Model predictive control and stabilisation of interconnected systems

University of Technology, Sydney. Faculty of Engineering and Information Technology.The attraction of having higher efficiency and quality, as well as increasing reliability and flexibility for industrial plants and network systems has created opportunities for new research in the control and optimisation fields. Among various design methods, model predictive control (MPC) strategies have proved to be effective in industrial applications. Whilst found widespread used with stand-alone controllers in the refining and many other industries, the field of orchestrating non-centralised MPCs and distributed MPCs is evaluated as still in its infancy. The work in this thesis is concerned with stabilising methods for the control of complex interconnected systems with mixed connection configurations employing distributed and decentralised model predictive control schemes. Inheriting the advantage of the MPC strategy, the control and state constraints are naturally dealt with by the employed methods. As a result, the novel concept of asymptotically positive realness constraint (APRC) and the segregation and integration constructive methods for the constrained stabilisation of interconnected systems are introduced and developed. The MPC is formulated with state space models and stabilising constraints within the open-loop paradigm in this thesis. By having the control inputs entirely decoupled between subsystems and no additional constraints imposed on the interactive variables rather than the coupling constraint itself, the proposed approaches outreach various types of systems and applications. For parallel connections that emulate parallel redundant structures and have unknown splitting ratios, a fully decentralised control strategy is developed as an alternative to the hybrid approaches. For the semi-automatic control systems, which is involved with both closed-loop and humanin- the-loop regulatory controls, the stability-guaranteed method of decentralised stabilising agents which are interoperable with different control algorithms is germinated and implemented for each single subsystem

Model predictive control of DFIG-based wind turbine

University of Technology, Sydney. Faculty of Engineering and Information Technology.Renewable energy as a green source of energy is clean, accessible and sustainable. Due to advanced control, lower cost and government incentives, wind energy has been the largest growth among other renewable sources. With fast growing in the new generation of generators, Doubly Fed Induction Generators (DFIGs) became more popular because of handling a fraction (20-30%) of the total system power which leads to reduce the losses in the power electronic equipment and also their ability in decoupling the control of both active and reactive power. In addition, DFIGs have better behaviour in system stability. Therefore, in this study, the model of one-mass wind turbine with DFIG is represented by a third order model. Model Predictive Control (MPC), as a powerful control method to handle multivariable systems and incorporate constraints, is applied in order to compensate inaccuracies and measurement noise. The optimization problem is recast as a Quadratic Programming (QP) which is highly robust and efficient. Multi-step optimization is introduced to bring the unhealthy voltages as close as possible to the normal operating points so that leads to minimize the changes of the control variables. In order to regulate the power flow between the grid and the generator, it is essential to update reactive power with real power and actual terminal voltage besides reaching maximum reactive power. In this study, the updated control input applies feedback to MPC at each control step by solving a new optimization problem

Advances in PID Control

Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications