC) for vibration suppression of industrial robot manipulators. Compared to the state-of-the-art techniques such as the dual-stage ILC method and the two-part Gaussian process regression (GPR) method, the proposed method adopts a two degrees-of-freedom (2DOF) structure and gives a very lean formulation as well as improved effects. Moreover, in regards to the system variations brought by the nonlinear dynamics of robot manipulators, two robust formulations are developed and analyzed. The proposed methods are explained using simulation studies and validated using an actual industrial robot manipulator.

Collison Control of the Robot Manipulator by a Learning Control Using the Weighted Least-Squares Method

1991 ◽  
Vol 3 (6) ◽  
pp. 491-496
Author(s):  
Hiroshi Wada ◽  
◽  
Toshio Fukuda ◽  
Hideo Matsuura ◽  
Fumihito Arai ◽  
...  
Keyword(s):  
Least Squares ◽  
High Speed ◽  
Control Method ◽  
Least Squares Method ◽  
Robot Manipulator ◽  
Weighted Least Squares ◽  
Learning Rule ◽  
Learning Control ◽  
Weighted Least Squares Method ◽  
Control Gain

Collision phenomena are very fast and nonlinear, thus, it is difficult to control a manipulator by collision phenomena. Therefore, in the past, manipulators moved slowly in order to avoid collision. However, the need for high-speed operation has been increasing, making it is indispensable to control manipulators by collision phenomena. With such fast phenomena, it is effective to use learning control in a forward manner. In this paper, we have proposed a learning control method to optimize the weighted least-squares criterion of learning errors. This method is applied in order to obtain a unique control gain by the Riccati equation which has a state dimension equal to the sampling number. It is shown that the convergence of learning error can be readily assured because the present learning rule consists of a steadystate Kalman filter. Based on this learning control method, experimental results of force control with a collision phenomena are reported.

An Adaptive Evolutionary Switching Control for Robot Manipulators

2005 ◽  
Author(s):  
P. R. Ouyang ◽  
W. J. Zhang ◽  
Madan M. Gupta
Keyword(s):  
Trajectory Tracking ◽  
Control Method ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Switching Control ◽  
Learning Ability ◽  
Pd Control ◽  
Control Approach ◽  
Gain Switching ◽  
Control Scheme

In this paper, a new adaptive switching control approach, called adaptive evolutionary switching PD control (AES-PD), is proposed for iterative operations of robot manipulators. The proposed AES-PD control method is a combination of the feedback of PD control with gain switching and feedforward using the input torque profile obtained from the previous iteration. The asymptotic convergence of the AES-PD control method is theoretically proved using Lyapunov’s method. The philosophy of the switching control strategy is interpreted in the context of the iteration domain to increase the speed of the convergence for trajectory tracking of robot manipulators. The AES-PD control has a simple control structure that makes it easily implemented. The validity of the proposed control scheme is demonstrated for the trajectory tracking of robot manipulators through simulation studies. Simulation results show that the AES-PD control can improve the tracking performance with an increase of the iteration number. The EAS-PD control method has the adaptive and learning ability; therefore, it should be very attractive to applications of industrial robot control.

scholarly journals A feedback control structure for on-line learning tasks

1997 ◽  
Vol 22 (3-4) ◽  
pp. 303-315 ◽  
Author(s):  
Manfred Huber ◽  
Roderic A. Grupen
Keyword(s):  
Feedback Control ◽  
Control Structure ◽  
Learning Tasks ◽  
On Line ◽  
On Line Learning
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