This paper reviews the theoretical development of automatic control strategies for arterial blood pressure regulation. The literature is classified by control strategy. Proportional-integral-derivative controllers, optimal controllers, adaptive controllers, and rule-based controllers are the most commonly encountered strategies in the literature. A brief description of each control scheme is given, followed by examples of each from the literature. Validation methods for the control performance vary from computer simulations to clinical tests on human patients. A number of reports of clinical success support the feasibility of advanced control systems in this problem. Issues on control systems in the clinical environment are also discussed.
This paper discusses the use of evolutionary programming (EP) for computer-aided design and testing of neural controllers applied to problems in which the system to be controlled is highly uncertain. Examples include closed-loop control of drug infusion and integrated control of HVAC/lighting/utility systems in large multi-use buildings. The method is described in detail and applied to a modified Cerebellar Model Arithmetic Computer (CMAC) neural network regulator for systems with unknown time delays. The design and testing problem is viewed as a game, in that the controller is chosen with a minimax criterion i.e., minimize the loss associated with its use on the worst possible plant. The technique permits analysis of neural strategies against a set of feasible plants. This yields both the best choice of control parameters and identification of that plant which is most difficult for the best controller to handle.
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