Multi-objectives model predictive control of multivariable systems

In this thesis, MOO [Multi-Objective Optimization] design for Model Predictive Control (MPC) and Proportional Integral (PI) control are investigated for a multivariable process

Tuning strategy for model predictive control through the use of sensitivity functions

Orientador: Luz Adriana Alvarez ToroDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: Nesse trabalho, uma metodologia prática de sintonia para controladores preditivos (MPC) é introduzida, sendo que nessa os parâmetros de sintonia são obtidos com o uso de funções de sensibilidade. A metodologia atua na função custo do controlador preditivo, sintonizando os parâmetros de priorização dessa relacionados às ações de minimização do erro e de redução do esforço de controle. Embora a metodologia apresente aplicação universal, o controlador utilizado nesse projeto foi do tipo de predição generalizada (GPC), que possui uma presença considerável na indústria em relação a outros. Uma vez que as funções de sensibilidade representam relações chaves na malha de controle, propõem-se sua utilização na forma de índices, capazes de mensurar individualmente a capacidade da metodologia em minimizar o erro, reduzir o esforço ou rejeitar os distúrbios, para a determinação dos parâmetros do sistema. O problema de otimização da sintonia promove a comparação dos índices com valores de referência adequadas para cada ação promovida pelo controlador. Cada ação é acompanhada por uma variável de penalização definida pelo usuário, sendo que a metodologia é balanceada de forma que a interação com essa seja intuitiva. O método é aplicado em sistemas de uma entrada e uma saída (SISO) e de múltiplas entradas e múltiplas saídas (MIMO), sendo que, nesse segundo caso, o conceito de decomposição em valores singulares (SVD) é aplicado para determinar as energias do sistema e promover a sintonia de acordo com o cenário. O método é testado em simulações de problemas típicos da engenharia química, como no controle de um tanque e de um reator CSTR encamisado. Foi observado que a metodologia atuou da maneira esperada, promovendo a minimização do erro, esforço e impacto do distúrbio no sistema de maneira facilmente determinada pelo usuário. Os resultados comprovaram o potencial da metodologia em obter valores adequados para os parâmetros de sintonia ao mesmo tempo que é capaz de ser aplicado de maneira intuitiva e customizada, garantindo a eficiência, praticidade e versatilidade comumente desejada para este tipo de ferramenta matemáticaAbstract: In this work, a practical tuning methodology for model predictive controllers (MPC) is introduced, in which the control parameters are determined using sensitivity functions. The methodology is focused on the predictive controller¿s cost function, tuning its weighting coefficients related to both error minimization and effort reduction actions. Although the methodology can be applied universally, the controller applied in this project was the generalized predictive controller (GPC), which possesses a considerable presence in the industry, relative to other types. The sensitivity functions represent key relations of the control system. Therefore, we propose their application in the form of indexes capable of individually measuring the methodology capability in minimizing the error, reducing the control effort and rejecting the disturbance, in describing the optimal control parameters. The tuning problem promotes the indexes comparison with reference values adequate for each action promoted by the controller. Each action comes with a numerical weight defined by the user, and the system is balanced, focusing on making the controller¿s usage intuitive. The method is applied both in single-input and single-output (SISO) as well as multiple-inputs multiple-outputs (MIMO) systems, being that, in this second occasion, the concept of singular value decomposition (SVD) is applied to acquire the system energies and promote the tuning according to the scenario. The method is tested through simulations in typical chemical engineering problems, such as the water level in a tank and a jacketed CSTR reactor control. It was observed that the methodology acted as expected, promoting the minimization of error, effort, and disturbance¿s impact in the system in a manner that can be easily determined by the user. The results verified the methodology¿s potential in obtaining desired values for the tuning parameters at the same time it was able to be applied in an intuitive and customizable manner, ensuring the efficiency, practicality and versatility commonly desired for this type of mathematical toolMestradoEngenharia QuímicaMestre em Engenharia Química33003017034P-8CAPE

Genetic design of multivariable control systems

In the real world there are three types of multivariable control systems. The first one is when the number of inputs is equal to the number of the outputs, this type of multivariable control system is defined as a squared multivariable control system and the main type of controller designed is a decoupling controller which minimizes interactions and gives good set-point tracking. The second type of multivariable control system is where the number of inputs is greater than the number of the outputs, for this type of system the main controller designed is a fail-safe controller. This controller remains stable if a sub-set of actuator fail. The third type of multivariable control system is the number of outputs is greater than the number of inputs, for this type of system the main controller designed is an override control system. This controller only controls a sub-set of outputs based on a lowest wins control strategy. All the three types of multivariable control systems are included in this thesis. In this thesis the design of multivariable decoupling control, multivariable fail-safe control and multivariable override control as considered. The invention of evolutionary computing techniques has changed the design philosophy for control system design. Rather than using conventional techniques such as Nyquest plots or root-loci control systems can be designed using evolutionally algorithm. Such algorithms evolve solutions using cost functions and optimization. There are a variety of system performance indicators such as integral squared error operator has been used as cost functions to design controllers using such algorithms. The design of both fail-safe and override multivariable controllers is a difficult problem and there are very few analytical design methods for such controllers. Therefore, the main objective of this thesis is to use the genetic algorithms to involve both fail-safe and override controller multivariable controllers, such that they perform well in the time-domain

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