Abstract

In practice, high quality control for mechatronic systems is often achieved by augmenting classical control architectures like PID controllers with numerous tailored nonlinear characteristic parameter curves and cascades. This complexity can be significantly reduced by utilizing advanced model predictive controllers (MPC). Furthermore, desired objectives like minimum control error and effort can be realized while explicitly adhering to state and control constraints. However, MPC is subject to iterative gradient-based online optimization algorithms which are computationally expensive. Hence, their application to mechatronic systems with fast dynamics is limited. It is worth mentioning that industrial systems often utilize low cost computational hardware. Accordingly, this contribution presents a model predictive trajectory set control (MPTSC) scheme that mimics a sub-optimal MPC by a rough discretization of the control input domain. A comparative analysis with a linear quadratic regulator demonstrates its ability to provide a sufficiently high control performance compared to the optimal reference. Furthermore, the approach is experimentally evaluated on a proportional directional control valve with a sample rate of 10 kHz. In addition to its efficiency the implementation of MPTSC is less complex and error-prone in comparison to MPC which is a reasonable advantage especially in industrial applications.

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