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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.

Model Based Predictive Control (MBPC) is a control strategy based on the explicit use of a model to predict the process output over a long-range time period. To compute the step ahead predictor, one would solve polynomial equations in recursive way. This paper deals with a new formula for computing the predictor of stochastic multivariable linear systems. This formula delimits the role of each model parameter and avoids the recursive resolution of diophantine equations. The process and prediction model which are used unite the most commonly used models in predictive controllers.

This paper deals with the robust predictive control of nonlinear systems. The behavior of the nonlinear system is described by an uncertainty Feedforward neural networks model, i.e. each output layer's parameter is uncertain. The control problem is formulated as a minimax optimization one which is a non convex problem. The performances of the proposed controller are illustrated and compared to a classical PI controller by a simulation example.

This paper provides an application of linear and nonlinear multivariable robust predictive control to a three tanks system. The design of the nonlinear solution is based on a Multi-Input Multi-Output Nonlinear Auto Regressive with eXogenous outputs (MIMO-NARX) model and the linear controller considers a MIMO Controlled Auto Regressive Integrated Moving Average (MIMO-CARIMA) model. Polytopic uncertainties and structured uncertainties are adopted in order to take into account the uncertain physical dynamics of the system. Using worst case strategy, the control law is obtained by the resolution of a min-max optimization problem. However, the performance criterion to be optimized is non-convex. A genetic algorithm is adopted to solve the control problem. The efficiency of the developed strategies is illustrated on a three tanks system.

This work presents an application of linear and nonlinear robust predictive control onto a three tanks system. The design of the linear solution is based on Single-Input Single-Output Controlled Auto Regressive Integrated Moving Average (CARIMA) model and the nonlinear controller considers Nonlinear Auto Regressive with eXogenous output (NARX) model. Parametric uncertainties and polytopic uncertainties are adopted in order to take into account the uncertain behavior of the system. Based on worst case strategy, the control law is obtained by the resolution of a min-max optimization problem. However, the performance criterion to be optimized is non-convex. To overcome this problem, non-determinist and determinist global optimization algorithms are proposed namely Genetic Algorithms (GA) and Generalized Geometric Programming (GGP). Experiment results onto a three tanks system are given to illustrate the effectiveness of the developed strategies.

Control of nonlinear uncertain dynamical systems is considered. The artificial neural networks (ANNs) are used to model the process. For each operating level an ANN is determined. The model predictive type of controller is designed that utilizes a set of ANN model and employs the input constraints. The nondominated sorting genetic algorithm (NSGA) is applied to solve the multiobjective optimization problem. The proposed control schema is applied to a numerical example and the simulation results are included.