AUTONOMOUS CONTROL-SYSTEMS - MONITORING, DIAGNOSIS AND TUNING

A systematic and unified approach which accomplishes performance monit oring, performance improvement and fault prediction in control systems is proposed. The feature vector which is a vector formed of the coeff icients of the estimate of the sensitivity function and the influence matrix which is the Jacobian of the feature vector with respect to the physical parameter are shown to contain the relevant information to r ealize an autonomous control system. The feature vector is estimated u sing a robust, accurate and reliable Linear Predictive Coding Algorith m (LPCA). The influence matrix is computed by perturbing the physical parameters one at a time and estimating the feature vectors for each c ase. The proposed scheme is evaluated both on simulated as well as on actual control systems.