Gradient-based nonlinear model predictive control with constraint transformation for fast dynamical systems
FacultiesFakultät für Ingenieurwissenschaften, Informatik und Psychologie
InstitutionsInstitut für Mess-, Regel- und Mikrotechnik
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Model predictive control (MPC) is a modern control methodology that is based on the repetitive solution of an optimal control problem (OCP) at fixed time instances. The determined state of the system at the current sampling instance is used as initial value for the OCP and the computed control action is then injected to the plant. This procedure is repeated in the next sampling step where the OCP is resolved with the new state of the system. A model predictive controller provides a number of benefits compared to classic control methods. The desired control objective is formulated in a cost function while constraints are directly taken into account. Additionally, an MPC allows to control nonlinear and multivariable systems. However, the numerical solution of an optimal control problem requires in general a significant computational effort and hence limits the application of a model predictive controller. This is even more severe if an MPC is used to control fast dynamical systems with low sampling times. To this end, efficient algorithms, powerful hardware platforms or a combination of both have to be used to circumvent this difficulty. This thesis discusses an MPC scheme that is well-suited for controlling fast nonlinear dynamical systems in real-time. This goal is achieved by combining the efficient gradient method with a transformation technique to handle a particular class of constraints in a systematic way allowing to reformulate a constrained optimal control problem into an unconstrained counterpart to reduce the numerical burden. The related systematic and algorithmic conditions and properties are discussed in detail together with convergence and stability results. The performance of the approach is demonstrated in simulation and experimental studies.
Subject HeadingsPrädiktive Regelung [GND]
Nichtlineare Regelung [GND]
Model predictive control [LCSH]