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This paper focuses on resolving the trajectory tracking problem of two wheeled mobile robot. We begin by presenting the kinematic model of the robot which is the base of the control law then we present a PI controller and a model predictive controller to solve the problem of trajectory tracking. We performed a comparison between the performances of the classical PI controller and the predictive controller which is an interesting approach that considers an explicit performance criterion and minimizes it during the computation of the control law. Simulation results are provided in order to show the effectiveness of model predictive control in the resolution of trajectory tracking problem.

This article deals with fractional systems that rep-resent better physical process and guarantee a very small number of parameters that can reduces the computation time. It focuses in particular on the state-space representation which highlights the state variables and allows to study the internal behavior of the system taking into account the initial state. Moreover, this representation adapts better to the multiple input multiple-output case. It also discusses the discretization of fractional system to finally adapt the Model Predictive Control to apply it and shows its efficiency and performance in these systems. The main objective of this article is to compare the commensurate and non-commensurate fractional models performance, calculation time and ease of use.

The design of a low order controller for decoupled MIMO systems is proposed. The main objective of this controller is to guarantee some closed loop time response performances such as the settling time and the overshoot. The controller parameters are obtained by resolving a non-convex optimization problem. In order to obtain an optimal solution, the use of a global optimization method is suggested. In this chapter, the proposed solution is the GGP method. The principle of this method consists of transforming a non-convex optimization problem to a convex one by some mathematical transformations. So as to accomplish the fixed goal, it is imperative to decouple the coupled MIMO systems. To approve the controllers' design method, the synthesis of fixed low order controller for decoupled TITO systems is presented firstly. Then, this design method is generalized in the case of MIMO systems. Simulation results and a comparison study between the presented approach and a PI controller are given in order to show the efficiency of the proposed controller. It is remarkable that the obtained solution meets the desired closed loop time specifications for each system output. It is also noted that by considering the proposed approach the user can fix the desired closed loop performances for each output independently.

International audience; The Internet of things (IoT) becomes a new solution for the future industry. It aims to provide an intelligent environment to control systems in real time. We propose an embedded predictive application in the Wireless Networked Control Systems (W-NCS) to control a DC motor via RF links, under the presence of packet losses and limited size constraint of transmitted packets in the communication channel. To design an IoT predictive application in W-NCS, a Model Predictive Control (MPC) method is proposed. This strategy is developed and implemented in the embedded device by using the RIOT OS which granted maintenance costs of IoT products and offers a real-time support in the control of the systems. The proposed approach is tested in a wireless environment with an intention to be applied to tackle the problem of communication. The practical experiment results obtained demonstrate the effectiveness of the Networked Predictive Control (NPC) approach based on the W-NCS structure for this kind of problems.

The design of a robust fixed low-order controller for uncertain decoupled multi-input multi-output (MIMO) systems is proposed in this paper. The simplified decoupling is used as a decoupling system technique. In this work, the real system behavior is described by a linear model with parametric uncertainties. The main objective of the control law is to satisfy, in presence of model uncertainties, some step response performances such as the settling time and the overshoot. The controller parameters are obtained by resolving a min–max nonconvex optimization problem. The resolution of this kind of problems using standard methods can generate a local solution. Thus, we propose, in this paper, the use of the generalized geometric programming (GGP) which is a global optimization method. Simulation results and a comparison study between the presented approach, a proportional integral (PI) controller, and a local optimization method are given in order to shed light the efficiency of the proposed controller.

This paper presents a predictive controller of a holonomic omnidirectional mobile robot. First, we studied the dynamic properties of the robot then we presented the dynamic model. The predictive control law is obtained by using a state space model which is based on the dynamic model of the robot. In this work the reference trajectory is planified by the cubic splines using the classical formulae and with a defined velocity profile (bang-bang). Simulation results are given to illustrate the effectiveness of the control law joined with the dynamic model.

In this study, a robust model predictive control of fractional-order systems with real uncertain parameters is presented. The Grunwald–Letnikov's method is used as an internal model to predict the plant future dynamic behaviour. This method consists in replacing the non-integer derivation operator of the adopted system representation by a discrete approximation. Therefore a robust fractional model predictive control is developed based on an uncertain fractional model. Moreover, the output deviation approach is adopted to design the j-step ahead output predictor, and the corresponding control law is obtained by the resolution of a min–max optimisation problem that takes into account the uncertainties of the fractional-order model parameters and under input constraints. The efficiency and the performances of the proposed predictive controller are illustrated with practical results of a thermal system.

In this paper, we propose a feedforward neural networks-based robust predictive controller for a class of multi-input–multi-output non-linear systems. Using the structured uncertainties of the output layer’s weights of the neural networks model, the non-linear model of the real system is determined at each operating point. The control law is formulated as a minimax problem, which is solved online. The non-convex optimization is developed by minimizing the worst case of the objective cost function, taking into account the uncertainties of the non-linear model and the input control signal constraints. The efficiency of the proposed neural predictive controller is illustrated, in simulation, with a multivariable system example.

This work addresses the implementation issue of constrained Model Predictive Control (MPC) for the autonomous trajectory-tracking problem. The chosen process to control is a Wheeled Mobile Robot (WMR) described by a discrete, Multiple Input Multiple Output (MIMO), state-space and linear parameter varying kinematic model. The main motivation of the constrained MPC usage in this case relies on its ability in considering, in a straightforward way, control and states constraints that naturally arise in trajectory tracking practical problems. The efficiency of the presented control scheme is validated through experimental results on a two wheeled mobile robot using both STM32F429II and STM32F407ZG microcontrollers. The controller implementation is facilitated by the usage of the automatic C code generation and interesting optimization before real-time execution. Based on the experimental results obtained, the good performance and robustness of the proposed control scheme are established.

In this paper, we present a synthesis of the fractional order Proportional Integral controller (PIλ) using Characteristic Ratio Assignment (CRA) method. This controller guaranties some step response performances such as setting time, rise time and overshoot. Simulation results show the efficiency of the proposed controller.

A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, offline, to train the neural networks model. The optimal control actions are computed by solving a nonconvex optimization problem with the gradient method. In gradient method, the steepest descent is a sensible factor for convergence. Then, an adaptive variable control rate based on Lyapunov function candidate and asymptotic convergence of the predictive controller are proposed. The stability of the closed loop system based on the neural model is proved. In order to demonstrate the robustness of the proposed predictive controller under set-point and load disturbance, a simulation example is considered. A comparison of the control performance achieved with a Levenberg-Marquardt method is also provided to illustrate the effectiveness of the proposed controller.

Model predictive control (MPC) is an optimisation-based strategy for high-performance control engineering practice and real-time applications. This method needs to solve online a quadratic programming (QP) problem at each sample time to find optimal control sequence. In this paper, a new optimised MPC architecture is presented, for a gradient-based QP solver to implement linear MPC on a field-programmable gate array platform, which allows obtaining high-quality performances for the real-time control applications. It requires a manual programming of the high-level C/C ++ code in opposition to the other presented approaches, which automatically generates the code. The efficiency of this approach is completed with real time control of the water level of a single tank system running on a Nanoboard 3000XN chip using a conception environment (Altium Designer), while comparing between the MPC and PID controllers.

This paper deals with the identification of nonlinear complex systems using the Nonlinear Auto Regressive Moving Average (NARMA) model. We propose, jor the monovariable systems, two new approaches allowing the determination of the significant terms in the expression of NARMA model as well as the estimate of the corresponding coefficients. The first one exploits the Binary Genetic Algorithm, whereas the second uses a procedure based on a single layer neural network with polynomial activation function which is optimised with a real-coded genetic algorithm. We propose, also, the extension of the second method to identify the multivariable nonlinear systems.

This paper deals with an adaptive nonlinear model predictive control (NMPC) based estimator in cases of mismatch modeling, presence of perturbations and/or parameter variations. Thus, we propose an adaptive nonlinear predictive controller based on the second-order divided difference filter (DDF) for multivariable systems. The controller uses a nonlinear state-space model for parameters and state estimation and for the control law synthesis. Two nonlinear optimization layers are included in the proposed algorithm. The first optimization problem is based on the output error (OE) model with a tuning factor, and it is dedicated to minimize the error between the model and the system at each sample time by estimating unknown parameters when assuming that all system states are available. The second optimization layer is used by the centralized nonlinear predictive controller to generate the control law which minimizes the error between future setpoints and future outputs along the prediction horizon. The proposed algorithm leads to a good tracking performance with an offset-free output and an effectiveness in perturbation attenuation. Practical results on a real setup show the reliability of the proposed approach.

This paper focus on resolving the trajectory tracking problem of two wheeled mobile robot. We begin by presenting the kinematic model of the robot which is the base of the control law than we present a PI controller and a model predictive controller to solve the problem of trajectory tracking. We performed a comparison between the performances of the classical PI controller and the predictive controller which is an interesting approach that considers an explicit performance criterion and minimizes it during the computation of the control law. Simulation results are provided in order to show the effectiveness of model predictive control in the resolution of trajectory tracking problem.

Field surveys were carried out in the main stone-fruit growing areas of Morocco to evaluate the sanitary status of commercial orchards, varietal collections and nurseries. The presence of virus and virus-like diseases was checked by ELISA, sap transmission to herbaceous hosts, testing on woody indicators and molecular hybridization (dot-blot and tissue-printing). 1211 samples (382 almond, 339 peach, 291 plum, 150 apricot and 49 cherry) were tested by ELISA for the presence of Prunus necrotic ring spot virus (PNRSV), Prune dwarf virus (PDV), Apple chlorotic leaf spot virus (ACLSV), Apple mosaic virus (ApMV) and Plum pox virus (PPV). The overall average of virus infection rate was 16.4%, whereas that of single species was: 22.6% for almond, 17.8% for plum, 15% for peach, 10.2% for cherry, and 2.7% for apricot. The following viruses were detected: PNRSV, PDV, ACLSV and ApMV. 565 samples were tested by dot-blot and tissue-printing hybridization for the presence of Peach latent mosaic viroid (PLMVd) and Hop stunt viroid (HSVd). 48 samples were infected, 41 by PLMVd and 7 by HSVd. In addition, nested-PCR tests identified Plum bark necrosis and stem-pitting associated virus (PBNSPaV) in a few almond trees affected by stem pitting.

The basic idea of this chapter is to implement a fixed low order controller on an electronic system using the STM32 microcontroller. The principal aim of this controller is to guarantee some time response specifications as the settling time and the overshoot. The controller parameters are obtained by minimizing a non-convex optimization problem while taking into account the desired closed-loop performances. Accordingly, the resolution of this sort of problem with classical optimization method may lead to a local solution and the achieved control law is not optimal. Hence, with an aim of obtaining an optimal solution that will be able to satisfy the desired specifications, the use a global optimization method is recommended. In this chapter, the Generalized Geometric Programming (GGP) method is exploited. The practical implementation, on a fast electronic system, of the designed control law and a Proportional Integral (PI) controller illustrates the effectiveness of the proposed algorithm.

The aim of this paper is to study the impact of the recent financial crisis on equity cost estimation. We use a data of a 22 firms listed in the Tunisian stock market during the period from July 2006 to June 2011. The choice of this period is motivated by the occurrence of the financial crisis of October 2008, which divides the period into two equal sub-periods. In the first stage, we make abstraction to the crisis impact and we run the three specifications of the cost of equity: the CAPM, the Fama -French three factor model and The Carhart four-factor model. Empirical results confirm the explanatory power of the three specifications in the context of the Tunisian market. We also confirm the existence of a size effect, a book to market effect and a momentum effect. In the second stage, we show that the presence of financial crisis does not affect the cost of equity. However, we note a decrease in the coefficients of the explanatory variable after introducing the dummy crisis variable.