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This paper proposes a dual decomposition method for solving distributed model predictive control. This controller is designed for systems subject to communication constraints, in which nonlinear subsystems interconnected via dynamics and by constraints. The interconnections are relaxed by using gradient method, accelerated gradient and alternating direction methods of multipliers. Also, an event-based communication is proposed to handle the issue of communication constraints especially in embedded systems. In the proposed event-based communication strategy, each controller solves the optimization problem and communicate only if the prices are updated significantly, which can reduce the computation load and release the burden of the network while achieving global performance. Finally, the simulations study of the four-tank benchmark is presented to demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a new model for a representation of linear system with delay. This latter, is given by decomposing the coefficients of the AutoRegressive with eXogenous input (ARX) model associated with the output and the input on two independent Meixner-like orthonormal bases. The resulting model is called ARX Meixner-like model. In order to ensure the parameter complexity reduction, the Meixner-like poles are optimized using Genetic algorithms method. Unknown But Bounded Error (UBBE) approaches are used to update the uncertainty domain of the parameters of the new obtained model. A numerical example of system with delay and two examples of experimental research are made to prove the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear distributed model predictive control based on dual decomposition approach is proposed for complex system. The global system can be decomposed into several subsystems and each one will be managed by its own controller. To design the nonlinear predictive control in a distributed fashion, an analytical solution is proposed. The latter is based on the approximation of the error using its expansion of Taylor series. The proposed approach is implemented on the three tank system to control the water levels. Simulation results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new Proportional Integral Derivative (PID) control algorithm based on the Meixner-like model. The Meixner-like functions are an extension of Laguerre functions and are convenient when the system has a slow start. The parameters of the PID controller are auto adjusted by the model predictive control (MPC) technique. To improve system performance, the resolution of the problem of saturation of the control signal is proposed. A numerical example of a variable parameter system is given to illustrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a technique to reduce the complexity of an existing (initial) model via the Laguerre filters. We present an analytical method for the parameter identification of the Fourier coefficients of the Laguerre model. This technique is based on the bilinear discrete transformation and in which the Fourier coefficients are expressed in recurrent form in terms of the Laguerre pole. This latter is estimated by an iterative technique, based on the Newton algorithm. This identification technique is after extended to the case of the ARX-Laguerre model and the MISO-ARX-Laguerre model and its performances are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

In this article, the Meixner-like model is used to represent the linear discrete-time system in comparison with the Laguerre model. Furthermore, we propose from input/output measurements a new recursive representation of the Meixner-like model. However, a significant approximation of this model is subject to an optimal choice of Meixner-like parameters: pole, order of generalization and Fourier coefficients. The present research yields a series of computational experiments, through a simulation example of a linear parameter varying (LPV) system. This study tests this approach, verifies the theoretical results and points out the positive outcomes of using the Meixner-like model in comparison with the Laguerre model. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is constructed for model predictive control (MPC) of multi-input multi-output (MIMO) systems. The latter are represented by a discrete-time MIMO ARX model expansion on Laguerre orthonormal bases. The resulting model, entitled the MIMO ARX-Laguerre model, provides a recursive representation with parameter number reduction. This reduction is strongly linked to the choice of Laguerre poles, and therefore we propose a new algorithm to optimize the Laguerre poles of the resulting model. The recursive formulation of the MIMO ARX-Laguerre model is used to obtain the MPC strategy. An ℓ2-norm finite moving horizon cost function is used to obtain a control law which is implemented as a quadratic programming (QP) problem. The effectiveness of the proposed controller that takes into account physical constraints is illustrated by a numerical simulation example and by a practical validation on an experimental communicating two-tank system (CTTS). [ABSTRACT FROM AUTHOR]

This article investigates the use of orthogonal basis functions, particularly Meixner-like functions, to streamline and optimize the parameter complexity in multi-input multi-output (MIMO) system modeling. We introduce a new optimal model for MIMO systems, developed by decomposing the auto-regressive MIMO model with external inputs (ARX) using orthonormal Meixner-like bases. This model, referred to as the MIMO ARX Meixner-like model, enables filtering of the system’s inputs and outputs with Meixner-like functions. Parameter reduction is achieved through the optimization of Meixner-like poles using an iterative technique based on the Newton-Raphson method. The proposed model’s efficacy is demonstrated through simulations on two experimental systems: a two-degrees-of-freedom (DOF) helicopter and a two-tank process. Results reveal that the model effectively reduces approximation errors with fewer parameters. Comparative analysis with the traditional MIMO ARX model confirms the superior performance of the MIMO ARX Meixner-like model. [ABSTRACT FROM AUTHOR]