An adaptive blood pressure control system based on the long-range or finite-time-horizon prediction strategy known as generalized predictive control has been examined. The system was designed to run as a real-time, multitasking operating system providing real-time monitoring and control of mean arterial pressure. The ability to control in the presence of constraints was also incorporated into the system.Results from both unconstrained and constrained control runs were obtained from experiments on aneasthetized dogs. The problems caused by model-plant mismatch, varying time delays and nonlinearity were handled satisfactorily by the system. The disturbance rejection of the constrained algorithm is better than the unconstrained one. The results demonstrate the applicability of both the constrained and unconstrained generalized predictive control algorithm for regulation of mean arterial pressure.
SUMMARYThis paper deals with on-line identification and constrained long-range predictive control of multivariable systems. It extends a recently proposed augmented upper diagonal factorization identification (AUDI) algorithm to identify input-output models of multivariable systems with distinct time delays. The multiinput, multi-output (MIMO AUDI) algorithm can simultaneously identify the process model order and process parameters. The MIMO AUDI algorithm is implemented by decomposing a MIMO system into as many multi-input, single-output (MISO) subsystems as the number of outputs and then identifying each MISO subsystem separately. The performance of the new MIMO AUDI algorithm is demonstrated by application to input-output data from a real process. The extension of this algorithm by incorporating a variable forgetting factor with a lower bound in its value is implemented on real plant data to demonstrate 'alertness' of the estimator. This paper evaluates the performance of the MIMO adaptive generalized predictive control algorithm with and without constraints by experimental application on a computerinterfaced, pilot-scale process. The MIMO adaptive GPC is shown to have good regulatory plus servotracking properties.
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