Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions

Coordinating industrial production and cogeneration systems to exploit electricity price fluctuations

Las fluctuaciones en el precio de la electricidad, procedentes de la aplicación de programas de respuesta de la demanda, son una oportunidad para que las industrias que cuenten con sistemas de cogeneración puedan reducir sus costes de producción mientras hacen que la red eléctrica sea más estable y segura en su conjunto. Dada la cantidad de factores involucrados y la dificultad que esto supone a la hora de tomar decisiones, en esta tesis se presenta una metodología basada en optimización dinámica que permite la gestión óptima de ambos sistemas y se aplica en simulación al caso de estudio de una industria azucarera. Como principales resultados, se ha obtenido que utilizando la metodología propuesta los costes variables de producción se pueden reducir hasta un 2.55% si se utiliza una tarifa por tramos típica, y en torno a un 5.41% si se utilizan los precios dados por el mercado eléctrico directamente.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria