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This paper presents a new approach to automatic code generation of advanced control algorithms, primarily model predictive control schemes, for microcontroller-based embedded systems. The main part of the developed tools, the transcompiler, makes it possible to effectively translate the algorithms described in a high-level language (MATLAB) into C language code for the chosen hardware platform. Implementation...
This paper is concerned with a Model Predictive Control (MPC) algorithm for dynamic systems described by nonlinear state-space models. A unique feature of the algorithm is the fact that the current value of the manipulated variable (i.e. the decision variable of MPC) is not calculated from an optimisation problem, but from an analytical linear control law. The coefficients of the control law, due...
This paper describes a Model Predictive Control (MPC) algorithm in which a Radial Basis Function (RBF) neural network is used as a dynamic model of the controlled process and it reports training and selection of the RBF model of the benchmark system for MPC. In order to obtain a computationally uncomplicated control scheme, the RBF model is successively linearised on-line, which leads to an easy to...
This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive Control (MPC) algorithm in which such a network is used for prediction. The Jordan network is a simple recurrent neural structure in which only one value of the process input signal (from the previous sampling instant) and only one value of the delayed output signal...
This paper is concerned with a computationally efficient suboptimal nonlinear predictive control algorithm. The nonlinear model of the plant is used to obtain a local linearisation and to calculate, by means of an iterative procedure, the nonlinear response and future control moves. In comparison with fully-fledged nonlinear algorithms, which hinge on non-convex optimisation, the presented approach...
The paper studies the dependability of software implementation of the numerical Generalized Predictive Control (GPC) Model Predictive Control (MPC) algorithm. The algorithm is implemented for a control system of a multivariable chemical reactor - a process with strong cross-couplings. Fault sensitivity of the proposed implementations is verified in experiments with a software implemented fault injector...
This paper describes a nonlinear Model Predictive Control (MPC) algorithm with on-line optimal linearisation. Unlike the classical MPC algorithms with successive model linearisation at the current operating point of the process (using the Taylor's series expansion), the best possible linear approximation of the nonlinear predicted output trajectory is repeatedly found in the discussed approach. The...
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