Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations

Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system

Grey-box model identification via evolutionary computing

This paper presents an evolutionary grey-box model identification methodology that makes the best use of a priori knowledge on a clear-box model with a global structural representation of the physical system under study, whilst incorporating accurate blackbox models for immeasurable and local nonlinearities of a practical system. The evolutionary technique is applied to building dominant structural identification with local parametric tuning without the need of a differentiable performance index in the presence of noisy data. It is shown that the evolutionary technique provides an excellent fitting performance and is capable of accommodating multiple objectives such as to examine the relationships between model complexity and fitting accuracy during the model building process. Validation results show that the proposed method offers robust, uncluttered and accurate models for two practical systems. It is expected that this type of grey-box models will accommodate many practical engineering systems for a better modelling accuracy

Data Driven Modelling and Optimization of MEA Absorption Process for CO2 Capture

Global warming is a rising issue and there are many research studies aiming to reduce greenhouse gas emissions. Carbon capture and storage technologies improved throughout the years to contribute as a solution to this problem. In this work the post-combustion carbon capture unit is used to develop surrogated models for operation optimization. Previous work included mechanistic and detailed modeling of steady-state and dynamic systems. Furthermore, control structures and optimization approaches have been studied. Moreover, various solutions such as MEA, DEA, and MDEA have been tested and simulated to determine the efficiency and the behavior of the system. In this work a dynamic model with MEA solution developed by (Nittaya, 2014) and (Harun, 2012) is used to generate operational data. The system is simulated using gProms v.5.1 with six PI controllers. The model illustrated that the regeneration of the solvent is the most energy-consuming part of the process. Due to the changes in electricity supply and demand, also, the importance of achieving a specific %CC and purity of carbon dioxide as outputs of this process, surrogated models are developed and used to predict the outputs and to optimize the operating conditions of the process. Multiple machine learning and data-driven models has been developed using simulation data generated after a proper choice of the operating variables and the important outputs. Steady-state and transient state models have been developed and evaluated. The models were used to predict the outputs of the process and used later to optimize the operating conditions of the process. The flue gas flow rate, temperature, pressure, reboiler pressure, reboiler, and condenser duties were selected as the operating variables of the system (inputs). The system energy requirements, %CC, and the purity of carbon dioxide were selected to be the outputs of the process. For steady-state modeling, artificial neural network (ANN) model with backpropagation and momentum was developed to predict the process outputs. The ANN model efficiency was compared to other machine learning models such as Gaussian Process Regression (GPR), rational quadratic GPR, squared exponential GPR, tree regression and matern GPR. The ANN excelled all other models in terms of prediction and accuracy, however, the other model’s regression coefficient (R2) was never below 0.95. For dynamic modelling, recurrent neural networks (RNN) have been used to predict the outputs of the system. Two training algorithms have been used to create the neural network: Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfrab-Shanno (BFGS). The RNN was able to predict the outputs of the system accurately. Sequential quadratic programming (SQP) and genetic algorithm (GA) were used to optimize the surrogated models and determine the optimum operating conditions following an objective of maximizing the purity of CO2 and %CC and minimizing the system energy requirements

Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models

Matlab/Simulink is a development and simulation language that is widely used by the Cyber-Physical System (CPS) industry to model dynamical systems. There are two mainstream approaches to verify CPS Simulink models: model testing that attempts to identify failures in models by executing them for a number of sampled test inputs, and model checking that attempts to exhaustively check the correctness of models against some given formal properties. In this paper, we present an industrial Simulink model benchmark, provide a categorization of different model types in the benchmark, describe the recurring logical patterns in the model requirements, and discuss the results of applying model checking and model testing approaches to identify requirements violations in the benchmarked models. Based on the results, we discuss the strengths and weaknesses of model testing and model checking. Our results further suggest that model checking and model testing are complementary and by combining them, we can significantly enhance the capabilities of each of these approaches individually. We conclude by providing guidelines as to how the two approaches can be best applied together.Comment: 10 pages + 2 page reference

A new T-S fuzzy model predictive control for nonlinear processes

Abstract: In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems

Developing dynamic machine learning surrogate models of physics-based industrial process simulation models

Abstract. Dynamic physics-based models of industrial processes can be computationally heavy which prevents using them in some applications, e.g. in process operator training. Suitability of machine learning in creating surrogate models of a physics-based unit operation models was studied in this research. The main motivation for this was to find out if machine learning model can be accurate enough to replace the corresponding physics-based components in dynamic modelling and simulation software Apros® which is developed by VTT Technical Research Centre of Finland Ltd and Fortum. This study is part of COCOP project, which receive funding from EU, and INTENS project that is Business Finland funded. The research work was divided into a literature study and an experimental part. In the literature study, the steps of modelling with data-driven methods were studied and artificial neural network architectures suitable for dynamic modelling were investigated. Based on that, four neural network architectures were chosen for the case studies. In the first case study, linear and nonlinear autoregressive models with exogenous inputs (ARX and NARX respectively) were used in modelling dynamic behaviour of a water tank process build in Apros®. In the second case study, also Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were considered and compared with the previously mentioned ARX and NARX models. The workflow from selecting the input and output variables for the machine learning model and generating the datasets in Apros® to implement the machine learning models back to Apros® was defined. Keras is an open source neural network library running on Python that was utilised in the model generation framework which was developed as a part of this study. Keras library is a very popular library that allow fast experimenting. The framework make use of random hyperparameter search and each model is tested on a validation dataset in dynamic manner, i.e. in multi-step-ahead configuration, during the optimisation. The best models based in terms of average normalised root mean squared error (NRMSE) is selected for further testing. The results of the case studies show that accurate multi-step-ahead models can be built using recurrent artificial neural networks. In the first case study, the linear ARX model achieved slightly better NRMSE value than the nonlinear one, but the accuracy of both models was on a very good level with the average NRMSE being lower than 0.1 %. The generalisation ability of the models was tested using multiple datasets and the models proved to generalise well. In the second case study, there were more difference between the models’ accuracies. This was an expected result as the studied process contains nonlinearities and thus the linear ARX model performed worse in predicting some output variables than the nonlinear ones. On the other hand, ARX model performed better with some other output variables. However, also in the second case study the model NRMSE values were on good level, being 1.94–3.60 % on testing dataset. Although the workflow to implement machine learning models in Apros® using its Python binding was defined, the actual implementation need more work. Experimenting with Keras neural network models in Apros® was noticed to slow down the simulation even though the model was fast when testing it outside of Apros®. The Python binding in Apros® do not seem to cause overhead to the calculation process which is why further investigating is needed. It is obvious that the machine learning model must be very accurate if it is to be implemented in Apros® because it needs to be able interact with the physics-based model. The actual accuracy requirement that Apros® sets should be also studied to know if and in which direction the framework made for this study needs to be developed.Dynaamisten surrogaattimallien kehittäminen koneoppimismenetelmillä teollisuusprosessien fysiikkapohjaisista simulaatiomalleista. Tiivistelmä. Teollisuusprosessien toimintaa jäljittelevät dynaamiset fysiikkapohjaiset simulaatiomallit voivat laajuudesta tai yksityiskohtien määrästä johtuen olla laskennallisesti raskaita. Tämä voi rajoittaa simulaatiomallin käyttöä esimerkiksi prosessioperaattorien koulutuksessa ja hidastaa simulaattorin avulla tehtävää prosessien optimointia. Tässä tutkimuksessa selvitettiin koneoppimismenetelmillä luotujen mallien soveltuvuutta fysiikkapohjaisten yksikköoperaatiomallien surrogaattimallinnukseen. Fysiikkapohjaiset mallit on luotu teollisuusprosessien dynaamiseen mallinnukseen ja simulointiin kehitetyllä Apros®-ohjelmistolla, jota kehittää Teknologian tutkimuskeskus VTT Oy ja Fortum. Työ on osa COCOP-projektia, joka saa rahoitusta EU:lta, ja INTENS-projektia, jota rahoittaa Business Finland. Työ on jaettu kirjallisuusselvitykseen ja kahteen kokeelliseen case-tutkimukseen. Kirjallisuusosiossa selvitettiin datapohjaisen mallinnuksen eri vaiheet ja tutkittiin dynaamiseen mallinnukseen soveltuvia neuroverkkorakenteita. Tämän perusteella valittiin neljä neuroverkkoarkkitehtuuria case-tutkimuksiin. Ensimmäisessä case-tutkimuksessa selvitettiin lineaarisen ja epälineaarisen autoregressive model with exogenous inputs (ARX ja NARX) -mallin soveltuvuutta pinnankorkeuden säädöllä varustetun vesisäiliömallin dynaamisen käyttäytymisen mallintamiseen. Toisessa case-tutkimuksessa tarkasteltiin edellä mainittujen mallityyppien lisäksi Long Short-Term Memory (LSTM) ja Gated Recurrent Unit (GRU) -verkkojen soveltuvuutta power-to-gas prosessin metanointireaktorin dynaamiseen mallinnukseen. Työssä selvitettiin surrogaattimallinnuksen vaiheet korvattavien yksikköoperaatiomallien ja siihen liittyvien muuttujien valinnasta datan generointiin ja koneoppimismallien implementointiin Aprosiin. Koneoppimismallien rakentamiseen tehtiin osana työtä Python-sovellus, joka hyödyntää Keras Python-kirjastoa neuroverkkomallien rakennuksessa. Keras on suosittu kirjasto, joka mahdollistaa nopean neuroverkkomallien kehitysprosessin. Työssä tehty sovellus hyödyntää neuroverkkomallien hyperparametrien optimoinnissa satunnaista hakua. Jokaisen optimoinnin aikana luodun mallin tarkkuutta dynaamisessa simuloinnissa mitataan erillistä aineistoa käyttäen. Jokaisen mallityypin paras malli valitaan NRMSE-arvon perusteella seuraaviin testeihin. Case-tutkimuksen tuloksien perusteella neuroverkoilla voidaan saavuttaa korkea tarkkuus dynaamisessa simuloinnissa. Ensimmäisessä case-tutkimuksessa lineaarinen ARX-malli oli hieman epälineaarista tarkempi, mutta molempien mallityyppien tarkkuus oli hyvä (NRMSE alle 0.1 %). Mallien yleistyskykyä mitattiin simuloimalla usealla aineistolla, joiden perusteella yleistyskyky oli hyvällä tasolla. Toisessa case-tutkimuksessa vastemuuttujien tarkkuuden välillä oli eroja lineaarisen ja epälineaaristen mallityyppien välillä. Tämä oli odotettu tulos, sillä joidenkin mallinnettujen vastemuuttujien käyttäytyminen on epälineaarista ja näin ollen lineaarinen ARX-malli suoriutui niiden mallintamisesta epälineaarisia malleja huonommin. Toisaalta lineaarinen ARX-malli oli tarkempi joidenkin vastemuuttujien mallinnuksessa. Kaiken kaikkiaan mallinnus onnistui hyvin myös toisessa case-tutkimuksessa, koska käytetyillä mallityypeillä saavutettiin 1.94–3.60 % NRMSE-arvo testidatalla simuloitaessa. Koneoppimismallit saatiin sisällytettyä Apros-malliin käyttäen Python-ominaisuutta, mutta prosessi vaatii lisäselvitystä, jotta mallit saadaan toimimaan yhdessä. Testien perusteella Keras-neuroverkkojen käyttäminen näytti hidastavan simulaatiota, vaikka neuroverkkomalli oli nopea Aprosin ulkopuolella. Aprosin Python-ominaisuus ei myöskään näytä itsessään aiheuttavan hitautta, jonka takia asiaa tulisi selvittää mallien implementoinnin mahdollistamiseksi. Koneoppimismallin tulee olla hyvin tarkka toimiakseen vuorovaikutuksessa fysiikkapohjaisen mallin kanssa. Jatkotutkimuksen ja Python-sovelluksen kehittämisen kannalta on tärkeää selvittää mikä on Aprosin koneoppimismalleille asettama tarkkuusvaatimus

NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun