Composite Learning Control With Application to Inverted Pendulums

Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC has been verified by the application to an inverted pendulum control problem.Comment: 5 pages, 6 figures, conference submissio

Technology and innovation management : analysis of the New Zealand plastics industry : technology status, problems and opportunities : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Production Technology at Massey University

The aim of this thesis is to evaluate the status of innovation and technology management in the New Zealand plastics industry using a survey. This thesis provides a comparison of plastics manufacturing capability in New Zealand and in other industrial countries; it also provides an examination of key trends, attitudes and problems within the New Zealand plastics industry to indicate where technology transfer from overseas, or research and development may be required