Introduction of Model Predictive Control for the System Optimization of a Proportional Directional Control Valve

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Abstract
During a model-based system optimization of a proportional directional control valve, the system performance is simulated for a wide range of different design parameter sets. The behavior of the model, which is used to evaluate the system performance, changes whenever the optimization algorithm provides new parameter values. The closed-loop control response of a proportional valve is used as the metric of the system performance. Thus, to avoid evaluating the robustness of the valve controller, a new controller design is required whenever a variation of the plant parameters is carried out. The native controller of a proportional directional control valve is a nonlinear PID controller with numerous coupled parameters. This controller design requires a complex evolutionary multi-objective parameter optimization. This contribution introduces the model predictive control (MPC) in the context of a fully automated model-based system optimization of a proportional directional control valve. Since the plant is known exactly for every design variation, the plant model becomes a part of the control concept. By updating the prediction model, the controller exhibits a design adaptive characteristic. Due to the inherent system knowledge of the MPC setup within the model-based system optimization, the number of free controller parameters decreases significantly. These additional free parameters, like the weights of the user-defined objective function, can be included easily into the design optimization vector enabling a single holistic system optimization procedure.

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