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This paper presents a robust observer-based leader-following consensus control for multi-agent systems subject to external disturbances. The stability and robustness to external disturbances are addressed through the Lyapunov approach and an optimal H criterion. It is shown that the design conditions for a correct convergence of the estimated states and consensus control can be expressed in a set of linear matrix inequalities whose solution allows computing the observer and controller gain matrices. In order to show the effectiveness of the proposed strategy, numerical examples are carried out to solve the formation control problem of a fleet of unmanned aerial vehicles (UAVs) under the effect of wind turbulence.

This paper presents an experimental platform for regulating the DC motor angular speed powered by photovoltaic cells. The experimental platform comprises an Eco Green Energy EGE-260P-60 solar panel, DC/DC SEPIC converter, DC bus, DC/DC buck converter, DC motor and Nexys 4 board with an Artix-7 100T FPGA. The DC/DC SEPIC converter is used for harvesting the maximum amount of energy from the PV cells using the perturb and observe algorithm to track the maximum power point. The DC/DC buck converter is used as the motor drive using the active disturbance rejection control to regulate the angular speed of the DC motor. In addition, the FPGA architecture design is presented using a hierarchical top-down methodology with the VHDL hardware description language and Xilinx System Generator tool. The software takes advantage of the FPGA’s concurrency to simultaneously evaluate the different processes, which is the main reason for choosing this digital device. Several tests were performed on the platform such as irradiance changes, DC bus variations, DC motor connection and load torque variations applied in the motor shaft. The results indicate that the maximum power is obtained from the photovoltaic cells, establishing the minimum operating conditions. In addition, the control approach estimates and cancels the effects of disturbances caused by variations in the environmental conditions, photovoltaic system, DC bus, and load changes in order to regulate DC motor speed.

In this paper a system of fault detection and isolation (FDI) in sensors of boiler following control of a thermal power plant is presented. The approach is based on a polytopic quasi-Linear Parameter Variant (q-LPV) model derived from a simplified nonlinear model for the boiler-turbine unit representing the dynamic behavior of the pressure in the first stage of the turbine, drum pressure, superheated steam pressure and electrical power. The nonlinear model is parameterized with real data operation of a thermal power plant, in a wide range of loads (low, medium and high). For residual generation a Luenberger-like observer based on the polytopic q-LPV model is designed. The performance of the FDI system is evaluated adequately by using a simulator in Simulink based on the nonlinear model. The main contribution of this work is to demonstrate that our proposed approach is feasible to be applied in a real thermal power plant.

In this paper, a state feedback control based on eigenstructure assignment for double-pipe heat exchanger is designed and implemented. The control system is designed to be robust to parametric uncertainties in order to achieve the regulatory control objetive, in this case the control objetive is to regulate the outlet temperatures of the heat exchanger. The hot inlet temperature is considered as the unknown uncertaintly parameter in the heat exchanger. The main contribution of this paper is the design and the implementation of a robust control based on eigenestructure assignment applied to an experimental double-pipe heat exchanger prototype of one cell. Finally, simulation and experimental results are done in order to illustrate the effectiveness of the proposed robust controller

In this study, a sensor fault detection and isolation (FDI) system is presented for a boiler-turbine unit of a thermal power plant in Mexico. The FDI system is based on a Luenberger-like observer for residual generation. An adaptive threshold for residual evaluation is considered to avoid false alarms. The observer is based on a quasi-linear parameter variant (quasi-LPV) model of the boiler-turbine unit parameterized with real data of the plant in a wide range of operations, namely, at low, medium, and high loads. The quasi-LPV model adequately represents the dynamics of more critical variables including first stage turbine pressure, superheated steam pressure, drum pressure, and electric power. The performance of the FDI system is evaluated in a practical scenario by using real data from the thermoelectric plant. The main contribution of this study involves proposing a reliable fault diagnosis system to detect sensor faults in a wide operational range of the process based on the quasi-LPV framework.

The subspace identification methods (SIMs), such as past outputs multivariable output-error state space (PO-MOESP), numerical algorithms for subspace state-space system identification, and canonic correlation analysis, build state-space models from a set of input–output data of a linear time invariant system. In these algorithms, the key step is the computation of the extended observability matrix, which is done via an orthogonal projection between spaces generated by the input–output data. However, the PO-MOESP algorithm, unlike the others subspace methods, uses a different space for computing such projection. This fact hinders the analysis and direct comparison with other algorithms. In this paper, we introduce a modified version of the PO-MOESP algorithm, which is conceptually simpler, requires fewer steps for computing the model parameters, and enables the comparison with other SIMs. Three numerical examples are provided in order to show the effectiveness of the proposed approach.

In this work, we present the design of a Fault-Tolerant Control (FTC) system, applied in the outlet temperature sensors and in the control valve (actuator) of a concentric double pipe counter-current flow heat exchanger. The FTC consists in a sensors Fault Detection and Isolation (FDI) system and an actuator FDI system. The sensors FDI system is based on analytical redundancy, in such a way that a bank of modified Kalman filters is developed in order to estimate the two outlet temperatures of the heat exchanger. To develop the modified Kalman filters a multi-linear models approach is used. So that, if a sensor fault is detected by the FDI system the measured temperature signal is replaced by the temperature estimation provided by the modified Kalman filter. Moreover, to detect an actuator fault a comparison between the control valve behaviors (the control valve voltage is used to estimate the water flow rate) and a predefined flow rate for each linear model is carried out. In order to keep the continuous operation of the heat exchanger even in fault presence a model-following control law is introduced, such that, when an actuator fault occurs, the FDI system detect the fault and immediately the model-following control makes the fault accommodation in order to compensate the actuator fault. The proposed scheme is presented with experimental data on-line. The successful tests are presented and discussed.

The PO-MOESP algorithm is a subspace identification method that employs the extended observability matrix for building state space models. In this algorithm, the matrix Oe is computed from an orthogonal projection onto the null space of input data. However, in order to achieve a consistent estimation of Oe is necessary to employ a special matrix called instrumental variable. In this paper, we propose a different operation scheme of the PO-MOESP based on the calculation of an alternative null space of input data. This approach is conceptually simpler and avoids the explicit use of the instrumental variable matrix for computing Oe.

This paper presents the implementation of a Digital Artificial Neural Network (ANN) of type Multilayer Perceptron trained for data classification corresponding to the output characteristic of a transistor MOSFET. The ANN digital was build and training with an adequate number of neurons and samples for achieve an approximate of 90% correct data classification of the transistor MOSFET.

The subspace identification methods or 4SID methods are known for their ability to construct mathematical models in state space from the input-output data of an LTI system. In these algorithms, the calculation of extended observability matrix is the step that receives the greatest attention, and generally, the performance of the algorithms is evaluated based on this matrix parameters. In contrast, the step of calculating the matrices of the system often does not receive the same attention, but their influence on the quality of the resulting model may be remarkable. In this paper we describe the principal techniques for calculating the system matrices and analyze their effect, via simulation, on the resulting mathematical model.

This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system's outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results.

This paper proposes a volumetric gas meter, on-line, based on the liquid displacement principle. The proposed measuring device is the capacity to measure volumetric gas in a range from 1 mL to 1000 mL, from a laboratory-scale biogas reactor. As application, the meter is used in order to obtain the biogas production data file of a laboratory-scale Upflow Anaerobic Sludge Blanket (UASB) reactor.

SUMMARY In this work, a generalized design scheme for the output feedback regulation of a special type of systems with bounded inputs is proposed. It gives rise to a simple dynamic controller that guarantees the regulation objective avoiding input saturation, for any initial condition within a specific set that may comprehend the whole state space and that does not require any additional system data (apart from the output variable). Several processes, like double-pipe heat exchangers, bioreactors, and binary distillation columns, are shown to be part of the type of systems that may be regulated through the developed methodology. The efficiency of the proposed scheme is corroborated through experimental and simulation results. Copyright © 2011 John Wiley & Sons, Ltd.

The purpose of this paper is to present a simple neuro-control law in order to control a geared DC motor. The main advantage of this controller is that it does not require an exact knowledge of the values of the motor parameters. The proposed artificial neural network is characterized by two input synaptic weights, two output synaptic weights and one threshold; these parameters are used to define the performance of the closed loop system. The DC motor parameters, the synaptic weights and the ANN threshold are combined in order to construct an off-line learning condition. Such condition guarantees that the seminorm of the regulation error remains bounded (closed loop performance index) and it is constructed through a Lyapunov-like analysis. The neuro-controller is evaluated through numerical simulations and through small-scale laboratory experiments by implementing the neuro-controller with electronic hardware.

In this article, a toolbox based on a monitoring and control interface (MCI) is presented and applied in a heat exchanger. The MCI was programed in order to realize sensor fault detection and isolation and fault tolerance using virtual sensors. The virtual sensors were designed from model-based high-gain observers. To develop the control task, different kinds of control laws were included in the monitoring and control interface. These control laws are PID, MPC and a non-linear model-based control law. The MCI helps to maintain the heat exchanger under operation, even if a temperature outlet sensor fault occurs, in the case of outlet temperature sensor failure, the MCI will display an alarm. The monitoring and control interface is used as a practical tool to support electronic engineering students with heat transfer and control concepts to be applied in a double-pipe heat exchanger pilot plant. The method aims to teach the students through the observation and manipulation of the main variables of the process and by the interaction with the monitoring and control interface (MCI) developed in LabVIEW©. The MCI provides the electronic engineering students with the knowledge of heat exchanger behavior, since the interface is provided with a thermodynamic model that approximates the temperatures and the physical properties of the fluid (density and heat capacity). An advantage of the interface is the easy manipulation of the actuator for an automatic or manual operation. Another advantage of the monitoring and control interface is that all algorithms can be manipulated and modified by the users.

International audience; The aim of this study is to provide a full comprehensive study of the singular linear parameter-varying (LPV) systems and observer synthesis in a real process. The states and unknown inputs estimation problem for a binary distillation column is solved by using an LPV proportional-integral observer (PI-Observer). A singular LPV model of the process is developed. This model facilitates the design of the PI-Observer for singular LPV systems. The observer proposed is tested using experimental data of a distillation column separating an ethanol??water mixture. The results indicate that the observer is able to accurately reconstruct the product composition dynamics and the unknown inputs of the process.

International audience; In this paper, a fault diagnosis method is developed for systems described by multi- models. The main contribution consists in the design of a new Fault Detection and Isolation scheme (FDI) through an adaptive filter for such systems. Based on the assumption that dynamic behavior of the process is described by a multi-model approach around different operating points, a set of residual is established in order to generate weighting functions robust to faults. These robust weighting functions are directly linked with the adaptive filter effectiveness which provides multiple fault magnitude estimations for the whole operating range of the system. Stability conditions of the adaptive filter are studied and its performances are tested using an hydraulic system.

This article proposes a robust nonlinear control based on Lyapunov’s redesign, whose purpose is to deal with parametric uncertainty in the resistance of the motor windings. The robust controller design is based on the passivity properties of the motor, as well as energy shaping and damping injection. The application of this control technique is focused on electric vehicles mainly formed by a battery bank, a power inverter, an AC brushless motor and the mechanical transmission. The sine PWM technique is used to trigger the switching devices of inverter. The results were obtained from simulation, where is shown that robust control makes a proper tracking of electromagnetic torque.