학술논문

Convergence properties of the modified renormalization algorithm based adaptive control supported by ancillary methods
Document Type
Conference
Source
Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02. Robot motion and control Robot Motion and Control, 2002. RoMoCo '02. Proceedings of the Third International Workshop on. :51-56 2002
Subject
Robotics and Control Systems
Computing and Processing
Signal Processing and Analysis
Components, Circuits, Devices and Systems
Convergence
Adaptive control
Fuzzy systems
Neural networks
Machine learning
Cellular neural networks
Neurons
Cybernetics
Costs
Computer applications
Language
Abstract
A new branch of computational cybernetics seems to emerge on the principles akin to that of the traditional soft computing (SC). In the present paper the essential differences between the conventional and the novel approach are summarized. At the cost of the use of a simple dynamic model, a priori known, uniform, lucid, structure of reduced size, machine learning by a simple and short explicit algebraic procedure especially fit to real time applications considerable computational advantages can be achieved. The key element of the approach is the modified renormalization transformation supported by the application of a simple linear transformation, and the use of a simple prediction technique. It analyzes how the satisfactory conditions of the "complete stability" can be guaranteed, and the convergence properties can be improved by the ancillary methods. Simulation examples are presented for the control of a 3 DOF SCARA arm by the use of partially stretched orthogonal transformations.