Nonlinear model predictive control strategy based on soft computing approaches and real time implementation on a coupled-tank system

In order to effectively implement a good model based control strategy, the combination of different linear models working at various operating regions are mostly utilised since a single model that can operate in that fashion is always a difficult task to develop. This work presents the use of soft computing approaches such as evolutional algorithm called simulated annealing (SA), a genetic algorithm (GA) and an artificial neural network (ANN) to design both a robust single nonlinear dynamic ANN model derived from an experimental data driven system identification approach and a nonlinear model predictive control (NMPC) strategy. SA is employed to give an initial weight for the training of the ANN model structure while a gradient descent based Levenberg–Marquardt Algorithm (LMA) approach is used to optimise the ANN weights. The designed NMPC strategy is optimised using a stochastic GA optimisation method and is tested first in simulation and then implemented in real time practical experiment on a highly nonlinear single input single output (SISO) coupled tank system (CTS). An excellent control performance is reported over the conventional proportional-integral-derivative (PID) controller and results show the effectiveness of the approach under disturbances. The nonlinear neural network model proved very reliable in different operating regions. The SISO system can be upgraded to multi-input multi-output (MIMO) system while the whole NMPC approach can easily be adapted to other industrial processes

Improving Tracking in Optimal Model Predictive Control

The thesis deals with the improvement in the tracking in model predictive control(MPC). The main motivation is to explore high embedding performance controllers with constraint handling capabilities in a simple fashion. There are several techniques available for effectively using an infinite horizon rather than a finite horizon. First, there has been relatively little discussion so far on how to make effective use of advance information on target changes in the predictive control literature. While earlier work has indicated that the default solutions from conventional algorithms are often poor, very few alternatives have been proposed. This thesis demonstrates the impact of future information about target changes on performance, and proposes a pragmatic method for identifying the amount of future information on the target that can be utilised effectively in infinite horizon algorithms. Numerical illustrations in MATLAB demonstrate that the proposal is both systematic and beneficial. This thesis introduces several important issues related to model predictive control (MPC)tracking that have been hitherto neglected in the literature, by first deriving a control law for future information about target changes within optimal predictive control (OMPC) for both nominal and constraints cases. This thesis proposes a pragmatic design for scenarios in which the target is unreachable. In order to deal with an unreachable target, the proposed design allows an artificial target into the MPC optimisation problem. Numerical illustrations in MATLAB provide evidence of the efficacy of the proposals. This thesis extends efficient, robust model predictive control (MPC) approaches for Linear Parameter-Varying (LPV) systems to tracking scenarios. A dual-mode approach is used and future information about target changes is included in the optimisation tracking problem. The controller guarantees recursive feasibility by adding an artificial target as an extra degree of freedom. Convergence to admissible targets is ensured by constructing a robustly invariant set to track any admissible target. The efficacy of the proposed algorithm is demonstrated by MATLAB simulation. The thesis considers the tractability of parametric solvers for predictive control based optimisations, when future target information is incorporated. It is shown that the inclusion of future target information can significantly increase the implied parametric dimension to an extent that is undesirable and likely to lead to intractable problems. The thesis then proposes some alternative methods for incorporating the desired target information, while minimising the implied growth in the parametric dimensions, at some possibly small cost to optimality. Feasibility is an important issue in predictive control, but the influence of many important parameters such as the desired steady-state, the target and the current value of the input is rarely discussed in the literature. At this point, the thesis makes two contributions. First, it gives visibility to the issue that including the core parameters, such as the target and the current input, vastly increases the dimension of the parametric space, with possible consequences on the complexity of any parametric solutions. Secondly, it is shown that a simple re-parametrization of the degrees of freedom to take advantage of allowing steady-state offset can lead to large increases in the feasible volumes, with no increases in the dimension of the required optimisation variables. Simulation with MAT LAB 2017a provides the evidence of the efficacy of all proposals

Desarrollo de un sistema de control en tiempo real para PC-104

La ingeniería de sistemas se ha visto muy favorecida en los últimos años gracias a los pasos agigantados a los que han evolucionado las tecnologías eléctricas y eléctronicas, además de la informática. La automatización y el control de sistemas ven cada vez menos límites en cuanto a la resolución de procesos complejos, la necesidad de potencia de cálculo o incluso la accesibilidad al hardware necesario. Pero para que exista este avance se necesita un aspecto esencial: la síntesis de la tecnología y los conocimientos que permite a los nuevos ingenieros adquirir rápidamente el estado actual para seguir mejorando poco a poco el futuro. En este proyecto se va a realizar una síntesis de la programación de controladores con la informática industrial de tiempo real. Generalmente los microprocesadores electrónicos en los que se programa la ejecución de controladores no disponen de precisión o de garantía suficiente para sistemas de tiempo crítico debido a la incertidumbre que ocasiona el sistema operativo y otros programas o servicios que se realizan en segundo plano. Las funcionalidades de tiempo real ofrecen solución a este tipo de sistemas que necesitan un control muy estricto, preciso y ligado al tiempo. El logro fundamental de este proyecto consiste en la realización de una librería informática en C++ que facilita un gran número de funciones y propone una estructura de programación sencilla e intuitiva para que el futuro ingeniero de control realice controladores en tiempo real sin que deba conocer ni estudiar la compleja programación informática interna. Esta librería se ha programado en un PC-104, que es una computadora o sistema empotrado recomendado para trabajar en tiempo real. Además, tanto el sistema operativo Ubuntu como todas las aplicaciones en las que se ha apoyado el desarrollo del proyecto son software libre para maximizar la accesibilidad. También se han realizado ejemplos y programas sencillos que tienen como objetivo facilitar el entendimiento y uso de la librería a sus futuros usuarios. Por último, se han implementado controladores predictivos más complejos que integran la librería del proyecto con la herramienta matemática y de control ACADO Toolkit con el objetivo final de demostrar la utilidad del tiempo real en sistemas mayores