A FORMULATION OF NONLINEAR MODEL PREDICTIVE CONTROL USING AUTOMATIC DIFFERENTIATION

Abstract The computational burden, which obstacles Nonlinear Model Predictive Control techniques to be widely adopted, is mainly associated with the requirement to solve a set of nonlinear differentiation equations and a nonlinear dynamic optimisation problem in real-time online. In this work, an efficient algorithm has been developed to alleviate the computational burden. The new approach uses the automatic differentiation techniques to solve the set of nonlinear differentiation equations, at the same time to produce the differential sensitivity of the solution against input variables. Using the differential sensitivity, the gradient of the cost function against control moves is accurately obtained so that the online nonlinear dynamic optimisation can be efficiently solved. The new algorithm has been applied to an evaporation process with satisfactory results to cope with setpoint changes, unmeasured disturbances and process-model mismatches.

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