Controle reconfigurável de processos sujeitos a falhas em atuadores : uma abordagem baseada no MPC em duas camadas

Orientadores: Flávio Vasconcelos da Silva, Thiago Vaz da CostaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: Plantas industriais modernas estão suscetíveis a falhas em equipamentos de processo e em instrumentos e componentes da malha de controle. Tais eventos anormais podem acarretar danos a equipamentos, degradação do desempenho do processo e até cenários extremos como a parada da planta e acidentes graves. Em vista disso, o emprego de sistemas de controle tolerante a falhas visa a elevar o grau de confiabilidade e segurança do processo por meio do tratamento e mitigação de eventos anormais, evitando que evoluam para situações críticas. Nesse sentido, este trabalho tem como objetivo desenvolver uma técnica de controle reconfigurável tolerante a falhas para processos sujeitos a falhas em atuadores. A presente proposta é baseada em abordagens por atuadores virtuais e ocultação da falha. Essas técnicas consistem no recálculo das ações de controle e na ocultação da falha do ponto de vista do controlador nominal, permitindo que o mesmo seja mantido após a reconfiguração da malha de controle. Na presente proposta, o atuador virtual é baseado na estrutura do controlador preditivo em duas camadas. Uma camada consiste no cálculo de referências para as variáveis de entrada e para o desvio previsto entre o comportamento da planta nominal e com falha. A outra camada, por sua vez, é responsável por conduzir as variáveis de processo para as referências calculadas na etapa anterior. Ambas as camadas são baseadas em problemas de programação quadrática e levam em consideração as restrições do processo, como limites de atuadores e desvios permissíveis em relação ao comportamento nominal da planta. Essa técnica possibilita a consideração de cenários de falhas nos quais não há graus de liberdade suficientes para a manutenção de variáveis controladas em valores desejados. Assim, a estimativa de perturbações permite que novas referências atingíveis sejam calculadas, ainda que haja erros de identificação do modelo pós-falha do processo. Por fim, a estrutura de controle proposta foi aplicada em simulações utilizando um processo de tanques quádruplos, bem como em experimentos conduzidos em uma planta de neutralização de pHAbstract: Modern industrial plants are susceptible to faults in process equipment and in instruments and components of the control loop. Such abnormal events can lead to equipment damage, degradation of process performance and even extreme scenarios such as plant shutdown and serious accidents. Thus, the use of fault-tolerant control systems aims to increase process reliability and safety by treating and mitigating abnormal events, preventing them from evolving to critical situations. In this sense, this work aims to develop a reconfigurable fault tolerant control technique for processes subject to actuator faults. The present proposal is based on the virtual actuator and fault hiding approaches. These techniques consist of recomputing control actions and hiding the fault from the nominal controller perspective, allowing it to be maintained after the control loop reconfiguration. We propose a virtual actuator based on the two-layer model predictive control structure. One layer consists of calculating references for input variables and for the predicted deviation between the nominal and faulty plant behaviors. The other layer, in turn, is responsible for driving process variables to the references calculated in the previous step. Both layers are based on quadratic programming problems and take into account process constraints such as actuator limits and permissible deviations from the nominal plant behavior. This technique allows the consideration of fault scenarios in which there are not enough degrees of freedom for the maintenance of controlled variables in desired values. Thus, disturbance estimation allows the calculation of new achievable references, even though there are identification errors in the post-fault model. Finally, the proposed control structure has been applied to an experimental pH neutralization plant. Finally, the proposed control structure was applied in simulations to a quadruple-tank process as well as in experiments conducted in a pH neutralization plantMestradoSistemas de Processos Quimicos e InformaticaMestre em Engenharia Química130952/2015-0CNP

Cooperative distributed MPC for tracking

This paper proposes a cooperative distributed linear model predictive control (MPC) strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in the case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady-State Target Optimizer (SSTO). The controller is applied to a real four-tank plant

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.Comment: 8 pages, 4 figures

Analysis and design of model predictive control frameworks for dynamic operation -- An overview

This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus on the particular challenges that arise when dealing with such more general control goals and present methods that have emerged in the literature to address these issues. The goal of this article is to present an overview of the state-of-the-art techniques, providing a diverse toolkit to apply and further develop MPC formulations that can handle the challenges intrinsic to dynamic operation. We also critically assess the applicability of the different research directions, discussing limitations and opportunities for further researc

Modern approaches to control of a multiple hearth furnace in kaolin production

The aim of this thesis is to improve the overall efficiency of the multiple hearth furnace (MHF) in kaolin calcination by developing control strategies which incorporate machine learning based soft sensors to estimate mineralogy related constraints in the control strategy. The objective of the control strategy is to maximize the capacity of the furnace and minimize energy consumption while maintaining the product quality of the calcined kaolin. First, the description of the process of interest is given, highlighting the control strategy currently implemented at the calciner studied in this work. Next, the state of the art on control of calcination furnaces is presented and discussed. Then, the description of the mechanistic model of the MHF, which plays a key role in the testing environment, is provided and an analysis of the MHF dynamic behavior based on the industrial and simulated data is presented. The design of the mineralogy-driven control strategy for the multiple hearth furnace and its implementation in the simulation environment are also outlined. The analysis of the results is then presented. Furthermore, the extensive sampling campaign for testing the soft sensors and the control strategy logic of the industrial MHF is reported, and the results are analyzed and discussed. Finally, an introduction to Model Predictive Control (MPC) is presented, the design of the Linear MPC framework for the MHF in kaolin calcination is described and discussed, and future research is outlined

Model Predictive Control of a Continuous Vacuum Crystalliser in an Industrial Environment: A Feasibility Study

Crystallisers are essentially multivariable systems with high interaction amongst the process variables. Model Predictive Controllers (MPC) can handle such highly interacting multivariable systems efficiently due to their coordinated approach. In the absence of a real continuous crystalliser, a detailed momentum-model was applied using the process simulator in Simulink. This process has been controlled by a model predictive controller widely used in industry. A new framework has been worked out for the incorporation of the Honeywell Profit Suite controller to the simulator of the crystalliser. The engineering model and the controller were connected via OPC (OLE-Object Linking and Embedding for Process Control standard). Models were developed in Profit Suite using the new fully-automated identification method. The feasibility study illustrated that the applied identification tool gave an accurate and robust model, and that the non-linear crystalliser may be controlled and optimised very well with the Honeywell Profit Suite package. The developed system is proven to be useful in research and development