On-line system identification in real time using a minicomputer

The parameters of the dynamic model of a linear system are estimated from the measurements of the input and output data by using the recursive algorithm for matrix pseudoinverse. For measurements contaminated with noise, the stochastic approximation algorithm is employed in conjunction with the matrix pseudoinverse algorithm for obtaining unbiased least-squares estimates. Both the methods are tested on-line in real time using the PDP 11/45 minicomputer while the system is simulated on a TR-20 analogue computer. The results confirm the feasibility of using the algorithms to identify the parameters of a class of industrial processes on-line using a minicomputer. This has special application to the adaptive control of such processes, based on rapid identification of slowly-varying parameters.