Zheng Geng

Two-dimensional model and algorithm analysis for a class of iterative learning control systems

The iterative learning control system (ILCS) has attracted considerable research interest in recent years. However the theoretical results on some fundamental properties of the ILCS have not yet been established. One of the common drawbacks shared by current approaches is lack of a suitable mathematical model which can clearly describe both the dynamics of the control system itself and the behaviour of the learning process as well. In this paper, a class of iterative learning control systems is analysed from the point of view of two-dimensional (2-D) system theory. The 2-D model for a class of ILCS is established in the form of‘Roessors model’, based on which a general type of learning controller is proposed. Analysis of the 2-D error equation shows that the 2-D asymptotic stability of the 2-D model guarantees the learning convergence of an ILCS. Design criteria for a learning controller is also suggested. The learning gain matrices are obtained from a recursive 2-D identifier using the input and output ...

Learning control system design based on 2D theory-an application to parallel link manipulator

An approach to iterative learning control system design based on 2D system theory is presented. A 2D model for the iterative learning control system which reveals the connections between learning control systems and 2D system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by 2D stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by computer simulation results.<<ETX>>

Expert self-learning controller for robot manipulator

Real-time expert system techniques and applications to robot manipulator control systems are discussed. A novel type of intelligent controller structure, the expert learning controller prototype ELEC (expert learning controller), is developed for the trajectory tracking control in repeat operations. ELEC, acting as an intelligent real-time controller in a closed-loop system, can modify the control series in a human-expert-like manner using the experience of previous operations in order to force the system output to converge to the prespecified desired trajectory. ELEC does not require the knowledge of system models, so it can be used in a fairly wide range of control problems. A numerical example for a two-link robot manipulator is given which shows the satisfactory performance of ELEC.<<ETX>>

A learning control scheme with gain estimator

An adaptive learning control approach is proposed that combines a mechanism to improve control input sequence as well as to improve the learning control scheme based on the knowledge learned about unknown system and environment. First, the iterative learning control problem is treated from the 2-D system point of view. A 2-D model for a class of iterative learning control systems is formulated. Then a learning gain estimator algorithm based on the 2-D model is presented. The overall learning control system structure is given.<<ETX>>

An adaptive learning control approach

An adaptive learning control approach is proposed which combines a mechanism to improve the control input sequence as well as to improve the learning control scheme based on the knowledge learned about the unknown system and environment. The iterative learning control problem is treated from the 2D system point of view. A 2D model for a class of iterative learning control system is formulated. A learning gain estimator algorithm based on the 2D model is presented. The overall learning control system structure is given. The proposed learning control scheme does not require prior knowledge of the controlled system and has the ability to generalize the knowledge learned from one task operation to other tasks. This scheme can be applied to nonlinear system control problems. To demonstrate the feasibility of the proposed learning algorithm, simulation results on learning control for a three-water-tank system are given. The results show an excellent learning performance, even for nonrepetitive tasks.<<ETX>>

Design of self-learning controllers using expert system techniques

The use of real-time expert system techniques to control systems, including robot manipulator control systems, is discussed. A novel type of intelligent controller structure, the expert learning controller prototype called ELEC (expert learning controller), is developed for the control series optimization of trajectory tracking problems in repeat operations. The ELEC, acting as an intelligent real-time controller in a closed-loop system, can modify the control series in a human-expert-like way using the experience of previous operations in order to force the system output to converge to the previously given desired trajectory. With the self-learning functions, the ELEC does not require the knowledge of system models; thus, it can be used in a wide range of control problems, especially in robot control. Numerical examples and simulation results of nonlinear, time-varying and multiple-variable robot systems are given to show the satisfactory performance of ELEC.<<ETX>>

Learning control system design for a parallel link robot manipulator

A method of iterative learning control system design based on the two-dimensional (2D) theory is applied to the trajectory tracking control problem for the parallel link robot manipulator designed at the National Institute of Standards and Technology (NIST). The 2D model for the iterative learning control system which reveals the connections between learning control systems and 2D system theory, is established. A novel learning control algorithm whose convergence is guaranteed by 2D stability is proposed and applied successfully to the parallel-link robot manipulator. The excellent performance of the learning algorithm is demonstrated by computer simulation results.<<ETX>>