Control Scheme For Robotic Systems Research Articles

Designing a Robust Control Scheme for Robotic Systems with an Adaptive Observer

This paper introduces a robust task-space control scheme for a robotic system with an adaptive observer. The proposed approach does not require the availability of the system states and an adaptive observer is developed to estimate the state variables. These estimated states are then used in the control scheme. First, the dynamic model of a robot is derived. Next, an observer-based robust control scheme is designed to compensate the uncertainties occurring in the control system. Moreover, upper bound of the lumped uncertainty is essential in the design of the robust controller. However, the upper bound of the lumped uncertainty is difficult to obtain in practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty, and an adaptive observer-based robust task-space controller is obtained. In this paper, we proved that the proposed adaptive observer-based controller can guarantee that the task-space tracking error and also the observation error converge to zero. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.

Neural network-based tracking control for robotic systems using only position feedback

An adaptive neural network-based position feedback tracking control scheme for robotic systems involving plant uncertainties and external disturbances is proposed. The developed controller is based on a neural network system and a linear reduced-order observer. The resulting closed-loop system guarantees a transient and asymptotic performance, in the sense that the tracking error locally converges to a small region around zero in terms of L∞ bound and H∞ performance. The implementation of the neural network basis functions depends only on the desired reference information. Only position measurements are required for feedback, and the developed controller is driven by the position tracking error. Consequently, the adaptive neural network-based controller developed possesses the properties of computational simplicity and easy implementation. Finally, a simulation example is provided to illustrate the tracking performance of a two-link robotic manipulator.

Learning control in robotic systems

The concept of learning and training machines, and some early methodologies were introduced about two decades ago. Robotic systems, undoubtedly, can be developed to a more advanced and intelligent stage. The realization of the learning capability, analogous to the human learning and thinking process, is a desired primary function. A betterment process has been investigated in the literature for applications of learning control to robotic systems. The existing schemes are a type of one-step learning process. A neighboring (2m+1)-step learning control scheme for robotic systems is presented in this paper. For each process, a betterment algorithm which chooses a generalized momentum as an output function, is executed. Also, it is associated with a conceptual learning process by adding a self-teaching knowledge base for speeding up the convergence, so that the learning capability of the resulting robotic systems can be enhanced.