The design of effective controllers for complex Two-Input-Two-Output (TITO) systems presents a significant challenge, particularly in the context of implementing a design methodology suited to this specific type of process. In the majority of cases, if the system in question exhibits highly complex dynamics or a high order, the procedure to be performed is an order reduction. This is done in order to describe the essential dynamic characteristics of the original system in a way that allows for the reliable design of controllers. This work presents a practical approach to the design of decentralized Fractional-Order Proportional-Integral-Derivative (FOPID) controllers for TITO systems. The methodology comprises a reduced-order representation of the original system and an optimization-guided design. The intention is to quantify the performance loss in the decentralized FOPID controller design procedure when reduced-order models are employed. This will facilitate a validity analysis of the fractional controller selection. To enable a fair comparison in the validation example, an equivalent procedure for a decentralized PID controller will be presented in parallel.

This paper builds upon previous research into training Artificial Neural Network (ANN)-based Proportional-Integral-Derivative (PID) controllers using the Scheduled Sampling (SS) approach. While SS was competitive for First-Order Plus Dead Time systems, its application to more challenging situations was to be studied. To address this issue, we introduce an Enhanced Scheduled Sampling (ESS) and apply it to a non-linear Continuous Stirred-Tank Reactor. The presented ESS methodology incorporates key enhancements such as weighted ANN actuation to blend controller outputs, including a sequence-based control error term and a controlled dynamic learning rate adjustment strategy. Preliminary results show that ESS models substantially outperform those trained with conventional/offline training, achieving control performance closer to the PID benchmark. The ESS framework robustly manages the challenges posed by complex systems, thus laying the foundation for developing effective ANN controllers for advancing future transfer learning research in control.

The CEC-UAB simulator models energy dynamics in citizen energy communities, simulating household consumption, photovoltaic generation, battery storage, and user behavior. This study leverages the simulator to evaluate dynamic energy allocation through predicting distribution coefficients. We compare the Holt-Winters Seasonal Forecasting Approach with the machine learning-based XGBoostLSS method. Using realistic CEC-UAB data, XGBoostLSS outperforms Holt-Winters, achieving a Mean Absolute Error (MAE) of 0.016 versus 0.023, Root Mean Squared Error (RMSE) of 0.022 versus 0.031, and Mean Absolute Scaled Error (MASE) of 0.167 versus 0.240. These results highlight the potential of combining advanced forecasting with realistic simulation tools to enhance fairness and efficiency in community energy systems.

In this paper, we present the implementation of the Benchmark Simulation Model No.1 (BSM1) on a Speedgoat Performance real-time target machine, with the aim of enabling hardware-in-the-loop (HIL) testing of advanced control strategies for wastewater treatment plants (WWTPs). Our main contribution lies in adapting the BSM1 Simulink model for execution in real-time environments. We focus on the dissolved oxygen (DO) control loop and integrate an artificial neural network (ANN)-based controller previously proposed by the authors. The required adjustments include resolving algebraic loops, handling fixed-step time constraints, and enabling real-time signal visualization through interpolation. Preliminary results confirm that the adapted BSM1 model runs reliably on the Speedgoat platform, preserving the dynamics of the original simulation and enabling future real-time control experimentation.

Collective Self-Consumption (CSC) is among the most widely adopted models for energy communities in countries like Spain and France. Despite its growth, challenges remain in optimizing photovoltaic energy distribution, improving short- to medium-term investment returns, and advancing toward smarter, self-sustaining communities. This study examines the Spain regulatory framework to identify key opportunities for enhancing CSC performance. A real-world case study of a Municipal Energy Community (MEC) in Barcelona is analyzed employing historical consumption and generation data. Simulations across two time-interval scenarios are presented, with the objective of evaluating current energy management strategies. The results highlight critical areas where optimization algorithms or machine learning approaches could substantially improve energy distribution and overall community performance.

Accurate and interpretable forecasting of photo-voltaic (PV) output is critical for grid integration and day-ahead market participation. Traditional black-box models often neglect the evolving internal dynamics of solar panels, particularly changes in efficiency due to aging and environmental conditions. This work introduces a novel framework that combines online machine learning with Bayesian inference to simultaneously predict solar power output and infer real-time panel efficiency. The model incrementally updates its understanding of efficiency based on observed data, providing continuous adaptation to changing conditions. By modeling efficiency as a probabilistic parameter, we quantify uncertainty in both predictions and inferences. Our method provides a transparent, adaptive, and uncertainty-aware solution.

Accurately predicting hydraulic behavior in Water Supply Systems (WSS) is fundamental for the maintenance of reliable operations, optimization of energy use, and control of maintenance costs. This study proposes a comparative study of several Machine Learning (ML) methodologies incorporating physics-informed, transfer learning, and ensemble techniques, to predict hydraulic behavior. Traditional hydraulic simulators, such as EPANET, typically rely on first principle hydraulic equations that may not capture real-world complexity. By integrating physical knowledge from multiple sources (e.g., EPANET output and simple mass conservation laws), the gap between predicted and observed behavior is minimized.The techniques analyzed in this work were trained and tested on a real-world WSS dataset from a water network in Portugal. Results show that semi-supervised and ensemble techniques often deliver superior accuracy. It was evidenced that leveraging knowledge from diverse domains may boost the ML models capability to generalize in unseen data. It is also suggested that a tailored feature-by-feature importance weighting (in the output) leads to improved predictive performance.Overall, this study offers practical guidance on the integration of physical-informed knowledge into the ML training process in complex and noisy systems. It is expected that the acquired observations lead to an increase in predictive capabilities applied in WSS or other systems.

Proportional-Integral-Derivative (PID) controllers are extensively used in industrial control applications due to their simplicity and effectiveness in various control tasks. In recent years, there has been a growing emphasis on the integration of Artificial Neural Networks (ANNs) with control theory. This integration aims to harness the versatility of ANNs to facilitate controller design in dynamic environments. Additionally, it opens the possibility of transfer learning for these models, allowing knowledge gained from one control scenario to be applied to others' thereby enhancing adaptability and efficiency in control applications. In this work-in-progress paper, we propose a training framework that addresses the key challenges of modeling PIDs as ANNs, specifically the discrepancy between training and inference behaviors, known as exposure bias, commonly encountered in text summarization models. To tackle this, we integrate scheduled sampling, a technique devised for text sequence generation tasks, with an online control simulation environment.

Due to growing concerns about water scarcity, achieving sustainable and effective management of water supply systems has become increasingly important. This work addresses this by focusing on pump operation with the objective of minimizing operational costs. Effective management of water infrastructures requires handling large amounts of data and enhancing its control system with predictive and decision-making technologies. To achieve this, the implementation of a Smart Predictive Digital Twin integrated into an orchestrated multiservice architecture is proposed. This work showcases the implementation of some aspects of the proposed systems, specifically the Machine Learning model to emulate the hydraulic behavior, generation of pump schedules, and the system's data integration.

In this work, a model-free Reinforcement Learning method is implemented to control the aeration in the nitrification process in wastewater treatment plants (WWTPs). Controlling real complex non-linear processes is challenging due to model mismatch between the model used in the controller and the real process. Model-based control strategies can fail in a real implementation due to modeling errors. WWTPs include multivariable, nonlinear, and complex biological processes, which makes it difficult to obtain adequate models to represent the dynamics of the process under all possible conditions. For this reason, a model-free reinforcement learning agent that takes advantage of Transfer Learning is selected. This data-driven procedure is trained to deal with the trade-off between effluent quality and operating costs. The results demonstrate that model-free RL can reach sub-optimal solutions through a data-driven procedure.

The decoupled control strategy is a well-established methodology in process control applications. This work-inprogress (WIP) manuscript introduces the decoupled internal model control-proportional derivative (IMC-PD) dual-loop scheme for inverse response process with dead time. The PD controller is constituted on Routh-stability guidelines with the help of Pade’s approximation method. The servo or the primary controller is designed on the IMC principle with a solitary tunable parameter. The equilibrium optimizer (EO) finally obtains the equilibrium locale (optimal controller settings) with the objective of minimizing integral square error. The suggested scheme is tested on a boiler steam level control system through simulations. It delivers appreciable enhancement in performance measures pertaining to transient and steady-state dynamics when compared with prevalent strategies.

This paper proposes some solutions to optimize the Butterworth filter from the structure of plug-in type repetitive controllers, as well as the use of the Bessel filter, which offers a simpler way of choosing the anticipatory factor from the repetitive controller structure. The comparative analysis of the two types of filters was made on the basis of three criteria: 1) the standard deviation of the error in the control loop, where the first type of filter proved to be superior; 2) ease of obtaining the optimal value of the anticipative parameter, in which case the Bessel filter is preferred; 3) sensitivity to changes in the filter parameters and/or process model, where the second filter is also preferred

In the last decades the use of fractional calculus in control applications has risen especially in the process modelling and the control algorithm design and controller tuning which are indispensable stages in the design of a control system. Several works have proved the effectiveness of the use of fractional order proportional-integral-derivative controllers, to satisfy performance considerations in the set-point tracking and in the load disturbance rejection, as well as for achieving robustness levels. Most of the studies have focused the tuning of FOPID controllers based on stable dynamics well characterized by first and second order plus dead time models as well as integrating Dynamics. However, when the process presents an inverse response there are not so many Works that explain how design the closed-loop system based on performance and robustness considerations and using a FOPID controller. In this work we present a methodology to compare the performance provided by the use of PID and FOPID when a robustness constraint is considered for inverse response dynamics in the control of an isothermal continuous stirred tank reactor (CSTR) and a process model with a transfer function which has large values for the normalized dead time and for the ratio between the zero time constant and the dominant time constant.

Microalgae are attractive in wastewater treatment applications due to their capacity of growing on inorganic nutrients (e.g., nitrogen and phosphorus salts) while their biomass is used to produce biodiesel, biogas or even bioethanol. The photosynthetic growth is the most attractive mechanism, but it brings additional limitation to the open ponds or the Photobioreactors (PBRs) in which they grow. The light is thus the main factor that drives the growth of microalgae and, with the development of commercial efficient LED light sources, the indoor cultures can become more efficient. The use of artificial light creates a new class of controllers, the lumostas, by which the incident light intensity is manipulated. In this paper, an alternative proposal based on PID control acting on Biomass algae desired profile is proposed. It is seen that the generated manipulated incident light generates optimal profiles for he biomass yield on light ratio. Also, better, more efficient batches that the ones based on model prediction over all the batch are obtained. This is a preliminary result that is to be extended with biomass observers. Also, in future work, the tuning of the PID controller in a more automated way will be addressed.

The present study aimed to develop a model that simulates the growth of Porphyridium purpureum, which explores the dependency of biomass concentration to light availability, through coupling of the radiative model and the kinetic growth of microalgae biomass. The desiderate was to fit the model with the biomass concentration data. The offline experimental dry mass data were used for the parameter estimation procedure. It was observed that it is not necessary to estimate more than one parameter to fit the model which results in a unidimensional optimization procedure of the error function. Future work will be focused on estimating the parameters that will fit the global photosynthetic growth model on the online data of dissolved oxygen, dissolved carbon dioxide and pH.

In this paper, we focus on the design and implementation of a deep learning based model for the identification and control of stable second order mechanical systems. To do so, we propose a model composed of two neural networks. A first neural network performs the identification of the mass, the elastic and damping constants of the system from its open-loop response. After that, a second network predicts the PID controller parameters which are necessary for the correct operation of the system. The proposed system is able to estimate process parameters with a reduced error and provide a resulting PID controller offering a satisfactory system response.

This study addresses the effect of climate conditions on treatment effectiveness and energy consumption in a conventional Wastewater Treatment Plant (WWTP). It has been observed that winter temperatures below 12°C produce a deterioration of pollutants elimination and energy efficiency in the activated sludge process (ASP). Then, in this work, variations of climatological conditions are considered, and ASP control parameters are modified, to evaluate their effect into the eco-efficiency of the WWTP operation. The eco-efficiency of the operation is analyzed from a plant-wide perspective considering the effects on different units of the plant. The Benchmark Simulation Model 2 (BSM2), that represents a typical WWTP, is selected for simulations. Introduction of seasonal temperature effects on ASP control strategy are contemplated for future work.

Data science has been attracting a great deal of attention in recent years because of its promise to extract value from the data that abounds in society. In the control engineering field, data-driven design, in which control system design can be performed directly from data, can also be considered a part of data science. Using the data-driven approach, a control system can be optimized directly from controlled data. However, even if a system is optimally designed, its behavior cannot be verified until the system is actually controlled. Therefore, in the present study, a data-driven response estimation-based tuning (DRET) is proposed in order to design a control system based on the estimated time response. It is applied to the control system design of not only stable systems but also unstable systems. In the design of DRET, a finite impulse response filter is used and a model matching problem is solved directly from the control data, to compensate for the difference between an original objective function and a data-driven objective function. The proposed method is applied to the control system design of a ball-and-beam system, which is an unstable system, and its usefulness is verified.

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

This paper deals with the increasing of the operation efficiency of a wastewater treatment plant by reducing the level of pollutant concentration in the effluent. In essence, the main goal is the reduction of the concentration of organic substrate, Model Predictive Control algorithm being used for this, considering as a model of the wastewater treatment process a neural network model based on a simplified mathematical model of the 4th order. For the implementation of the control algorithm, two neural networks were trained. In the first version of the neural network, it was considered that all 4 state variables are measurable and in the second version, only the organic substrate was considered measurable. Regarding the training of the two neural networks, good results were obtained in the case of both versions. Instead, the control algorithm gave better results when the second neural network was used (lower values of the substrate, lower variations of the aeration rate, which means a lower cost of operation).

In this paper, we address the design of data-based Artificial Neural Networks (ANN) controllers. More specifically, we consider a scalable design based on a Transfer Learning approach where an ANN controller trained at a given source scenario is transferred to other target domains. In order to properly assess the transfer suitability of the controller, the adoption of a Transfer Suitability Metric (TSM) is required. And here resides the main goal of this paper: to develop a TSM able to measure the amount of information captured by a neural network to estimate a desired output from input data. To do so, we resort to Mutual Information (MI) studies addressing the learning process in a neural network. As shown in the paper, we propose a MI-based metric able to assess the transfer suitability while reducing metric computation complexity.

The world class mining industry is operating mines under fundamental parameters as safe work, care environmental, social commitments, and sustainable production. Based on named parameters, this paper exposes an initial statement analysis of main variables of critical equipment from operational and maintenance aspects, showing what data better represents the operational condition of equipment; what data involves maintenance analysis; what data can create an operational or environmental risk. The analysis is conducted in order to further continue by creating models to predict future behaviour and to allow for a fast response from operator and the control system itself. One of the main goals of the research includes Machine Learning applications, with the goal to interact in easy way with operators and responsibles of maintenance, teaching and training them regarding interest variables taking advantage of the industrial PI System platform with all its resources.

In the last years, the industrial digitalisation and the Industry 4.0 paradigm is no longer a fairy-tale but a reality. It is becoming more common to find industrial environments relying and adopting data-based approaches to perform some sorts of processes. Some of them are related to the industrial control, where the incursion of Artificial Neural Networks (ANNs) is promoting the usage of data-based solutions to substitute conventional control structures. Besides, one of the greatest issues related to the ANN time-consuming training process has been alleviated by means of Transfer Learning (TL) methods. However, in the industrial control domain TL cannot be freely adopted since the final performance of the transferred control structure cannot be known before substituting the conventional structure. This is an issue that needs to be tackled, especially in critical industrial scenarios where an incorrect control can produce huge disasters. For that reason we present here the Transfer Suitability Metric (TSM). Based on the environments similarities, its main aim is to compute the transference suitability of ANN-based controllers in order to transfer the ANN to the target domain without resorting to new control design and optimization. It provides the plant operators with an insight of the controller behaviour before it is finally substituting the conventional control structure. Results have shown that the metric is highly correlated with the final control behaviour in the sense that the higher the metric, the better the final ANN-based controller performance.

Cascade control structures are widely implemented in industries, due to the fact that they increase the performance of single-loop control. Nevertheless, their implementation requires a higher initial investment. Because of this, it is of great importance to be able to quantify their relative performance improvement. Very few studies handle this subject, and they do not obtain results which directly establish this quantification on a general basis. Therefore, the objective of this project is to conduct a study with help of computational tools, in order to determine the relative performance improvement of a cascade control structure with respect to a single-loop structure, for setpoint tracking and disturbance rejection. An overdamped second-order-plus-dead-time process, separable in two first-order-plus-dead-time subprocesses, was considered. The controllers were implemented using a PID algorithm. Simulations were conducted to obtain the ratio of the IAE performance indexes of each control structure, as a function of the time constant ratio, for different process parameters. The controllers were tuned by means of optimization routines, in order to guarantee that they provided the best IAE index. Finally, two general equations were obtained, which quantify the relative performance improvement of a cascade control structure with respect to a single-loop structure, as a function of the process parameters, for set point tracking and disturbance rejection.

To increase the biogas production, the anaerobic digestion process requires advanced control tools. However, in order to achieve an advanced control of the anaerobic digestion process, two constraints must be taken into account: the need for available plants for experimentation and the need to reduce costs. In this paper, it is proposed to develop a test platform for the anaerobic digestion process using the Hardware in the Loop Simulations principle. The emulation of the anaerobic digestion process is ensured by a computer that communicates with a data acquisition board, and the process control is ensured by a Programable Logic Controller. Such a platform is effective because it allows testing in extreme situations, which in the case of environmental processes is very difficult. The platform also includes a cloud computing component for advanced data processing.

The incursion of the Industry 4.0 paradigm and the Artificial Neural Networks (ANNs) is changing the way as the industrial systems are conceived and controlled. Now, it is more common to talk about data-driven methods either supporting conventional industrial control strategies, or acting as the control itself. Thus, one can find that in the last years it is more common to find control systems which are purely based on data leaving aside the highly complex mathematical models. However, data-driven models and ANNs have to be correctly trained in order to offer a good performance and therefore, be contemplated as the core part of a control strategy. This can become a time-demanding and tedious process. For that reason, Transfer Learning (TL) techniques can be adopted to ease the conception, design and training processes of the data-based and ANNs methods, since the efforts have to be mainly focused on training a unique net which will be then transferred into the other scenarios. In that sense, we present here a TL approach to design and implement the whole control of a Wastewater Treatment Plant (WWTP). First, the control of the quickest dynamics under control is performed by means of a Long Short-Term Memory cell (LSTM) based Proportional Integral (PI) controller (LSTM-based PI). Once the LSTM is trained and tested, its knowledge will be transferred into the remaining WWTP control loops. In that way, an ease and reduction in the time involved in the design and training of the control as well as in its complexity is achieved. Results have shown a twofold achievement: (i) the LSTM-based PI achieves an improvement of the control performance with respect to a conventional PI controller around a 93.56% and a 99.07% in terms of the Integrated Absolute (IAE) and Integrated Squared (ISE) errors between the desired measurement and the obtained one, respectively, and (ii) the LSTM-based PI controller achieves an average improvement in the IAE and ISE around a 9.55% and 15.25%, respectively, when it is transferred into a different WWTP control loop.

Chlorination in drinking water treatment plants (DWTP) is the final process applied to water before it is sent to storage tanks in the supply network for subsequent human consumption. An excessive dosage of chlorine or, conversely, too small a dosage, may breach existing legal regulations on mandatory limits. In DWTP where there is no significant variability in the quality of the water to be treated, a type of control that is proportional to the flow rate in the effluent can have fully satisfactory results. Therefore, Proportional-Integral (PI) control is a rather frequently used solution. However, when there are inherently long delays in the process, variability in the quality of the water to be treated and considerable variations alternative type is needed. This article presents the strategy and results of a control method that proposes a Fuzzy based feed-forward system to complement an existing PI control. The control system results are shown as applied to the DWTP of Barcelona city, producing satisfactory experimental results.

Wastewater Treatment Plants (WWTPs) are industries where highly complex and non-linear processes are performed to reduce the pollutant concentrations of residual waters. However, some nitrogen and phosphorus derived pollutants are generated in these processes. As a consequence, certain control strategies have been developed to maintain these pollutants under certain limits. Benchmark Simulation Model No.1 (BSM1), a framework emulating the behaviour of a general purpose WWTP, considers a default controller strategy based on Proportional Integral (PI) controllers. Nevertheless, these controllers are based on linearised models of the WWTP behaviour. For that reason, this work proposes a new control approach based on Internal Model Controllers (IMC) adopting Artificial Neural Networks (ANNs), which are able to model the real plant behaviour without performing linearisation. Results show that the proposed IMC is improving the default controller performance around a 16% and a 53% in terms of the Integral Absolute Error (IAE) and the Integral Square Error (ISE), respectively.

The variable frequency has an important usage in industrial applications. Electrical energy produced by Power Stations is normally 50/60 Hz and is not applicable for many domestic and industrial applications. There are some electrical and electromechanical devices, which need variable frequency than the fixed power supply frequency. Permanent Magnet Synchronous Motor is one of the best examples of variable frequency drives. The Permanent Magnet Synchronous Motor is widely used in the low to medium power system due to its characteristics of high efficiency, high torque to inertia ratio, high reliability, and fast dynamic performance. The Permanent Magnet Synchronous Motor and variable frequency drive have the large sum of demand in industrial and power generation applications. The frequency converter is such device, which generates the variable frequency. In this paper, the vector control of Permanent Magnet Synchronous Motor fed by a frequency converter is modelled and simulated by using PI controller and Fuzzy controller, one of the intelligent methods used in electric drives. Waveforms derived from the simulation of the vector control of Permanent Magnet Synchronous Motor fed a frequency converter are examined comparatively for PI and Fuzzy Controllers. Thus, this project strongly recommends the frequency converter for Permanent Magnet Synchronous Motor application using a Fuzzy controller.

Disturbance attenuation is often recognized as the primary concern of a control system. Regulation of the operating conditions is the usual task work to be pursued by a feedback controller. However, much of the academic works almost concentrate on set-point experiments for controller evaluation. Even those that explicitly concentrate on the disturbance attenuation problem, usually concentrate on input load disturbances. They exclude the specific, but usually not considered in the literature, case when the path from the disturbance to the controlled variable is different to the path from the controller output to the controlled variable. There are some process units such as heat exchangers or distillation columns where this situation may be encountered. However actual feedback controllers, usually of PI/PID type, do not take into account this situation. The point raised in this paper is that an increment of performance can be obtained if the information regarding the disturbance dynamics is included in the PI/PID design stage.

This work faces the problem of frequency deviation in microgrid systems. The considered microgrid includes renewable energy sources such as wind and solar photovoltaic. As long as these sources provide an irregular power supply or there is a sudden change in the system load, the power system frequency deviates. In order to compensate such deviations, alternative, conventional energy sources should be commanded in order to provide the corresponding power deficit. In this paper a very simple and of common industrial practice control approach such as the Internal Model Control based on first order plus time delay models is proposed within an event-based framework. As the commanded energy sources are based on fuel consumption, the control usage has to be maintained at low levels. It is shown that the event-based approach is able to provide accurate frequency deviation control with very low movements for the control signal. Time domain simulations show the effectiveness of the approach as compared with other more sophisticated controllers already proposed in the literature.

In this paper, a One-Degree-of-Freedom PI controller is optimized using the model reference tuning approach for a Second Order Inverse Response and Dead Time Process operating as a servo control. In addition, a graphic user interface tool that computes the PI optimized controller parameters is presented, also showing the response of the control system operating as a servo-control (the optimized one) and the associated response for the regulatory-control case.

The control of low power systems, which include renewable energy sources, a local network, an electrochemical storage subsystem and a grid connection, is inherently hierarchical. The lower level consists on the control systems of wind sources (power limitation at rated value in full load regime and energy optimization in partial load regime) and photovoltaic (energy conversion optimization). The control problem at the higher level is treated in this paper and aims at generating the control solution for the energetic transfer between the system components, given that the powers of the renewable energy sources and the power in the local network are random variables. For the higher level, the paper proposes a mixed performance criterion, which includes an energy sub-criterion concerning the costs of electricity supplied to local consumers, and a sub-criterion related to the lifetime of the battery. It was defined an algorithm to control the energy transfer in the system, implemented by using fuzzy techniques and a deficit/surplus prediction of energy in the system.

This paper deals with the identification of fractional models with one fractional parameter. This kind of models are capable to represent an extensive range of dynamics, including overdamped and oscillatory behaviors. The identification algorithm consists in applying an optimization function, starting from an initial point, that allows the program to calculate a very representative model of the process. The results demonstrate the usefulness and robustness of the tool, which can be employed to identify integer and fractional systems in an easy way and this can be later exploited for further studies, for example the development of tuning rules.

In this work, an analysis of multi-objective optimization methods for PI controllers tuning is presented using the Pareto front concept. A new multi-objective method is proposed to find an evenly spaced Pareto frontier which, at the same time, gives the user an idea of the degradation of one of the objective functions. The results are analyzed from a control theory perspective, which include the comparison with classical PI tuning methods.

An multistage approach is proposed merging a deterministic and evolutionary algorithm for PI controller tuning. This technique is formulated through design of a multi-objective optimization procedure, to ensure the construction of Pareto frontier that guarantee well distribution and exclude the non-Pareto and local Pareto points. This procedure focuses on reliability-based optimization instances. To validate the approach, we will consider the Boiler Control Benchmark. The results of its usefulness for controller tuning is demonstrated.

The aim of this paper is to present the application of a model reference design procedure to the robust tuning of two-degree-of-freedom proportional integral derivative controllers with filter for control of unstable controlled processes. The design is based on the use of an optimization procedure with servo and regulatory closed-loop transfer functions targets. Due to the constrains imposed by the unstable processes the robust design is based in the maximum obtainable robustness with the selected control system response target. Controller tuning equations are provided for unstable first-order plus dead-time models. The design procedure considers at the same time the five parameters of the controller including the filter time constant. Equivalent parameters and a tuning rule for Standard PID controllers are also presented.

This paper presents the application of the NNC method to the tuning of PI controllers. The main contribution is on the analysis of the tradeoff among different performance indexes as well as the need of considering the robustness as another tradeoff. Robustness has been included during last year's. However, the authors do question if it is needed to include an explicit robustness measure or is better to find its correlation with another performance-like figure of merit. The use of specific compromise criteria to select an unique solution from the Pareto front generates a possibility for tuning a PI control that generates better system outputs than existing tuning methods.

The aim of the paper is to present tuning equations for proportional integral (PI) and proportional integral derivative (PID) controllers with set-point weight (two degrees off freedom - 2DoF). These are based on a performance/robustness trade-off analysis with first- and second-order-plus-dead-time (FOPDT, SOPDT) models. On the basis of this analysis a tuning method was developed for 2DoF PI and PID controllers that allow designing closed-loop control systems with a specified MSrobustness that at the same time have the maximum IAE performance allowed. The control system robustness is adjusted varying only the controller proportional gain.

The aim of the paper is to present two-degree-of-freedom (2DoF) proportional integral derivative (PID) control algorithms and conversion relations between their parameters. The Ideal PID with filter (PID2F) is the more general PID controller. Restrictions to obtain equivalent 2DoF Standard or Series PID controllers are presented taking into account the derivative filter constant. Examples are used to illustrate when or when not equivalent controllers exist.

In this paper ten control strategies are tested, starting from a default tuning of the controllers, to evaluate its results in terms of general plant performance indicators such as the effluent quality index (EQI) and the operating cost index (OCI). Using the Benchmark simulation No 1 (BSM1), many simulations are done to determine the most sensitive parameter of the proportional-integral (PI) controllers implemented. The most sensitive parameter is selected by evaluating the influence of the proportional gain (Kp) and the integral time (T) in the general performance of the plant when they are changed into the proposed ranges. From the simulation study, Ti is selected as the most sensitive parameter. A comparison between the results in terms of EQI vs OCI graphics, varying Kpand Ti and the results varying only Ti, shows similar behaviours on the plant performance for all strategies.

The five papers in this special section focus on industrial control.

This work presents a Data-Driven control applied to a wastewater treatment plant in a simulation study. The Benchmark Simulation Model 1 (BSM1) is used as a benchmark to compare the obtained results. It was found that similar results from the default controllers are achieved, without the need of any simplification of the model, and using only data from the direct simulation of the process. The Virtual Reference Feedback Tuning method is applied in a Two Degrees of Freedom Proportional-Integral (PI) control for the Dissolved Oxygen and Nitrate Nitrogen control loops, adding a constraint in the optimization problem to guarantee that the discrete version of the PI preserves the characteristics of the analog counterpart.

This communication addresses the setpoint robust PID tuning for stable first order processes with time delay (FOPTD) from a general min-max model matching formulation. In order to get a standard PID compensator, several choices are possible. This work considers the problem of finding the simplest one, based on conveniently adopting an approximate delay-free model for the FOPTD along with a particularly simple instance of the general model matching problem. The adopted methodology leads to a PID tuning just depending on a single parameter. Attending to common performance/ robustness indicators, this parameter is finally fixed in order to provide an automatic tuning just depending on the model information.

This paper introduces the stability analysis for the so-called intermediate-tuning PID controller. Thus, each of the three PID parameters is proposed to lie within the convex hull of two extreme values, being defined as the optimal tuning settings for servo and regulation modes respectively. The result ensures that all the PID controllers generated for each frozen value of the interpolating parameters guarantee the stability of the closed-loop.

This paper analyzes optimal controller settings for controllers with One-Degree-of-Freedom (1-DoF) Proportional-Integral-Derivative (PID) structure. The analysis is conducted from the point of view of the operating mode (either servo or regulation mode) of the control-loop and tuning mode of the controller. Performance of the optimal tuning settings can be degraded when the operating mode is different from that selected for tuning and obviously both situations can be present in any control system. In this context, a Performance Degradation index is minimized and based on this minimization, an autotuning procedure as a function of the normalized process dead-time is proposed.

This paper focuses on the estimation of the non-measurable physical states of wastewater systems when nonlinear models with uncertainties describe the processes. The activated sludge process (ASP), as the most commonly applied biological wastewater purification technique, attracts a great deal of attention from the research community. We developed for this class of processes a state dependent differential Riccati filter (SDDRF) for state estimation of nonlinear model describing the system. The resulting software sensor is simple to implement and has a relatively low computational cost. The results are compared with the extended Kalman filter (EKF) in order to demonstrate the better performance of the SDDRF filter. The filter allows the on-line tracking of process variables, which are not directly measurable. The simulation results point out to the advantage of using this approach.

Human-Machine-Interfaces are with no doubt one of the constitutive parts of an automation system. However, it is not till recently that they have received appropriate attention. It is because of a major concern about aspects related to maintenance, safety, achieve operator awareness, etc has been gained. Even there are in the market software solutions that allow for the design of efficient and complex interaction systems, it is not widespread the use of a rational design of the overall interface system. Specially for large scale systems where the monitoring and supervision systems may include hundreds of interfacing screens. It is on this respect hat this communication provides an example of such development also by showing how to include the automation level operational modes into the interfacing system. Therefore allowable for monitoring.

Data-driven control is a methodology that attempts to find a suitable controller, based only on data taken from the system. Within this method, the virtual reference feedback tuning (VRFT), is a one-shot data-driven approach that transforms the control problem into an identification problem using restricted complexity controllers. However, how to choose the number of parameters, or the relation between them is a subject that often is left apart. In this paper, the VRFT framework is applied to an alternate two-degrees-of-freedom (2DoF) structure and a ¿covariance test¿ is used in order to find the number of parameters needed. As it was expected, this test shows that the number of parameters is dependent on the way the controller is parameterized. A numerical example is shown at the end of the paper.

This paper proposes a multi-loop decentralized control strategy for the control of a wastewater treatment plant based on an activated sludge (ASP) model. The ASP is a described by means of a nonlinear model and results on a two-input two-output multivariable system. Even though advanced control strategies have been presented in the literature, the proposal of this paper is to show that by appropriate tuning of decentralized PI controllers, it is possible to get comparable performance as with other approaches. With this purpose the paper proposes a way of addressing the design of the decentralized controllers as well as a regulation based tuning for a PI controller. Simulations are carried out on the nonlinear model showing the performance of the proposed approach.

This paper presents an analytically deducted procedure for tuning two-degree-of-freedom proportional integral (PI) controllers for first-order-plus-dead-time (FOPDT) controlled process. The equations incorporate a design parameter, which relates the feedback control systempsilas time constant, with the controlled process time constant. The design procedure considers the control-loop robustness by means of maximum sensitivity requirements, allowing the designer to deal with the performance-robustness trade-off.

This paper analyzes optimal controller settings for controllers with One-Degree-of-Freedom (1-DOF) Proportional-Integral-Derivative (PID) structure. The analysis is conducted from the point of view of the operating mode (either servo or regulation mode) of the control loop and tuning mode of the controller. Performance of the optimal tuning settings (ISE-like) can be degraded when the operating mode is different from the select one for tuning. An index for measuring the overall Performance Degradation is proposed and from the minimum of this, tradeoff tuning settings are given. The proposed procedure is applied to control a Continuous Stirred Tank Reactor (CSTR), which is a non-linear system.

The proper tuning of multivariable PID controllers is not a straightforward task. This is specially true when data driven models and controller design methodologies are adopted to increase the industry acceptance of controller implementation and commissioning. On the other hand there are well established model based design methodologies which provide controllers of higher complexity. In this paper we propose a tuning methodology based on a PID controller matching to a figure of merit. Thus the PID tuning parameters are selected to minimize certain error metric when compared with an optimal controller. Moreover the index to be minimized is a residual that can be obtained from data. As a result it is possible to validate the proximity of the PID controller to the desired figure of merit in the frequency domain from data.

One of the main drawbacks of the Smith predictor (SP) for controlling systems affected by external delays is that the resulting performance relies on the accuracy of the knowledge regarding such delays. Thus, as the mismatch between the true value of the delay and the nominal one employed for control purposes increases the closed loop performance deteriorates accordingly. This point is particularly remarkable when dealing with multivariable plants in which strong coupling between the different input-output channels may exist. In this communication an intelligent control scheme aimed at the control of multivariable plants with uncertain external delays is proposed. It arises as the result of combining the basic SP with a supervised multiple model architecture that tackles the delay uncertainty.

In this paper a general two-step design procedure for robustness enhancement aimed at finding stable stabilizing (strong stabilizing) controllers for LTI systems is presented. The proposed approach attemps to deal with strong stabilization issues and recent linear robust control design techniques in a unified way.

Robustness and performance analysis for a control system with a PID controller tuning from optimal settings are presented. Those analysis are conducted from the point of view of the stability margins characterization and of the operating mode (either servo or regulation mode) of the control loop and tuning mode of the controller, respectively. It is well known that, specially for optimization based settings, the performance of the control loop is defined in terms of the expected operating mode of the control loop. When the control system is not operating in the same operating mode as the controller was tuned, the performance may exhibit very poor results. The performance degradation with respect to the optimal performance is defined and analyzed for settings based on ISE-like optimization criteria. As a consequence, in order to get a minimal overall performance degradation with respect to both operating modes, tradeoff settings are proposed and the stability margins for the transition between both servo and regulation tunings are obtained.

This paper considers the design of feedforward controllers when model uncertainty is present. The main contribution is an alternative approach to the generation of the feedforward control action on the basis of the Internal Model Control formulation. This new structure allows for completely independent tuning of the feedback and feedforward controllers and provides an explicit expression for the achieved nominal performance degradation when the uncertain case is considered. The formulation of the feedforward controller as an Internal Model Controller allows existing design approaches to be applied and uncertainty effect taken into account by means of the corresponding analysis equation.

This communication addresses the tuning of PI and PID controllers on the basis of the IMC approach. The tuning is based upon a first order plus time delay (FOPTD) model and aims to achieve a step response specification. It is analyzed that by using the IMC approach we get a PI or a PID depending on the kind of approximation used for the time delay term. This paper raises the question that the use of a PID instead of a PI controller should obey to another reason more related to the control objectives rather than the use of a better approximation for the time delay. An alternative tuning is presented that, even got within the IMC formulation is based on a min-max optimization. From the tuning rule provided by this approach the optimum settings from an integral squared error (ISE) criterion point of view are derived. The optimal controller results to be a PI controller. From this optimal controller as the starting point, the introduction of the derivative action can be seen as a detuning procedure that can increase the controller robustness. The approach provides further insight into the tuning of PI and PID controllers giving the (alternative) parameters a precise and engineering meaning.