Moufida Ksouri

Global optimization method for model predictive control based on Wiener model

This paper deals with model predictive control based on Wiener model. The nonlinear block of the considered model is represented by a polynomial relation and the models parameters are determined using the neural networks. A global optimization method, i.e. the generalized geometric programming method, is used to solve the nonconvex optimization control problem. The efficiency of the proposed controller is illustrated through a simulation example.

A New Strategy of Validities’ Computation for Multimodel Approach: Experimental Validation

The evaluation of validities is a fundamental step in the design of the multimodel approach. Indeed, it is thanks to validities that we estimate the contribution of each base-model in the reproduction of the behavior of the global process in a given operating area. These coefficients are calculated most commonly by the approach of the residues formulated by the distance between the real output and the sub-models’ outputs. In this paper, a strategy allowing to improve the performances of the residues’ approach in terms of precision and robustness is proposed. This strategy is based on a quasi-hierarchical structuring. A simulation example and a validation on a semi-batch reactor showed the interest and the effectiveness of the proposed strategy.

Separable identification of continuous-time systems having multiple unknown time delays from sampled data

This paper treats the problem of continuous-time model identification with unknown time delays from sampled data. The proposed method estimates the plant and the time delays in a separable way. More precisely, the plant is estimated by the standard recursive Least Square algorithm while the time delays are explicitly estimated by the Gauss-Newton algorithm. This means clear separation between the system dynamics and time delays. Numerical simulations are given at last to illustrate the validity of the proposed scheme.

Identification of continuous-time hybrid ‘Box-Jenkins’ systems having multiple unknown time delays

For many years, various methods for the identification of parameters of continuous-time models have been available and implemented in widely. However, most methods apply models where the output are contaminated by a white noise or without noise in some others cases, which are unrealistic in most practical applications owing to their associated noise structure. Some other methods neglect the presence of time delays. Then it can be shown that the estimates are not statistically efficient. To cope with this issue, this paper deals with the identification of multi-input single-output continuous-time hybrid ‘Box-Jenkins’ systems having multiple unknown time delays from sampled input/output data. The proposed work presents a based-instrumental variable method for the separable estimation of both process parameters, multiple unknown time delays and the noise model. The effectiveness of the proposed scheme is proven through a numerical example illustrated by Monte Carlo analysis.

Filled Function Method for Nonlinear Model Predictive Control

A new method is used to solve the nonconvex optimization problem of the nonlinear model predictive control (NMPC) for Hammerstein model. Using nonlinear models in MPC leads to a nonlinear and nonconvex optimization problem. Since control performances depend essentially on the results of the optimization method, in this work, we propose to use the filled function as a global optimization method to solve the nonconvex optimization problem. Using this method, the control law can be obtained through two steps. The first step consists of determining a local minimum of the objective function. In the second step, a new function is constructed using the local minimum of the objective function found in the first step. The new function is called the filled function; the new constructed function allows us to obtain an initialization near the global minimum. Once this initialization is determined, we can use a local optimization method to determine the global control sequence. The efficiency of the proposed method is proved firstly through benchmark functions and then through the ball and beam system described by Hammerstein model. The results obtained by the presented method are compared with those of the genetic algorithm (GA) and the particle swarm optimization (PSO).

Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

In this work, the pH neutralization process is described by a neural network Wiener (NNW) model. A nonlinear Model Predictive Control (NMPC) is established for the considered process. The main difficulty that can be encountered in NMPC is solving the optimization problem at each sampling time to determine an optimal solution in finite time. The aim of this paper is the use of global optimization method to solve the NMPC minimization problem. Therefore, we propose in this work, to use the Self Organizing Migrating Algorithm (SOMA) to solve the presented optimization problem. This algorithm proves its efficiency to determine the optimal control sequence with a lower computation time. Then the NMPC is compared to adaptive PID controller, where we propose to use the SOMA algorithm to formulate the PID in order to determine the optimal parameters of the PID. The performances of the two controllers based on the SOMA algorithm are tested on the pH neutralization process.

Optimal iterative learning control for a class of non-minimum phase systems

In this paper, an optimal iterative learning control (ILC) approach is proposed for a class of repetitive non-minimum phase (NMP) systems. The control law synthesis is based on the resolution of a quadratic criterion which minimises the errors between the setpoint references and the system outputs at each iteration for each trial. The resolution of the control problem uses a new gain which avoids matricial inversion problems appearing in classical ILC algorithms such as direct model inversion (I-ILC) and optimal ILC (Q-ILC). The new optimal ILC approach improves the learning convergence significantly compared to the previously mentioned algorithms. Furthermore, sufficient and necessary stability conditions are established with convergence properties. The effectiveness of the proposed method is proved by simulations with an NMP mass-spring damper system.

Identification of continuous-time systems with multiple unknown time delays

Purpose The purpose of this paper is to present a separable identification algorithm for a multiple-input single-output (MISO) continuous-time (CT) system. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT systems with unknown time delays by using the Simplified Refined Instrumental Variable method. Findings Simulations results are presented to show the performance of the proposed approach in the presence of additive output measurement noise. Originality/value This paper presents an optimal and robust method to separable delays and parameter identification of a MISO CT system with unknown time delays from sampled input/output data.

Online identification of continuous-time systems with multiple-input time delays from sampled data using sequential nonlinear least square method from sampled data

This research considers the problem of online identification of continuous-time system with multiple unknown time delays from sampled data. The presented algorithm estimates, simultaneously, the linear parameters and the multiple time delays. Therefore, a Sequential Nonlinear Least Square algorithm is used. Indeed, we propose a new formulation of the identification problem which permits the definition of the time delays and the parameters in the same estimated vector. Numerical example is presented to illustrate the robustness of the proposed algorithm.

Multivariable Decoupling Controller: Application to Multicellular Converter

A new control strategy is presented in this paper, based on previous works limited to the control of the capacitor voltages considered as the outputs of a three cell converter. An additional control input is proposed to this latter in order to obtain the desired current output. The experimentations performed on a multicellular converter are presented and the discussed results showing the efficiency of the contribution