Rui Araújo

Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control

Abstract In the operation of wastewater treatment plants a key variable is dissolved oxygen (DO) content in the bioreactors. The paper describes the development of an adaptive fuzzy control strategy for tracking the DO reference trajectory applied to the Benchmark Simulation Model n.1. The design methodology of this data-driven controller uses the Lyapunov synthesis approach with a parameter projection algorithm to construct an adaptive fuzzy controller (AFC), and guarantees the global stability of the resulting closed-loop system. To work in parallel with the AFC it is proposed a new easy to design supervisory fuzzy control with a smooth switching scheme between supervisory and nonsupervisory modes. Both controllers assume no mathematical model of the plant and may integrate human knowledge. The results of simulations show that this combined controller can learn and improve control rules resulting in accurate DO control.

Electric vehicle simulator for energy consumption studies in electric mobility systems

One of the most important environmental problems in large cities is the vehicular emission. Electric Vehicles (EVs) are a growing alternative for internal combustion engine (ICE) vehicles. Since this kind of vehicle has low autonomy yet, it is important to optimize energy consumption, for instance by planning a suitable infrastructure of battery recharge and/or battery-switch stations. This paper presents an architecture for EV simulation, important to analyze traffic flow, its dynamics and the performance when there are obstructions or intense traffic. There are several tools for traffic simulation, SUMO (Simulation of Urban MObility) is one of them. But none of the existing traffic simulators integrates models of EV that allow, for example, perform simulation studies regarding energy consumption. SUMO is a portable open source simulator with multi-modal traffic feature capabilities that permit the simulation of various types of vehicles. This work is an extension of the SUMO, two-dimensional (2D) vehicular simulation package. To allow the simulation of energy consumption of EV, two extensions were incorporated in SUMO: EV models and modeling of altitude, transforming SUMO into a three-dimensional (3D) simulator. The energy model effectiveness and correctness with 3D capabilities has been validated using two driving schedules (Urban Dynamometer Driving Schedule and Highway Fuel Economy Driving Schedule). This new tool will also support the study of better routes choice in 3D environment with EV aiming minimum energy consumption.

Learning sensor-based navigation of a real mobile robot in unknown worlds

In this paper, we address the problem of navigating an autonomous mobile robot in an unknown indoor environment. The parti-game multiresolution learning approach is applied for simultaneous and cooperative construction of a world model, and learning to navigate through an obstacle-free path from a starting position to a known goal region. The paper introduces a new approach, based on the application of the fuzzy ART neural architecture, for on-line map building from actual sensor data. This method is then integrated, as a complement, on the parti-game world model, allowing the system to make a more efficient use of collected sensor information. Then, a predictive on-line trajectory filtering method, is introduced in the learning approach. Instead of having a mechanical device moving to search the world, the idea is to have the system analyzing trajectories in a predictive mode, by taking advantage of the improved world model. The real robot will only move to try trajectories that have been predicted to be successful, allowing lower exploration costs. This results in an overall improved new method for goal-oriented navigation. It is assumed that the robot knows its own current world location-a simple dead-reckoning method is used for localization in our experiments. It is also assumed that the robot is able to perform sensor-based obstacle detection (not avoidance) and straight-line motions. Results of experiments with a real Nomad 200 mobile robot are presented, demonstrating the effectiveness of the discussed methods.

Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development

In the last decades ensemble learning has established itself as a valuable strategy within the computational intelligence modeling and machine learning community. Ensemble learning is a paradigm where multiple models combine in some way their decisions, or their learning algorithms, or different data to improve the prediction performance. Ensemble learning aims at improving the generalization ability and the reliability of the system. Key factors of ensemble systems are diversity, training and combining ensemble members to improve the ensemble system performance. Since there is no unified procedure to address all these issues, this work proposes and compares Genetic Algorithm and Simulated Annealing based approaches for the automatic development of Neural Network Ensembles for regression problems. The main contribution of this work is the development of optimization techniques that selects the best subset of models to be aggregated taking into account all the key factors of ensemble systems (e.g., diversity, training ensemble members and combination strategy). Experiments on two well-known data sets are reported to evaluate the effectiveness of the proposed methodologies. Results show that these outperform other approaches including Simple Bagging, Negative Correlation Learning (NCL), AdaBoost and GASEN in terms of generalization ability.

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T-S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T-S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithms performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T-S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, 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 effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T-S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.

Prune-Able Fuzzy ART Neural Architecture for Robot Map Learning and Navigation in Dynamic Environments

Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new online method for learning maps of unknown dynamic worlds. For this purpose the new Prune-able fuzzy adaptive resonance theory neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network with novel mechanisms that provide important dynamic adaptation capabilities. Relevant PAFARTNA properties are formulated and demonstrated. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. The proposed methods are integrated into a navigation architecture. With the new navigation architecture the mobile robot is able to navigate in changing worlds, and a degree of optimality is maintained, associated to a shortest path planning approach implemented in real-time over the underlying global world model. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods

An on-line weighted ensemble of regressor models to handle concept drifts

Abstract Many estimation, prediction, and learning applications have a dynamic nature. One of the most important challenges in machine learning is dealing with concept changes. Underlying changes may make the model designed on old data, inconsistent with new data. Also, algorithms usually specialize in one type of change. Other challenge is reusing previously acquired information in scenarios where changes may recur. This strategy improves the learning accuracy and reduces the processing time. Unfortunately, most existing learning algorithms to deal with changes are adapted on a batch basis. This process usually requires a long time, and such data may not reflect the current state of the system. However, even the system is adapted on a sample basis, existing algorithms may adapt slowly to changes and cannot conciliate old and new information. This paper proposes an On-line Weighted Ensemble (OWE) of regressor models which is able to learn incrementally sample by sample in the presence of several types of changes and simultaneously retain old information in recurring scenarios. The key idea is to keep a moving window that slides when a new sample is available. The error of each model on the current window is determined using a boosting strategy that assigns small errors to the models that predict accurately the samples predicted poorly by the ensemble. To handle recurring and non-recurring changes, OWE uses a new assignment of models׳ weights that takes into account the models׳ errors on the past and current windows using a discounting factor that decreases or increases the contribution of old windows. In addition, OWE launches new models if the system׳s accuracy is decreasing, and it can exclude inaccurate models over time. Experiments with artificial and industrial data reveal that in most cases OWE outperforms other state-of-the-art concept drift approaches.

Genetic fuzzy system for data-driven soft sensors design

This paper proposes a new method for soft sensors (SS) design for industrial applications based on a Takagi-Sugeno (T-S) fuzzy model. The learning of the T-S model is performed from input/output data to approximate unknown nonlinear processes by a coevolationary genetic algorithm (GA). The proposed method is an automatic tool for SS design since it does not require any prior knowledge concerning the structure (e.g. the number of rules) and the database (e.g. antecedent fuzzy sets) of the T-S fuzzy model, and concerning the selection of the adequate input variables and their respective time delays for the prediction setting. The GA approach is composed by five hierarchical levels and has the global goal of maximizing the prediction accuracy. The first level consists in the selection of the set of input variables and respective delays for the T-S fuzzy model. The second level considers the encoding of the membership functions. The individual rules are defined at the third level, the population of the set of rules is treated in fourth level, and a population of fuzzy systems is handled at the fifth level. To validate and demonstrate the performance and effectiveness of the proposed algorithm, it is applied on two prediction problems. The first is the Box-Jenkins benchmark problem, and the second is the estimation of the flour concentration in the effluent of a real-world wastewater treatment system. Simulation results are presented showing that the developed evolving T-S fuzzy model can identify the nonlinear systems satisfactorily with appropriate input variables and delay selection and a reasonable number of rules. The proposed methodology is able to design all the parts of the T-S fuzzy prediction model. Moreover, presented comparison results indicate that the proposed method outperforms other previously proposed methods for the design of prediction models, including methods previously proposed for the design of T-S models.

Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm

The paper proposes a new method to automatically extract all fuzzy parameters of a Fuzzy Logic Controller (FLC) in order to control nonlinear industrial processes. The main objective of this paper is the extraction of a FLC from data extracted from a given process while it is being manually controlled. The learning of the FLC is performed by a hierarchical genetic algorithm (HGA), from a set of process-controlled input/output data. The algorithm is composed by a five level structure, being the first level responsible for the selection of an adequate set of input variables. The second level considers the encoding of the membership functions. The individual rules are defined on the third level. The set of rules are obtained on the fourth level, and finally, the fifth level selects the elements of the previous levels, as well as, the t-norm operator, inference engine and defuzzifier methods which constitute the FLC. To optimize the proposed method, the HGAs initial populations are obtained by an initialization algorithm. This algorithm has the main goal of providing a good initial solution for membership functions and rule based populations, enhancing the GAs tuning. Moreover, the HGA is applied to control the dissolved oxygen in an activated sludge reactor within a wastewater treatment plant. The results are presented, showing that the proposed method extracted all the parameters of the fuzzy controller, successfully controlling a nonlinear plant.

Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine

This paper proposes a learning framework for single-hidden layer feedforward neural networks (SLFN) called optimized extreme learning machine (O-ELM). In O-ELM, the structure and the parameters of the SLFN are determined using an optimization method. The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonovs regularization in order to improve the SLFN performance in the presence of noisy data. The optimization method is used to the set of input variables, the hidden-layer configuration and bias, the input weights and Tikhonovs regularization factor. The proposed framework has been tested with three optimization methods (genetic algorithms, simulated annealing, and differential evolution) over 16 benchmark problems available in public repositories.