Abstract

Some of the most difficult problems associated with process control are due to process nonlinearity, manipulated variable constraints, uncertain parameters and unmeasured variables. In this paper a nonlinear programming approach is developed to estimate process parameters, unmeasured state variables and process disturbances. A constrained optimization-based procedure is also used to maintain a desired output variable trajectory, similar to techniques that have proven successful for linear systems. The process model, characterized by a set of nonlinear differential equations, is transformed into algebraic equations using orthogonal collocation on finite elements. A system with inverse response characteristics and a bioreactor model are used as examples.

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