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In this paper an on-line tuning approach for adaptive GPC is proposed. The tuning parameters are calculated on-line using the updated process parameters and the tuning procedure is automatically completed at every interval so that it is suitable for the adaptive control of fast changing systems. As the approach is based on a pole-placement criterion, the control system behaves as a system with the...
Two least-squares-type UD factorization algorithms are proposed which update only part of the covariance matrix and parameter vector during each identification interval. This reduces the computational load per interval but still provides good parameter estimates for slowly varying systems. The algorithms are particularly well suited for applications where a large number of parameters must be estimated,...
This paper presents a modified version of the Generalized Predictive Controller (GPC) originally developed by Clarke et al (1987), which permits specification of the desired closed-loop poles as an alternative to ad hoc tuning approaches. The proposed controller does not require on-line matrix inversion even in the adaptive case and simulations show that it gives excellent performance.
The MIMO sliding mode controller (SMC) developed in this paper retains the robustness to parameter variations and disturbances which characterizes traditional SMC systems but is designed using Lyapunov's direct method As a result, it is not necessary 1) to transform the systems to phase canonical form; 2) to require that each sliding submanifold be stable with respect to the corresponding control...
An algorithm for estimating the model parameters and structure in a multivariable, input/output system is developed in this paper. It is based on the canonical, state-space representation by Guidorzi and an extension of Bierman's UD factorization algorithm. The algorithm is easy to interpret, straightforward to implement and also possesses excellent numerical properties.
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