Experimental assessment and design of multiple model predictive control based on local model networks for industrial processes
This paper presents the experimental assessment of a class of multiple model predictive controllers based on linear local model networks. The control design is established on a clear and easily understandable structure where local models are used to describe the nonlinear process in several different operating points. Thus, simplifying the model predictive control (MPC) algorithm by eliminating the need of a nonlinear optimization strategy, reducing it to a well-grounded quadratic programming dynamic optimization problem. The investigated methodology was tested in an experimental neutralization pilot plant instrumented with foundation fieldbus devices. The steps of models selection, experimental system identification and proper model validation were addressed. In addition, an open-source system used for control calculation was presented. Results regarding the control problem showed that the MPC based on local relevant models was capable of smooth setpoint tracking despite system nonlinearities and a reduced demand of the final control element was attained. It was shown that the technique is easily implementable and can be used to achieve improvements in the control of nonlinear processes at the cost of little modification to the linear MPC algorithm.
model predictive control process control nonlinear identification neutralization process