Keywords: Control applications, Industrial control, Fuzzy and neural systems
Abstract: This work consists of modeling, simulating and finally control of an industrial absorption packed column designated to remove CO2, from natural gas. The neural network approach is used here for its efficiency coping with non linear systems. Two neural network strategies are used here, the certainty equivalence (CE) and the Neural Autoregressive Moving Average (NARMA). We compared the results with those obtained with the traditional PID control,and the results obtained are satisfactory.

Keywords: Control applications, Industrial control, Optimization
Abstract: This paper presents an iterative approach which provides optimal solutions of the best achievement performance in the case of closed-loop aged-based processes applied on semiconductor manufacturing industry. The aim is to provide an online parameter estimation of RLS-LT controller in the case of mixed product Run-to-Run (RtR) control. The online estimation is done without any a priori knowledge of process parameters and noise structure. The best achievement performance improvements is illustrated using a simulation problem from literature and the example is applied on the removal rate control in a Chemical Mechanical Polishing (CMP) process.

Keywords: Control applications, Modeling and simulation, Motion control
Abstract: In this paper a new adaptive control technique for path tracking of nonholonomic mobile robot is designed to deal with unknown skidding and slipping as well as external disturbances. The main contribution of the proposed controller consists on the estimation and attenuation of the effect of disturbances and unknown skidding and slipping simultaneously without the need of torque measurement. First, a desired velocity input is designed using Backstepping techniques. Then, an adaptive dynamic controller, which contains two adaptive laws, is proposed to guarantee good tracking performance. The system stability and the convergence of the tracking errors are rigorously proven using Lyapunov stability theory. Finally, simulation results are illustrated to show the effectiveness of the proposed controller in comparison with the classic sliding mode control.

Keywords: Control applications, Modeling and simulation, Power systems
Abstract: This paper proposes Interleaved Boost DC-DC converter suitable for renewable energy applications. The proposed converter utilises a novel adaptive delta modulation to control the pulse generator. A thorough and effective analysis of the converter is carried out in order to achieve the required system stability and to improve the dynamic performance. The output response characteristics obtained by this method is very much improved compared to the conventional PWM method of control and simplified sigma delta modulation method. Simulation results are presented to illustrate the advantages of the proposed scheme. The performance of the DC/DC converter system with ADM control loop provides a number of features that do not exist in today's electrical systems like very vast response. All the advantages of interleaving, such as higher efficiency and reduced ripple for voltage/current, are also maintained in the proposed converter.

Keywords: Control applications, Multivariable control, Power systems
Abstract: This paper presents the use of a higher order sliding mode scheme for sensorless control of induction motors (IM). The sliding mode approach is used both for control and for generating the rotor speed and the rotor flux estimates necessary for the controller, based only on the measurements of stator voltages and currents. The second order sliding mode control (SOSMC) is based on a super-twisting algorithm. Then, the proposed control scheme and the observer are tested on an experimental setup confirming their convergence properties. The used observer converges in a finite time and permits to give a good estimate of flux and speed even in presence of rotor and stator resistance variations.

Keywords: Fault detection and Diagnostics, Manufacturing systems, Modeling of complex systems
Abstract: Nowadays, the semiconductor manufacturing process is becoming very complex, consisting of hundreds of steps before obtaining the final product. Continuous Yield improvement is required to meet the current competitive market needs. High yield can be achieved by monitoring the wafer quality in real time throughout the auto-correlated multivariate process. However, the missing quality measurements make the product quality control very difficult. In this paper, we present an approach to predict product quality by using both the available on-line measurements of the production equipment data and the information about correlations between product quality measurements across the different steps. For this purpose, a regularized regression technique is used to represent statistical relationships between the correlated product quality measurements and the high-dimensional sensor data.

Keywords: Modeling and simulation, Fuzzy and neural systems, Linear and nonlinear systems
Abstract: The principal aim of this work, is to ensure stability of continuous fuzzy chaotic systems. In fact, the examples choosen are represented via the Takagi-Sugeno fuzzy model. Then, the asymptotic stability is achieved throw the design of a fuzzy-model-based controllers, based on so-called « parallel distributed compensation » (PDC). The fuzzy controllers satisfying the stability are got by solving linear matrix inequalities (LMIs) using the numerical simulation on Matlab which verify the results needed. Finally, the generalized Lorenz hyperchaotic system GLHS and the Chen chaotic system are employed to illustrate the effectiveness of our designing controllers for ensuring their stability.

Keywords: Modeling and simulation, Image processing
Abstract: Gradex method is one of the most used methods in Morocco for estimating flood peaks in order to size hydraulic structures. The aim of this study is to model spatial variation of this parameter to elaborate its cartography on the Tensift Basin. The Map is elaborated using the geostatistical method of kriging. This tool is a general method of statistical interpolation that can be applied within any discipline to sampled data from random fields that satisfy the appropriate mathematical assumptions. In our case several reference functions (exponential model, spherical, linear, Gaussian and cubic) were used for modeling the kriging variogram. Cross validation enabled a comparison between the results of these models and choice of spherical model with anisotropy and trend fit by a second-order polynomial as the most suitable. The use of available series of annual maximum daily rainfall recorded at 23 rainfall stations, distributed over the Tensift basin, led also to develop the cartography of standard prediction errors’ values associated to the predicted parameter for each point of Tensift basin. These errors vary from acceptable values (16.8%) to very high ones depending on the density of the rainfall stations at the desired site.

Keywords: Modeling and simulation, Linear and nonlinear systems, Robust control and Hinfty control
Abstract: In this paper, the motorcycle lateral dynamic stabilization problem are studied by considering longitudinal velocity and cornering stiffness variations . Based on multiple model approach and a transformation technique, the motocycle nonlinear model is represented by two uncertain TS fuzzy systems. To obtain a good transient response of the closed loop system, the energy-to peak performance has been considered. The design conditions of controller are transformed on optimization problem under LMI constraints. Finally, simulation results are provided to show the effectiveness of the proposed design method.

Keywords: Modeling and simulation, Robust control and Hinfty control, Process control and instrumentation
Abstract: In this work, the piezoresistive accelerometer step and impulse responses are enhanced by using appropriate parameters (damping rate and frequency range). The proposed parameters provide stable and accurate accelerometer. Therefore, tests were conducted by computer simulation and the obtained results have showed the difference between the accelerometer with the proposed parameters and the accelerometer used in the experiment.

Keywords: Estimations and identification, Power systems, Modeling and simulation
Abstract: Different estimators are available for on-line parametric identification among which the Kalman filter (KF). This latter can be used in its traditional form (TKF) for the linear system or in its extended form (EKF) for the nonlinear system. Another interesting application of the Kalman filter is when it is biaised (BKF). The consideration of the bias makes it possible to reduce the mean squared error (MSE) between measured and estimated values of the system state variable. Thus, the normalized MSE (NMSE) can be decreased as well. Furthermore, standard deviation (STD) between real and estimated values of the parameter can be limited in the tolerable percentage. All these situations are discussed in this paper where the system under study is a salient-pole synchronous generator (SPSG). MATLAB/Simulink codes are performed to implement the different KFs and to demonstrate the effectiveness of the BKF in both steady and transient states.

Keywords: Motion control, Power systems, Control applications
Abstract: This paper describes the design of a new algorithm for the rotor resistance adaptation. The obtained adaptive observer is used for the improvement of the vector control of induction motor (IM) drives. The adaptive observer detects the rotor speed and flux components in the stationary reference frame using the stator voltages and currents. Stability analysis based on Lyapunov theory is performed in order to guarantee the closed loop stability. In addition, the estimated rotor resistance is used for correction of the controllers and the rotor time constant which is used in the calculation of the stator pulsation. Simulation tests under disturbances are provided to evaluate the consistency and the effectiveness of the proposed control technique in terms of robustness and dynamic performances.

Keywords: Power systems, Modeling and simulation, Intelligent and AI based control
Abstract: This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed system to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

Keywords: Power systems, Optimization, Linear and nonlinear systems
Abstract: With ever increasing load and insufficient growth of generation and transmission capacities, the operating constraints of modern integrated power system are increasing. It has led to ever increasing dynamic problems of low frequency oscillations in the system that needs to be detected and damped out quickly and efficiently. In electrical power networks small oscillations appear from time to time. These oscillations concern the quantities determining the equilibrium point of the system, and following which, system stability and system behaviors are influenced. The objective of our study is to check the static stability of the high voltage power to small perturbations of electrical network. In this context, after linearizing the system, power system stabilizer (PSS) has so far been extensively utilized to mitigate these problems. This paper presents a novel and efficient approach for the optimal tuning of power system stabilizer parameters (PSS) using a genetic algorithm (GA) with the eigenvalue-based objective function. The proposed approach is implemented and examined in a system with a single machine connected to an infinite bus via a transmission line. The results of this technique have been verified by eigenvalue analysis and time-domain simulations. The obtained results were evaluated and compared with ones obtained by ordinary GAs. Eigenvalue analysis and nonlinear system simulations demonstrated the effectiveness of the proposed approach in damping the electromechanical oscillations and enhancing the system dynamic stability. Keywords—genetic algorithms, objective optimization, single machine (SIMB), power system stabilizer, modal analysis.

Keywords: Power systems, Optimization, Modeling and simulation
Abstract: this paper introduces a differential evolution algorithm as an optimization technique for the tuning of the power system stabilizers (PSS) parameters. PSS have been widely used to damp low frequency electromechanical oscillations caused by small signal stability issues. The tuning of the PSS parameters is formulated as an objective with constraints including the damping ratio of power system oscillatory modes. The application of the proposed procedure to the New England New York test system has illustrates the performance of the differential evolution algorithm to enhance the small signal stability

Keywords: Fuzzy and neural systems
Abstract: This paper deals with the problem of stability and stabilization of Takagi-Sugeno (T-S) fuzzy systems with a fixed delay by linear programming (LP) while imposing positivity in closed-loop. The sufficient conditions of stabilization are derived using the single Lyapunov-Krasovskii Functional (LKF). An example of a real plant is studied, and the comparison between LMI and LP approach is presented to show the advantages of the design procedure.

Keywords: Fuzzy and neural systems, Intelligent and AI based control, Control applications
Abstract: In this paper a design, hardware implementation and experimental result of adaptive speed control for IRFOC induction motor drive is presented. Controllers with constant parameters are often insufficient for controlling complex systems, as the induction motor drive systems, that are subject to the large variations of the rotor resistance and to the various load torques during their normal operating cycles. More sophisticated controllers are required, such Self-Tuning Controllers, which in presence of variations of motor parameters, are able to change their features to maintain the desired behavior. The aim of the proposed approach is to design a new Fuzzy Self-Tuning IP Speed Controller, where the controller coefficients are adjusted continuously by a fuzzy inference system. The validity of this method and its effectiveness are proved by means of experimental tests with a 1.5 kW induction motor.

Keywords: Fuzzy and neural systems, Intelligent and AI based control, Optimization
Abstract: This paper proposes a Takagi-Sugeno-Kang (TSK)-type Neuro-Fuzzy model tuned by an adaptive learning algorithm, called Adaptive Cooperative Particle Sub-Swarms Optimization (ACPSSO). The proposed algorithm represents a modified version of the standard Cooperative Particles Sub-Swarms Optimization algorithm (CPSSO). It has adaptive performance to treat real systems dynamics. In this study, a time-varying evaluation function is used to express environmental changes. The proposed algorithm was demonstrated through online control problems of dynamic systems, and was compared with others algorithms to verify its performance.

Keywords: Fuzzy and neural systems, Linear and nonlinear systems
Abstract: This paper deals with the problem of stabilization of the Roesser type polynomial discrete-time 2-D Takagi-Sugeno (T-S) fuzzy system by using the sum of squares (SOS) approach. First, we derive a stabilization conditions based on polynomial Lyapunov functions that contain quadratic Lyapunov functions as a special case. Second, a non-quadratic stabilization conditions of the polynomial fuzzy systems are also proposed to stabilize the underlying 2-D T-S fuzzy system. Both of them are represented in terms of SOS and are numerically solved via the recent developed SOSTOOLS. In addition, polynomial discrete-time 2-D T-S fuzzy system contain discrete 2-D (T-S) fuzzy system as a special case. Hence, the approach discussed in this paper is more general than that based on the existing LMI approaches to discrete 2-D T-S fuzzy system. Finally, a numerical example is provided to illustrate the effectiveness of the proposed methods.

Keywords: Fuzzy and neural systems, Linear and nonlinear systems, Modeling and simulation
Abstract: This paper treats a robust neural control strategy for discrete time uncertain nonlinear systems affected by parameters uncertainties. The control approach is based on an inverse neural model (INM) trained using a specialized learning technique. The importance of the presented approach is the use of a sliding mode backpropagation algorithm (SM-BP) for the learning of the direct neural model (DNM) and the (INM) which has been proved as the best configuration for the control of uncertain nonlinear systems in previous study. A simulation example is treated to illustrate the effectiveness of the proposed control strategy.

Keywords: Control algorithms implementation, Estimations and identification, Optimization
Abstract: The problem of estimating process derivatives in the on-line optimization case has been extensively addressed in last few decades. In fact, it has received such attention due to the fact that earlier methods have encountered major drawbacks in the past. These drawbacks have either caused the non-convergence of the optimization algorithm, or led to the decrease in its performance. This paper investigates a new technique based on Genetic Algorithms (GA’s) for estimating these derivatives within the well-known Integrated System Optimization and parameter Estimation (ISOPE) algorithm. It is shown using simulations on a Continuous Stirred Tank Reactor (CSTR) system how the proposed GA technique can successfully estimate the derivative information and lead the ISOPE algorithm to converge to the right optimum point. Moreover, the technique is able to effectively estimate some unknown model parameters, is less computationally burdened and stable compared to earlier methods.

Keywords: Estimations and identification, Fault detection and Diagnostics, Industrial control
Abstract: All industrial systems and machines are subjected to degradation processes which can be related to the operating conditions. This degradation can cause unwanted stops at any time and major maintenance work sometimes. Prognostic activity is now recognized as a key feature in maintenance strategies, it gives to operators a potent tool in decision making by quantifying how much time is left until functionality is lost. The reason is to plan the heavy interventions and to manage the stock of spare parts. In addition, it can be used to improve the characterization of the material proprieties that govern damage propagation for the structure being monitored. The tool for measuring the size of damage and health status in industry is Remaining Useful Life (RUL) which can be estimated by using four main approaches, namely experience based, model-based, data-driven and hybrid approaches. A new method of prognosis is developed in this paper to improve the accuracy of system state. This framework of forecasting integrates the model-based information and the hybrid approach, which employ the structured residuals in the first one and the Particle Filter in the second. The performance of the suggested fusion prognostic framework is employed to predict the RUL of battery pack in Hybrid Electric Vehicle (HEV).

Keywords: Estimations and identification, Linear and nonlinear systems, Optimization
Abstract: This work tackles the problem of the L2 − L∞ filtering for continuous- time delay systems with polytopic uncertainties. Based on the parameter dependant Lyapunov function approach (PDLFA), we propose a less conservative results by means of a linear matrix inequalities. The use of slack variables provide filter design conditions with less conservative-ness.This approach is illustrated by popular numerical example from the literature.

Keywords: Estimations and identification, Modeling and simulation, Optimization
Abstract: In this paper, the robust observer problem is tackled for mode identification of hybrid dynamical system with variable structures. The main objective is to design an observer that, in the presence of faults and disturbances, the detection time of the active mode is minimized. Two observed-based method are discussed herein: The Linear matrix inequalities approach (LMI) and the pole placement method. In order to promote the most effective method a comparison between the two mentioned methods are investigated.

Keywords: Estimations and identification, Optimal control, Robust control and Hinfty control
Abstract: This paper is concerned with the design of robust observer based controllers for continuous-time linear systems with parameter uncertainties. The proposed method allows one to compute simultane-ously the observer and controller gains by solving a single bilinear matrix inequalities (BMI), which be-comes an linear matrix inequalities (LMI). Numerical examples are given to illustrate the validity and the effectiveness of the proposed method

Keywords: Optimal control, Intelligent and AI based control, Renewable Energy
Abstract: This paper presents a new optimal controller design for the sensorless variable speed wind turbine (SVSWT) based on the extreme learning machine (ELM) and the adaptive particle swarm optimisation (APSO) algorithms. The two main objectives of this command are to maximize the conversion of wind energy below the rated wind speed and to maintain the safety of the wind turbine system (WT) by minimizing stress on the drive train shafts. The proposed technique is based on the efficiency of the ELM for single hidden layer feed forward neural networks (SLFN) combined to sliding mode control (SMC) to respectively, improve the used model and stabilize the operation of the WT. ELM algorithm with high learning speed is used to approximate the nonlinear unmodelled dynamics while SMC is used to compensate the external disturbances and modelling errors. APSO algorithm is introduced to adapt and optimize the gain of the SMC. The efficiency of the proposed method is illustrated in simulations by the comparison with traditional SMC.

Keywords: Renewable Energy, Fuzzy and neural systems, Power systems
Abstract: A wind generation system consisting of a fixed pitch angle wind turbine, a Permanent Magnet Synchronous Generator (PMSG), power electronic converters and a load, is treated in this paper. This study deals with the problem of Maximum Power Point Tracking (MPPT) of the generated power based on Takagi Sugeno fuzzy model. The stability analysis is achieved by means of Lyapunov theory and H-infinity performance. The gains of the designed controller are calculated by solving Linear Matrix Inequality (LMI). In order to check the efficiency of the proposed control strategy, simulation results are carried out using MATLAB/SIMULINK.

Keywords: Renewable Energy, Robust control and Hinfty control, Control applications
Abstract: This paper treats the control of wind energy conversion systems (WECS) based on a Takagi-Sugeno (T-S) model combined with H∞ performance. The present control strategy proposes a new stabilization conditions in the LMI formulation, to the best conservative than some stabilization conditions published recently in the literature for fuzzy observer and H∞ controller designs, where the H∞ performance of a feedback control system is designed to be robust by taking into account direct wind speed as a perturbation. The main controller objective is to maintain a satisfactory quality of power in high wind speeds operating region by reducing mechanical loads. The wind turbine performances in terms of wind turbine rotational speed and its stability, relative to wind speed changes are discussed. The effectiveness and the merit of the proposed approach are shown by simulation results.

Keywords: Estimations and identification, Linear and nonlinear systems, Manufacturing systems
Abstract: This paper deals with missing data in semiconductor manufacturing derived from measurement sampling strategies. The idea is to construct a virtual metrology module to estimate non measured variables using a new modified Just- In-Time Learning approach (JITL). The classical method has been proposed and applied to chemical process. This latter presents some drawbacks and our main contribution is to improve the existing algorithm version of the JITL approach. In collaboration with our industrial partner STMicroelectronics in Rousset, the efficiency of the proposed method is illustrated by using industrial data-sets derived from Chemical Mechanical Planarization (CMP) process that enable to compare results obtained with the classical and the modified version.

Keywords: Industrial control, Modeling and simulation, Process control and instrumentation
Abstract: In this paper we will proposed a program on Matlab / Simulink, which is calculated, based on the ISO-5167. ISO 5167-5:2016 also provides background information for calculating the flow rate and is applicable in conjunction with the requirements given in ISO 5167 1. This program will take the system parameters on considerations such as the willingness plates, the size of the orifice, the given design conditions, reference conditions, find pressure drop for a given flow, or flow for a loss of given load. The results are considered very good and satisfactory because the errors identified of measuring instruments system are within the margin of error limit by the regulations.