ON-LINE IDENTIFICATION AND CONTROL ALGORITHMS FOR LINEAR AND NONLINEAR SYSTEMS USING SVD'S ON SYSTOLIC ARRAYS

IN THE PROPOSED STUDY, A NEW CLASS OF ALGORITHMS BASED UPON A GENERALIZED SINGULAR VALUE DECOMPOSITION (SVD) IS CONSIDERED FOR SYSTEM IDENTIFICATION, STATISTICAL MODEL ORDER DETERMINATION, MODEL ORDER REDUCTION, AND PREDICTIVE CONTROL. THE EXTENSION OF THESE ALGORITHMS TO NON-LINEAR POLYNOMIAL SYSTEMS FOR APPROXIMATING ARBITRARY NONLINEAR SYSTEMS IS PROPOSED. CURRENTLY AVAILABLE ALGORITHMS FOR SYSTEM IDENTIFICATION AND CONTROL ARE NOT COMPLETELY RELIABLE FOR AUTOMATIC IMPLEMENTATION ON MICROPROCESSORS IN REAL TIME. IN THE GENERALIZED SVD APPROACH, THE ALGORITHMS ARE COMPUTATIONALLY STABLE AND NUMERICALLY ACCURATE AND CAN BE IMPLEMENTED ON SYSTOLIC ARRAY PROCESSORS USING RECENTLY DEVELOPED ALGORITHMS RESULTING IN A CONSIDERABLE SPEEDUP. THE PROPOSED RESEARCH APPROACH IS BASED UPON A RECENT GENERALIZED CANONICAL BARIATE ANALYSIS (CBA) METHOD FOR DETERMINING THE OPTIMAL STATE OF A RESTRICTED ORDER IN A SYSTEM IDENTIFICATION, REDUCED ORDER STOCHASTIC FILTERING, AND MODEL PREDICTIVE CONTROL. THIS PERMITS A UNIFIED APPROACH TO THE SOLUTION OF THESES PROBLEMS FROM THE VIEWPOINTS OF PREDICTION AND AS AN APPROXIMATION PROBLEM. THE RESULTS GENERALIZE REALDISY TO NONLINEAR SYSTEMS USING POLYNOMIAL APPOXIMATIONS. THE EXPECTED RESULTS FROM THE PHASE I RESEARCH ARE THE DETERMINATION OF DETAILED METHODS AND ALGORITHMS TO DEMONSTRATE THE FEASIBILITY OF ONLINE COMPUTATION FOR IDENTIFICATION, FILTERING, AND CONTROL FOR HIGH ORDER LINEAR MULTIVARIABLE SYSTEMS AND POLYNOMIAL NONLINEAR SYSTEMS. IN PHASE II, SOFTWARE WILL BE DEVELOPED FOR IMPLEMENTING THESE ALGORITHMS ON PARALLEL PROCESSORS AND HIGH SPEED SERIAL PROCESSORS.