Initial tuning of predictive controllers by reverse engineering

This paper demonstrates a method for finding the cost function and state observer to be used in model predictive control (MPC) so that when constraints are inactive a pre- existing low order controller is reproduced. The MPC controller thereby inherits its desirable properties. This can be used as a baseline for further tuning. The available degrees of design freedom are explored, in order to facilitate, as appropriate, exploitation of constraint-handling, offset-free and redundancy management capabilities of MPC.This work is supported by the EPSRC, the European Space Agency, and EADS Astrium

Engineering ligand-responsive RNA controllers in yeast through the assembly of RNase III tuning modules

The programming of cellular networks to achieve new biological functions depends on the development of genetic tools that link the presence of a molecular signal to gene-regulatory activity. Recently, a set of engineered RNA controllers was described that enabled predictable tuning of gene expression in the yeast Saccharomyces cerevisiae through directed cleavage of transcripts by an RNase III enzyme, Rnt1p. Here, we describe a strategy for building a new class of RNA sensing-actuation devices based on direct integration of RNA aptamers into a region of the Rnt1p hairpin that modulates Rnt1p cleavage rates. We demonstrate that ligand binding to the integrated aptamer domain is associated with a structural change sufficient to inhibit Rnt1p processing. Three tuning strategies based on the incorporation of different functional modules into the Rnt1p switch platform were demonstrated to optimize switch dynamics and ligand responsiveness. We further demonstrated that these tuning modules can be implemented combinatorially in a predictable manner to further improve the regulatory response properties of the switch. The modularity and tunability of the Rnt1p switch platform will allow for rapid optimization and tailoring of this gene control device, thus providing a useful tool for the design of complex genetic networks in yeast

On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RMinisterio de Economía y Competitividad DPI2013-482443-C2-1-

Investigation of Model Predictive Control (MPC) for Steam Generator Level Control in Nuclear Power Plants

The capabilities and potential of Model Predictive Control (MPC) based strategies for steam generator level (SGL) controls in nuclear power plants (NPPs) have been investigated. The performance has been evaluated for all operating conditions that also include start-ups, low power operations and load rejections. These evaluations have been done for MPC controllers based on existing advanced methodologies, as well as for any potential performance improvement that can be achieved by fine tuning some of the parameters (based on the characteristics of the SGL) of the existing MPC approaches. Two version of MPC have been designed and implemented. The Standard MPC (SMPC) has investigated the performance of existing advanced MPC methodologies. The Improved MPC (IMPC) has investigated potential performance improvement over SMPC by selecting appropriate values in the weight matrix of the objective function. Performance of MPC based approaches has been evaluated and compared with an optimized PI controller in term of i) set point tracking, ii) load-following, iii) transient responses, and iv) effectiveness subject to steam and feed water flow disturbances and feed water flow signal noise. The performance evaluation has been done through computer simulation, and also through simulation on a mock-up steam generator level system. The simulation results indicate strong potential for MPC based strategies, in particular for IMPC strategy, for effective control of the steam generator levels in nuclear power plants

Designing output-feedback predictive controllers by reverse-engineering existing LTI controllers

An approach to designing a constrained output-feedback predictive controller that has the same small-signal properties as a pre-existing output-feedback linear time invariant controller is proposed. Systematic guidelines are proposed to select an appropriate (non-unique) realization of the resulting state observer. A method is proposed to transform a class of offset-free reference tracking controllers into the combination of an observer, steady-state target calculator and predictive controller. The procedure is demonstrated with a numerical example.This work was supported by Engineering and Physical Sciences Research Council grant EP/G030308/1, the European Space Agency and EADS Astrium.This is the author's version of an article that has been published in IEEE Transactions on Automatic Control. Changes were made to this version by the publisher prior to publication. The final version of record is available at: http://dx.doi.org/10.1109/TAC.2013.2258781 (c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations