Fixed structure controllers are widely used, however the tuning thereof can be cumbersome and gives no guarantee of optimality, especially when the system is Linear Parameter-Varying (LPV). Iterative Feedback tuning (...
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Fixed structure controllers are widely used, however the tuning thereof can be cumbersome and gives no guarantee of optimality, especially when the system is Linear Parameter-Varying (LPV). Iterative Feedback tuning (IFT) is a technique for the optimisation of a parameterised controllerbased on closed-loop experiments. This paper extends the applicability of IFT to LPV systems for the case where the LPV scheduling parameters are measurable but cannot be controlled. The closed-loop LPV system matrices are factorised such that the effect of the scheduling parameter on the IFT gradient estimates can be compensated. A suffcient number of IFT experiments are performed to estimate the cost gradient and tune the parameters. The method is validated successfully via a simulation study for a special case with an LPV system. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
This paper discusses the application of the virtual reference tuning (VRT) techniques to tune neural controllers from batch input-output data, by particularising nonlinear VRT and suitably computing gradients backprop...
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This paper discusses the application of the virtual reference tuning (VRT) techniques to tune neural controllers from batch input-output data, by particularising nonlinear VRT and suitably computing gradients backpropagating in time. The flexibility of gradient computation with neural networks also allows alternative block diagrams with extra inputs to be considered. The neural approach to VRT in a closed-loop setup is compared to the linear VRFT one in a simulated crane example. (C) 2011 Elsevier Ltd. All rights reserved.
Optimal operation of batch processes usually involves closely following a pre-optimized batch trajectory, e.g., the temperature trajectory in an exothermic batch reactor. controllers for trajectory tracking have previ...
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Optimal operation of batch processes usually involves closely following a pre-optimized batch trajectory, e.g., the temperature trajectory in an exothermic batch reactor. controllers for trajectory tracking have previously been designed and tuned based on a physical or empirical plant model. In batch processes where it is difficult to build a sufficiently accurate model, it is attractive to tune a nonlinear parameterized controller directly on the plant data. provided the number of batch runs required for the iterative tuning remains acceptably low. In a recent work, the authors have proposed a tuning method that makes the best use of each plant run to rigorously calculate the correct gradient for the iterative tuning optimization. In this work, this method is applied to obtain a tuned neural-network controller for tracking the temperature trajectory in an exothermic batch reactor example taken from the literature. Results indicate the efficacy of the method for optimizing a neural controller without requiring an excessive number of batch runs for the trial-and-error iterations. (C) 2002 Eisevier Science Ltd. All rights reserved.
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