Title :
Tracking control of polynomic nonlinear systems
Author_Institution :
Australian Centre for Field Robotics, Sydney Univ., NSW, Australia
Abstract :
A computationally efficient tracking controller for polynomic nonlinear systems by a combined Volterra series and an extended Kalman filtering approach is presented. The developed state estimation approach is applied for the state estimation of the states of a pendulum model and the tracking control of the pendulum is accomplished by utilizing these estimates along with the internal model principle and the feedback linearization method. It is demonstrated that this approach is more computationally efficient as compared to the standard extended Kalman filtering approach and thus also leads to a computationally efficient control algorithm
Keywords :
Kalman filters; Volterra series; control system synthesis; feedback; linearisation techniques; nonlinear dynamical systems; nonlinear filters; pendulums; polynomials; recursive filters; state estimation; Volterra series; computationally efficient control algorithm; computationally efficient tracking controller; extended Kalman filtering; feedback linearization method; internal model principle; pendulum model; polynomic nonlinear systems; state estimation; tracking control; Australia; Control systems; Filtering; Kalman filters; Linear systems; Noise measurement; Nonlinear control systems; Nonlinear systems; State estimation; State feedback;
Conference_Titel :
TENCON '98. 1998 IEEE Region 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control
Conference_Location :
New Delhi
Print_ISBN :
0-7803-4886-9
DOI :
10.1109/TENCON.1998.797112