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Jérôme Mendes has received his PhD degree in Electrical and Computer Engineering from the University of Coimbra in 2014, with the subject “Computational Intelligence Methodologies for Control of Industrial Processes”. He is currently researcher at the Institute of Systems and Robotics (ISR-Coimbra). His current research interests include computational intelligence, intelligent control, intelligent identification, failure detection and predictive maintenance, automatic decision making; evolving systems for control and identification, design of interpretable processes models and controllers for experts operators, and auto-tuning of industrial processes/machines. He has published more than 30 papers, and been involved in 16 R&D projects, being Principal Investigator at ISR-UC of the project “IMfire- Intelligent Management For Wilds” financed by FCT. Jérôme has got funds for his Postdoc, a Postdoctoral FCT grant “Self-Learning Fuzzy Logic Control for Industrial Processes”; from May/2015 to Feb/2019.
This paper proposes the use of a regularized mixture of linear experts (MoLE) for predictive modeling in multimode-multiphase industrial processes. For this purpose, different regularized MoLE were evaluated, namely, through the elastic net (EN), Lasso, and ridge regression (RR) penalties. Their performances were compared when trained with different numbers of samples, and in comparison to other nonlinear predictive models. The models were evaluated on real multiphase polymerization process data. The Lasso penalty provided the best performance among all regularizers for MoLE, even when trained with a small number of samples.
Francisco Souza; Jérôme Mendes; Rui Araújo. A Regularized Mixture of Linear Experts for Quality Prediction in Multimode and Multiphase Industrial Processes. Applied Sciences 2021, 11, 2040 .
AMA StyleFrancisco Souza, Jérôme Mendes, Rui Araújo. A Regularized Mixture of Linear Experts for Quality Prediction in Multimode and Multiphase Industrial Processes. Applied Sciences. 2021; 11 (5):2040.
Chicago/Turabian StyleFrancisco Souza; Jérôme Mendes; Rui Araújo. 2021. "A Regularized Mixture of Linear Experts for Quality Prediction in Multimode and Multiphase Industrial Processes." Applied Sciences 11, no. 5: 2040.
The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.
Jérôme Mendes; Ricardo Maia; Rui Araújo; Francisco A. A. Souza. Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules. Applied Sciences 2020, 10, 5836 .
AMA StyleJérôme Mendes, Ricardo Maia, Rui Araújo, Francisco A. A. Souza. Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules. Applied Sciences. 2020; 10 (17):5836.
Chicago/Turabian StyleJérôme Mendes; Ricardo Maia; Rui Araújo; Francisco A. A. Souza. 2020. "Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules." Applied Sciences 10, no. 17: 5836.
The regeneration factor, that expresses the ratio between the energy recovered to the battery during braking and the total braking energy, is difficult to be measured from independent instruments. In this paper, a reinforcement learning (RL) method is used to adjust and improve a fuzzy logic model for regenerative braking (FLmRB) for modeling Electric Vehicles’ (EV) regenerative braking systems (RBSs). With the proposed approach, a specialist can infer the regeneration factor, by tuning the model for a specific EV using real data gathered from field tests, using as inputs, only variables measured from independent instruments, namely EV acceleration and jerk, and road inclination. The proposed approach was tested with real data sets of the Nissan Leaf EV. Twelve short-distance data sets in urban areas were collected to learn the regeneration factor, and two long-distance data sets in urban and sub-urban areas were used to validate the learned models. The results show that the learning method can successfully learn the regenerative braking factor improving the previously proposed FLmRB model approach which is based on manual design of the model.
Ricardo Maia; Jérôme Mendes; Rui Araújo; Marco Silva; Urbano Nunes. Regenerative braking system modeling by fuzzy Q-Learning. Engineering Applications of Artificial Intelligence 2020, 93, 103712 .
AMA StyleRicardo Maia, Jérôme Mendes, Rui Araújo, Marco Silva, Urbano Nunes. Regenerative braking system modeling by fuzzy Q-Learning. Engineering Applications of Artificial Intelligence. 2020; 93 ():103712.
Chicago/Turabian StyleRicardo Maia; Jérôme Mendes; Rui Araújo; Marco Silva; Urbano Nunes. 2020. "Regenerative braking system modeling by fuzzy Q-Learning." Engineering Applications of Artificial Intelligence 93, no. : 103712.
This paper proposes an iterative learning approach to learn a fuzzy system composed of a sum of multiple univariate zero-order Takagi-Sugeno (T-S) fuzzy systems. The learning algorithm is based on the backfitting algorithm, and new fuzzy rules are iteratively added based on a novelty detection criterion, which gives the novelty degree of a new data by a value between zero and one, allowing an easier rule creation threshold's definition. In order to validate the performance of the proposed approach, 10 benchmark data sets are used to compare the proposed approach with two well-known state-of-the-art methods, the Extreme Learning Machine (ELM), and the Support Vector Regression (SVR), and with the GAM-ZOTS approach, which model is similar to the proposed approach. From the results, it is concluded that the proposed approach outperforms ELM, SVR and GAM-ZOTS in almost all data sets.
Jérôme Mendes; Francisco A. A. Souza; Ricardo Maia; Rui Araújo. Iterative Learning of Multiple Univariate Zero-Order T-S Fuzzy Systems. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society 2019, 1, 3803 -3808.
AMA StyleJérôme Mendes, Francisco A. A. Souza, Ricardo Maia, Rui Araújo. Iterative Learning of Multiple Univariate Zero-Order T-S Fuzzy Systems. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 2019; 1 ():3803-3808.
Chicago/Turabian StyleJérôme Mendes; Francisco A. A. Souza; Ricardo Maia; Rui Araújo. 2019. "Iterative Learning of Multiple Univariate Zero-Order T-S Fuzzy Systems." IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society 1, no. : 3803-3808.
This paper proposes a self-evolving intelligent controller, tested under a proof of concept of a purely virtual test platform for critical cyberphysical systems in closed-loop. The controller is a fuzzy logic controller, whose structure is designed offline using only the information of the range of the variables, and then, it is online designed in an evolving way, where parameters are adjusted, and new control rules are added based on a novelty detection criterion. The controller is tested on a Two-Tank system in a closed-loop networked environment under a proof-of-concept platform for testing cyberphysical systems, named KhronoSim. The proposed self-evolving controller has been successfully evolved/designed, controlling the system on initially unknown regions of operation.
Jerome Mendes; Ricardo Maia; Rui Araujo; Goncalo Gouveia. Intelligent Controller for Industrial Processes Applied to a Distributed Two-Tank System. 2018 First International Conference on Artificial Intelligence for Industries (AI4I) 2018, 39 -43.
AMA StyleJerome Mendes, Ricardo Maia, Rui Araujo, Goncalo Gouveia. Intelligent Controller for Industrial Processes Applied to a Distributed Two-Tank System. 2018 First International Conference on Artificial Intelligence for Industries (AI4I). 2018; ():39-43.
Chicago/Turabian StyleJerome Mendes; Ricardo Maia; Rui Araujo; Goncalo Gouveia. 2018. "Intelligent Controller for Industrial Processes Applied to a Distributed Two-Tank System." 2018 First International Conference on Artificial Intelligence for Industries (AI4I) , no. : 39-43.
An evolving design approach to fuzzy logic controllers (FLCs) is proposed. The FLC to be evolved is composed of a set of univariate fuzzy control rules, allowing to have a better understanding of the influence of each input variable on the controller's behavior. The criterion to add new control rules is based on a novelty detection criterion allowing to detect data which are considered new, unknown, or badly representative, with respect to the learned FLC. The proposed approach is tested on a Two-Tank process controlled in a networked environment under a proof-of-concept platform, named KhronoSim, for testing cyberphysical systems in closed-loop. The proposed approach has successfully evolved/designed the FLC, controlling the process on unknown (for the controller) regions of operation.
Jerome Mendes; Ricardo Maia; Rui Araujo; Goncalo Gouveia. Evolving Fuzzy Controller, and Application to a Distributed Two-Tank Process. 2018 International Conference on Electrical Engineering and Informatics (ICELTICs)(44501) 2018, 55 -60.
AMA StyleJerome Mendes, Ricardo Maia, Rui Araujo, Goncalo Gouveia. Evolving Fuzzy Controller, and Application to a Distributed Two-Tank Process. 2018 International Conference on Electrical Engineering and Informatics (ICELTICs)(44501). 2018; ():55-60.
Chicago/Turabian StyleJerome Mendes; Ricardo Maia; Rui Araujo; Goncalo Gouveia. 2018. "Evolving Fuzzy Controller, and Application to a Distributed Two-Tank Process." 2018 International Conference on Electrical Engineering and Informatics (ICELTICs)(44501) , no. : 55-60.
A proof of concept of a purely virtual test platform for critical cyberphysical systems in closed-loop is presented in this work. An H ∞ direct adaptive fuzzy controller is formulated to tackle the wheel slip tracking problem in Antilock-Braking System over a CAN network. A Lyapunov function for the nonlinear control system is derived using the Riccati equation solution in order to prove stability and robustness with respect to network-induced delays, data packet losses, and model uncertainty. Simulation results show that high performance and robustness are achieved.
Carlos Belchior; Rui Araujo; Jerome Mendes; Alcidney Chaves; Ricardo Maia. $H_{\infty}$ Adaptive Fuzzy Control Approach Applied to Antilock-Braking Systems Over a CAN Network. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 2018, 1, 910 -917.
AMA StyleCarlos Belchior, Rui Araujo, Jerome Mendes, Alcidney Chaves, Ricardo Maia. $H_{\infty}$ Adaptive Fuzzy Control Approach Applied to Antilock-Braking Systems Over a CAN Network. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). 2018; 1 ():910-917.
Chicago/Turabian StyleCarlos Belchior; Rui Araujo; Jerome Mendes; Alcidney Chaves; Ricardo Maia. 2018. "$H_{\infty}$ Adaptive Fuzzy Control Approach Applied to Antilock-Braking Systems Over a CAN Network." 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 1, no. : 910-917.
This paper presents a test case for KhronoSim, a platform for testing Cyber-Physical Systems in closed loop. The test case consists of a satellite in a sun-synchronous orbit. Dynamic physics models have been implemented to simulate the positions and orientation of the different astronomical bodies, as well as the effects of the actuators. These are capable of controlling the satellite's and solar panels' attitudes, currently measured by coarse sun sensors. The test case is implemented under a proof-of-concept of KhronoSim in the form of eleven distributed modules, each containing models of the environment and satellite components which are simulated by means of solving ordinary differential equations via the Runge-Kutta method.
Ricardo Pereira; Rui Araujo; Jerome Mendes; Ricardo Maia; Goncalo Gouveia. Sun-Synchronous Satellite Simulation on KhronoSim, the New System Testing Architecture. 2018 International Conference on Electrical Engineering and Informatics (ICELTICs)(44501) 2018, 61 -66.
AMA StyleRicardo Pereira, Rui Araujo, Jerome Mendes, Ricardo Maia, Goncalo Gouveia. Sun-Synchronous Satellite Simulation on KhronoSim, the New System Testing Architecture. 2018 International Conference on Electrical Engineering and Informatics (ICELTICs)(44501). 2018; ():61-66.
Chicago/Turabian StyleRicardo Pereira; Rui Araujo; Jerome Mendes; Ricardo Maia; Goncalo Gouveia. 2018. "Sun-Synchronous Satellite Simulation on KhronoSim, the New System Testing Architecture." 2018 International Conference on Electrical Engineering and Informatics (ICELTICs)(44501) , no. : 61-66.
Addressed to the problem of translating the control knowledge of a human expert operator into fuzzy control rules, this paper proposes an approach to automatically design a Mamdani fuzzy logic controller. The proposed approach is based on the use of a data set extracted from a process that has been manually controlled, and has the aim of learning a Mamdani logic controller with the capability to imitate the control action of an expert human operator. The proposed approach is an iterative method where fuzzy control rules can be added to the control knowledge base, as a function on the error between the target controller and the learned control. This iterative process stops when the learned controller reaches a behavior similar to the target controller or a maximum control structure complexity. Moreover, a real control setup composed by two coupled DC motors was used to test the performance of the proposed approach. The results show that the proposed approach has the capability of designing the Mamdani fuzzy controller in order to successfully controlling the real experiment.
Jerome Mendes; Antonio Craveiro; Rui Araujo. Iterative Design of a Mamdani Fuzzy Controller. 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO) 2018, 85 -90.
AMA StyleJerome Mendes, Antonio Craveiro, Rui Araujo. Iterative Design of a Mamdani Fuzzy Controller. 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO). 2018; ():85-90.
Chicago/Turabian StyleJerome Mendes; Antonio Craveiro; Rui Araujo. 2018. "Iterative Design of a Mamdani Fuzzy Controller." 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO) , no. : 85-90.
This paper proposes an automatic and simple approach to design a neo-fuzzy neuron for identification purposes. The proposed approach uses the backfitting algorithm to learn multiple univariate additive models, where each additive model is a zero-order T-S fuzzy system which is a function of one input variable, and there is one additive model for each input variable. The multiple zero-order T-S fuzzy models constitute a neo-fuzzy neuron. The structure of the model used in this paper allows to have results with good interpretability and accuracy. To validate and demonstrate the performance and effectiveness of the proposed approach, it is applied on 10 benchmark data sets and compared with the extreme learning machine (ELM), support vector regression (SVR) algorithms, and two algorithms for design neo-fuzzy neuron systems, an adaptive learning algorithm for a neo-fuzzy neuron systems (ALNFN), and a fuzzy Kolmogorov’s network (FKN). A statistical paired t test analysis is also presented to compare the proposed approach with ELM, SVR, ALNFN, and FKN with the aim to see whether the results of the proposed approach are statistically different from ELM, SVR, ALNFN, and FKN. The results indicate that the proposed approach outperforms ELM and FKN in all data sets and outperforms SVR and ALNFN in almost all data sets that they were statistically different in almost all data sets and that in most data sets the number of fuzzy rules selected by cross-validation was small obtaining a model with a small complexity and good interpretability capability.
Jérôme Mendes; Francisco Souza; Rui Araujo; Saeid Rastegar. Neo-fuzzy neuron learning using backfitting algorithm. Neural Computing and Applications 2017, 31, 3609 -3618.
AMA StyleJérôme Mendes, Francisco Souza, Rui Araujo, Saeid Rastegar. Neo-fuzzy neuron learning using backfitting algorithm. Neural Computing and Applications. 2017; 31 (8):3609-3618.
Chicago/Turabian StyleJérôme Mendes; Francisco Souza; Rui Araujo; Saeid Rastegar. 2017. "Neo-fuzzy neuron learning using backfitting algorithm." Neural Computing and Applications 31, no. 8: 3609-3618.
Jérôme Mendes; Luís Osório; Rui Araújo. Self-tuning PID controllers in pursuit of plug and play capacity. Control Engineering Practice 2017, 69, 73 -84.
AMA StyleJérôme Mendes, Luís Osório, Rui Araújo. Self-tuning PID controllers in pursuit of plug and play capacity. Control Engineering Practice. 2017; 69 ():73-84.
Chicago/Turabian StyleJérôme Mendes; Luís Osório; Rui Araújo. 2017. "Self-tuning PID controllers in pursuit of plug and play capacity." Control Engineering Practice 69, no. : 73-84.
The paper proposes a methodology to self-evolve an online fuzzy logic controller (FLC). The proposed methodology does not require any initialization at all, it can start with an empty set of fuzzy control rules or with a simple collection of fuzzy control rules obtained from an expert operator. The FLC design is online, using only the input/output data obtained during the normal operation of the system while it is being controlled. The FLC is composed of a simple structure, where each input variable has its own set of fuzzy control rules, and is evaluated individually by the proposed methodology avoiding the high increase in the number of fuzzy control rules. The FLC structure and their antecedent and consequent parameters are both online modified by the proposed methodology. Only simple information about the system and controller is need, specifically the universe of discourse of the input and output variables, an information that is mandatory to control any process. The performance of the proposed methodology is tested on a simulated continuous-stirred tank reactor (CSTR) system where the results show that the proposed methodology has the capability of designing the FLC in order to successfully controlling the CSTR system by evolving/modifying the FLC structure when unknown regions of operation are reached (unknown for the controller).
Jérôme Mendes; Francisco Souza; Rui Araújo. Online evolving fuzzy control design: An application to a CSTR plant. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) 2017, 218 -225.
AMA StyleJérôme Mendes, Francisco Souza, Rui Araújo. Online evolving fuzzy control design: An application to a CSTR plant. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). 2017; ():218-225.
Chicago/Turabian StyleJérôme Mendes; Francisco Souza; Rui Araújo. 2017. "Online evolving fuzzy control design: An application to a CSTR plant." 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) , no. : 218-225.
Saeid Rastegar; Rui Araújo; Jérôme Mendes. Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm. Applied Mathematical Modelling 2017, 45, 606 -620.
AMA StyleSaeid Rastegar, Rui Araújo, Jérôme Mendes. Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm. Applied Mathematical Modelling. 2017; 45 ():606-620.
Chicago/Turabian StyleSaeid Rastegar; Rui Araújo; Jérôme Mendes. 2017. "Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm." Applied Mathematical Modelling 45, no. : 606-620.
Saeid Rastegar; Rui Araújo; Jalil Sadati; Jérôme Mendes. A novel robust control scheme for LTV systems using output integral discrete-time synergetic control theory. European Journal of Control 2017, 34, 39 -48.
AMA StyleSaeid Rastegar, Rui Araújo, Jalil Sadati, Jérôme Mendes. A novel robust control scheme for LTV systems using output integral discrete-time synergetic control theory. European Journal of Control. 2017; 34 ():39-48.
Chicago/Turabian StyleSaeid Rastegar; Rui Araújo; Jalil Sadati; Jérôme Mendes. 2017. "A novel robust control scheme for LTV systems using output integral discrete-time synergetic control theory." European Journal of Control 34, no. : 39-48.
Francisco A.A. Souza; Rui Araújo; Jérôme Mendes. Review of soft sensor methods for regression applications. Chemometrics and Intelligent Laboratory Systems 2016, 152, 69 -79.
AMA StyleFrancisco A.A. Souza, Rui Araújo, Jérôme Mendes. Review of soft sensor methods for regression applications. Chemometrics and Intelligent Laboratory Systems. 2016; 152 ():69-79.
Chicago/Turabian StyleFrancisco A.A. Souza; Rui Araújo; Jérôme Mendes. 2016. "Review of soft sensor methods for regression applications." Chemometrics and Intelligent Laboratory Systems 152, no. : 69-79.
The paper proposes a new framework to learn a Fuzzy Logic Controller (FLC), from data extracted from a process while it is being manually controlled, in order to control nonlinear industrial processes. The learning of the FLC is performed by a hierarchical genetic algorithm (HGA). First, the fuzzy c-means (FCM) clustering algorithm is applied to initialize the HGA population, in order to reduce the computational cost and increase the performance of the HGA. The HGA is composed by five hierarchical levels and it is an automatic tool since it does not require any prior knowledge concerning the structure (e.g. the number of rules) and the database (e.g. antecedent and consequent fuzzy sets) of the FLC, and concerning the selection of the adequate input variables and their respective time delays. After the extraction of the FLC by the proposed method, in order to obtain a better control results, if necessary, the learned FLC can be improved manually by using the information transmitted by a human operator, and/or the learned FLC could be easily applied to initialize the required fuzzy knowledge-base of adaptive controllers. In order to improve the results of the learned FLC, a direct adaptive fuzzy controller is applied. Moreover, the proposed method is applied on control of the dissolved oxygen in an activated sludge reactor within a simulated wastewater treatment plant. The results are presented, showing that the proposed method successfully extracted the parameters of the FLC.
Jérôme Mendes; Rui Araújo; Tiago Matias; Ricardo Seco; Carlos Belchior. Evolutionary learning of a fuzzy controller for industrial processes. IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society 2014, 139 -145.
AMA StyleJérôme Mendes, Rui Araújo, Tiago Matias, Ricardo Seco, Carlos Belchior. Evolutionary learning of a fuzzy controller for industrial processes. IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society. 2014; ():139-145.
Chicago/Turabian StyleJérôme Mendes; Rui Araújo; Tiago Matias; Ricardo Seco; Carlos Belchior. 2014. "Evolutionary learning of a fuzzy controller for industrial processes." IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society , no. : 139-145.
This paper proposes a method for adaptive identification and predictive control using an online sequential extreme learning machine based on the recursive partial least-squares method (OS-ELM-RPLS). OL-ELM-RPLS is an improvement to the online sequential extreme learning machine based on recursive least-squares (OS-ELM-RLS) introduced in [1]. Like in the batch extreme learning machine (ELM), in OS-ELM-RLS the input weights of a single-hidden layer feedforward neural network (SLFN) are randomly generated, however the output weights are obtained by a recursive least-squares (RLS) solution. However, due to multicollinearities in the columns of the hidden-layer output matrix caused by the presence of redundant input variables or by a large number of hidden-layer neurons, the problem of estimation the output weights can become ill-conditioned. In order to circumvent or mitigate such ill-conditioning problem, it is proposed to replace the RLS method by the recursive partial least-squares (RPLS) method. The identification methodology is proposed for two application problems: (1) construction of a inferential model, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an adaptive predictive control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on modeling of two public regression data sets and on control of the flow through a simulated valve.
Tiago Matias; Francisco Souza; Rui Araújo; Saeid Rastegar; Jérôme Mendes. Adaptive identification and predictive control using an improved on-line sequential extreme learning machine. IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society 2014, 58 -64.
AMA StyleTiago Matias, Francisco Souza, Rui Araújo, Saeid Rastegar, Jérôme Mendes. Adaptive identification and predictive control using an improved on-line sequential extreme learning machine. IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society. 2014; ():58-64.
Chicago/Turabian StyleTiago Matias; Francisco Souza; Rui Araújo; Saeid Rastegar; Jérôme Mendes. 2014. "Adaptive identification and predictive control using an improved on-line sequential extreme learning machine." IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society , no. : 58-64.
This paper proposes a new unsupervised fuzzy clustering algorithm (NUFCA) to construct a novel online evolving Takagi–Sugeno (T-S) fuzzy model identification method and an adaptive predictive process control methodology. The proposed system identification approach consists of two main steps: antecedent T-S fuzzy model parameters identification and consequent parameters identification. The NUFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modelling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system; then the recursive least squares method is exploited to obtain initialization type consequent parameters and to construct a method for on-line fuzzy model identification. The integration of the proposed adaptive identification method with the generalized predictive control results in an effective adaptive predictive fuzzy control methodology. For better demonstration of the robustness and efficiency of the proposed methodology, it is applied to the identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment plant (WWTP); and to control a simulated continuous stirred tank reactor (CSTR), and a real experimental setup composed of two coupled DC motors. The results show that the developed evolving T-S fuzzy model methodology can identify nonlinear systems satisfactorily and can be successfully used for a prediction model of the process for the generalized predictive controller. It is also shown that the algorithm is robust to changes in the initial parameters, and to unexpected disturbances.
Saeid Rastegar; Rui Araújo; Jérôme Mendes. A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems. Journal of Vibration and Control 2014, 22, 1820 -1837.
AMA StyleSaeid Rastegar, Rui Araújo, Jérôme Mendes. A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems. Journal of Vibration and Control. 2014; 22 (7):1820-1837.
Chicago/Turabian StyleSaeid Rastegar; Rui Araújo; Jérôme Mendes. 2014. "A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems." Journal of Vibration and Control 22, no. 7: 1820-1837.
Jérôme Mendes; Rui Araújo; Tiago Matias; Ricardo Seco; Carlos Belchior. Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm. Engineering Applications of Artificial Intelligence 2014, 29, 70 -78.
AMA StyleJérôme Mendes, Rui Araújo, Tiago Matias, Ricardo Seco, Carlos Belchior. Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm. Engineering Applications of Artificial Intelligence. 2014; 29 ():70-78.
Chicago/Turabian StyleJérôme Mendes; Rui Araújo; Tiago Matias; Ricardo Seco; Carlos Belchior. 2014. "Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm." Engineering Applications of Artificial Intelligence 29, no. : 70-78.
Jérôme Mendes; Rui Araújo; Francisco Souza. Adaptive fuzzy identification and predictive control for industrial processes. Expert Systems with Applications 2013, 40, 6964 -6975.
AMA StyleJérôme Mendes, Rui Araújo, Francisco Souza. Adaptive fuzzy identification and predictive control for industrial processes. Expert Systems with Applications. 2013; 40 (17):6964-6975.
Chicago/Turabian StyleJérôme Mendes; Rui Araújo; Francisco Souza. 2013. "Adaptive fuzzy identification and predictive control for industrial processes." Expert Systems with Applications 40, no. 17: 6964-6975.