Model predictive control.
Variable horizon model predictive control: robustness and optimality
Process control. Dynamical systems.
Batina, I.. Eindhoven : Technische Universiteit Eindhoven, Batina, I , ' Model predictive control for stochastic systems by randomized algorithms ', Doctor of Philosophy, Department of Mathematics and Computer Science, Eindhoven.
DOI: PY - Y1 - N2 - The main topic of this thesis is control of dynamic systems that are subject to stochastic disturbances and constraints on the input and the state. U2 - Batina I. Eindhoven: Technische Universiteit Eindhoven, Available from, DOI: Access to Document Next, complexity reduction is considered, using a form of input parameterisation known as move blocking. After introducing a new notation for move blocking, algorithms are presented for designing a move-blocked VH-MPC controller. Constraints are tightened in a novel way for robustness, whilst ensuring that guarantees of recursive feasibility and finite-time completion are preserved.
Model predictive control for stochastic systems by randomized algorithms
Simulations are used to illustrate the effect of an example blocking scheme on computation time, closed-loop cost, control inputs and state trajectories. An optimisation problem is formulated to maximise the volume of an inner approximation to the region of attraction, parameterised in terms of the tightening policy. Alternative heuristic approaches are also proposed to deal with high state dimensions. Numerical examples show that the new technique produces substantially improved regions of attraction in comparison to other proposed approaches, and greatly reduces the maximum required prediction horizon length for a given application.
Finally, a case study is presented to illustrate the application of the new theory developed in this thesis to a non-trivial example system. A simplified nonlinear surface excavation machine and material model is developed for this purpose.
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The model is stabilised with an inner-loop controller, following which a VH-MPC controller for autonomous trajectory generation is designed using a discretised, linearised model of the stabilised system. Realistic simulated trajectories are obtained from applying the controller to the stabilised system and incorporating the ideas developed in this thesis. Keywords Model predictive control, Robust control, Variable horizon, Optimal control. Rights Attribution-NonCommercial 2.
Robust and Multi-Objective Model Predictive Control Design for Nonlinear Systems
In addition, fast responses during transients can be achieved by eliminating the modulator, i. This thesis presents a direct MPC algorithm for a three-phase two-level grid-connected voltage source converter VSC with an LCL filter that can operate the converter at a fixed switching frequency despite the absence of a modulator. Several refinements to the algorithm are presented which improve the performance of the system.
Moreover, the algorithm is extended to emulate the degree discontinuous PWM switching pattern. In steady-state operation, the method achieves similar total harmonic distortion THD levels to CB-PWM, and during transients faster responses are obtained due to the elimination of the modulation stage.
University : Tampereen teknillinen yliopisto - Tampere University of Technology.