Saïda Bedoui

1. System identification provides an interesting approach to developing a mathematical model for dynamic systems using measured input and output data (Pintelon & Schoukens, 2012). The complexity of...

Summary: In practical applications, many processes have nonlinear characteristics that require nonlinear models for accurate description. However, constructing such models and determining their parameters are a challenging task. This article explores filtered identification methods for estimating the parameters of a particular type of nonlinear Hammerstein systems with ARMA noise. An auxiliary model‐based least squares algorithm is developed for such systems based on the auxiliary model identification idea. A hierarchical least squares algorithm that utilizes the hierarchical identification principle is proposed to enhance computational efficiency. Additionally, a key term separation technique is employed to express the system output as a linear combination of parameters, allowing the system to be decomposed into smaller subsystems for more efficient estimation of parameters. Simulation results demonstrate the effectiveness of these proposed algorithms.

In this article, we propose an approach for system identification for a class of discrete-time fractional-order Hammerstein systems using only input–output data. Using a combined state and parameter estimation approach, we develop an algorithm serving to estimate simultaneously, the system parameters, the system orders, and the system states. By minimizing the defining criterion, which is non-convex and nonlinear in the parameters, the model parameters are estimated using the recursive least squares and the Levenberg–Marquardt algorithms. Next, the system states are estimated using the Luenberger observer. Then, the convergence analysis of the proposed algorithm is proved. Finally, the Monte Carlo simulation analysis and an application to Ultracapacitor system are used to demonstrate the effectiveness of the suggested method.

The design of an accurate fault-tolerant control fault-tolerant control is still an active researches of switched hybrid system whose switching mechanism is unknown. In this paper, a new approach is proposed to deal with switching mode estimation and control problems for switched system subject to actuator faults and disturbances. First, the switching mode is estimated off-line using the Chiu clustering algorithm. Then, actuator fault estimation is proposed using data-based projection approach based on performance measures that allow fault estimation without relying on mathematical models and fault assumptions. Second, a novel switching control strategy is developed for actuator fault compensation and recovering the desired performances. These strategy integrate a set of controllers, corresponding to a set of partial modes to design a bank of switching control laws, compensating for the actuator fault effect and attenuating the sinusoidal disturbance. A new linear matrix inequality is proposed to compute control gains based on Lyapunov stability. This approach allows finding the optimal control gains matrices and ensure both actuator fault compensation and disturbance attenuation. Simulation results of two numerical examples shows the effectiveness of the proposed estimation and control strategies in spite of the presence of actuator faults and sinusoidal disturbance.

This paper aims to design of an active fault-tolerant control AFTC of switched systems in the presence of sensor fault and periodic disturbances. AFTC requires sensor fault estimation. Thus, a data-based projection approach is proposed based on performance measures that allow fault estimation without relying on mathematical models and fault assumptions. A new switching control strategy is proposed for fault compensation and recovering the desired performances. These techniques integrate a bank of controllers, corresponding to a set of partial models to design a set of switching control laws, compensating for the fault effect and attenuating the disturbance. A new linear matrix inequality (LMIs) based on Lyapunov stability analysis is proposed to compute control gains. This technique allows finding the optimal values of the control gains matrices, which ensure both sensor fault compensation and disturbance attenuation. A comparative study of the proposed strategy with existing work is carried out to show the effectiveness of the designed fault-tolerant control.

Jointly identification of the sample delay and the parameters of time-delay discrete-time ARMAX models are addressed in this article. Like any identified problem, the proposed approach is based on the solution of an optimization problem. It consists then of two main steps: the first is the problem formulation and the second is the problem resolution. The problem formulation defines the criterion to be minimized according to an unknown parameter vector consisting of the sample delay and the parameters with their corresponding observation vector. Since the sample delay is of integer type, the obtained optimization problem cannot be efficiently solved using a real optimization algorithm. To overcome this difficulty, the rounding properties are used to transform the obtained problem into a real optimization problem. The solution is then done using the least square approach. Consistency of the estimates with their convergence rates is established under the persistent excitation condition. Finally, some numerical simulation and experimental results are presented to illustrate the performance of the proposed method.