Iterative nonlinear model predictive control of a PH reactor. A comparative analysis

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the past runs make the control strategy capable not only of eliminating repeated errors but also of responding to new disturbances that occur during the run. The paper uses these ideas to devise an integrated controller that increases the capabilities of Nonlinear Model Predictive Control (NMPC) with batch-wise learning. This controller tries to improve existing strategies by the use of a nonlinear controller devised along the last-run trajectory as well as by the inclusion of filters. A comparison with a similar controller based upon a linear model is performed. Simulation results are presented in order to illustrate performance improvements that can be achieved by the new method over the conventional iterative controllers. Although the controller is designed for discrete-time systems, it can be applied to stable continuous plants after discretization

LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification

The goal of this paper is to analyze Long Short Term Memory (LSTM) neural networks from a dynamical system perspective. The classical recursive equations describing the evolution of LSTM can be recast in state space form, resulting in a time-invariant nonlinear dynamical system. A sufficient condition guaranteeing the Input-to-State (ISS) stability property of this class of systems is provided. The ISS property entails the boundedness of the output reachable set of the LSTM. In light of this result, a novel approach for the safety verification of the network, based on the Scenario Approach, is devised. The proposed method is eventually tested on a pH neutralization process.Comment: Accepted for Learning for dynamics & control (L4DC) 202

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

INVESTIGATION OF ADVANCED CONTROL STRATEGY FOR A pH NEUTRALIZATION PROCESS PLANT

pH neutralization is one of the crucial processes to all industries with various functions range from food processing industry to wastewater treatment. Hence, the process must be maintained at optimum performance to fulfill its functionality. However, pH neutralization is a highly nonlinear process with high sensitivity at the neutralization point. The complexity of the process has challenged the conventional control strategy's performance. Currently, the control strategy used in the pilot plant (PI controller) is adequate with certain range of error. Thus, the objective of this project is to investigate, design and implement advanced control strategy which can improve the overall performance of the conventional control strategy. The calibration results show that the pilot plant's measuring meters have poor accuracy and repeatability. Due to this, no practical experiments have been performed throughout this research. Prior to simulation works, the pilot plant's model obtained from other researcher has been validated. The simulation results show that the model has faster dynamic response compare to the pilot plant. Nevertheless, the model is still being used for simulation. Through this research, the limitation of PI control strategy in controlling nonlinear process has been observed. Fuzzy logic controller (FLC) has been developed to improve the control performance of PI controller. According to the simulation results, FLC has produced excellent control performance with the ability of controlling process' nonlinear region. As a conclusion, advanced control strategy such as FLC is more superior to PI controller in nonlinear process control. For further research, perhaps the advanced control strategy developed can be implemented in the pilot plant to examine its real time performance

A Comparative Study of Applying Active-Set and Interior Point Methods in MPC for Controlling Nonlinear pH Process

A comparative study of Model Predictive Control (MPC) using active-set method and interior point methods is proposed as a control technique for highly non-linear pH process. The process is a strong acid-strong base system. A strong acid of hydrochloric acid (HCl) and a strong base of sodium hydroxide (NaOH) with the presence of buffer solution sodium bicarbonate (NaHCO3) are used in a neutralization process flowing into reactor. The non-linear pH neutralization model governed in this process is presented by multi-linear models. Performance of both controllers is studied by evaluating its ability of set-point tracking and disturbance-rejection. Besides, the optimization time is compared between these two methods; both MPC shows the similar performance with no overshoot, offset, and oscillation. However, the conventional active-set method gives a shorter control action time for small scale optimization problem compared to MPC using IPM method for pH control

Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks

The use of Recurrent Neural Networks (RNNs) for system identification has recently gathered increasing attention, thanks to their black-box modeling capabilities.Albeit RNNs have been fruitfully adopted in many applications, only few works are devoted to provide rigorous theoretical foundations that justify their use for control purposes. The aim of this paper is to describe how stable Gated Recurrent Units (GRUs), a particular RNN architecture, can be trained and employed in a Nonlinear MPC framework to perform offset-free tracking of constant references with guaranteed closed-loop stability. The proposed approach is tested on a pH neutralization process benchmark, showing remarkable performances.Comment: This work is the extended version of the article accepted at the Third IFAC Conference on Modelling, Identification and Control of Nonlinear Systems (MICNON 2021) for publication under a Creative Commons Licence CC-BY-NC-N