Kamel Guesmi

Design of an optimized fractional order fuzzy PID controller for a piezoelectric actuator

This paper deals with piezoelectric actuators control. A fractional order fuzzy PID controller is designed for this class of systems with the help of particle swarm optimization (PSO) algorithm. The controller parameters are defined as an optimization problem and the PSO is used to find their optimal values. The proposed approach validation and their performances evaluation are done through simulation.

Identification of the PEA Hysteresis Property Using a Minimum Variance Scheme

The piezoelectric actuators (PEA) based on the inverse piezoelectric effect are used in many fields due to their properties. Indeed, for example they are very used in the ultra-precision applications [1]-[3]. However, the hysteresis property, existing in piezoelectric materials, makes the modeling and the control of PEA difficult. Many nonlinear models was developed in the literature to describe the hysteresis property of piezoelectric actuators such as the Preisach model and its modifications [4]-[8], the Duhem model [9], [10], the Maxwell Resistance Capacitor (MRC) model [11], the Bouc-Wen model [12]-[15], the PrandtlIshlinskii model [16]-[20] and the modified Rayleigh model [21]. A survey on these models can be found in [22]. Furthermore, the experimentation showed that the hysteresisnon-linearity in PEA is not symmetric and many models was proposed in [23]-[25] to describe the asymmetric hysteresis existing in PEA. To compensate the hysteresis behavior of PEA, many intelligent techniques was used such as fuzzy logic [26], [27], neural networks [28], [29], adaptive filter [30], [31], hybrid models [32], NARMAX models [33], [34] and iterative learning control [35]. The most previous models are nonlinear and difficult to implement in on-line which makes the controller synthesis and analysis difficult. To deal with this problem, the PEA can be described by linear models using identification algorithms [36], [37]. In this paper, we propose a technique for the description of the hysteresis property. This technique is based on the modification of the minimum variance controller algorithm to be used for identification purpose. This paper is organized as follows: the extended least squares recursive identification method is described in Section II, then, the proposed minimum variance identification scheme is presented in Section III and before concluding, the proposed approach is validated through simulation results.

Identification of the PEA hysteresis property using a fractional order model

This paper deals with the identification of the hysteresis property existing in piezoelectric actuators (PEA). In this paper, a fractional order model is used with the Recursive Least Squares (RLS) algorithm extended to fractional order models. The proposed approach is validated through numerical simulations and a comparison study is carried out to evaluate its performances.

Fuzzy identification and control of a piezoelectric actuator

Due to the special characteristics of piezoelectric actuators, the modelling and control task of these devices has been one of the most important research topic in the last decade. In the first part of this paper, a T-S fuzzy model is proposed to represent the behavior of this type of actuators and the recursive least squares (RLS) algorithm is used to optimize the parameters of this model. In the second part, based on an analytical model, a fuzzy PID controller for the piezoelectric actuator plant is presented. An ant colony optimization (ACO) algorithm is used to find the parameters of the proposed controller. The proposed approaches are validated through simulation along with their satisfactory performances.

Adaptive fuzzy synergetic control of nonlinear systems with unknown backlash-like hysteresis

The hysteresis non-linearity exists in many physical systems and materials, such as electrical and mechanical actuators, ferromagnetic and ferroelectric materials. However, this property makes the analysis and control of such systems and devices difficult. In order to control this property, a new Adaptive fuzzy synergetic control strategy is proposed in this paper. Indeed, to highlight the problem, the hysteresis behavior of the system is described using a backlash-like model. Then a synergetic control scheme is proposed to deal with the problem of controlling non-linear hysteretic systems. Its formed of a fuzzy system to approximate the unknown system dynamics with an adaptive synergetic controller to achieve the desired performances. The proposed approach is validated through simulation along with its satisfactory performances.

Optimized Sliding Mode Control of DC-DC Boost Converter for Photovoltaic System

DC–DC converters are an important element in photovoltaic systems to attain desired level of energy and to shape it according to the demand. This paper proposes new optimized sliding mode controller (SMC) with fixed switching frequency for a boost converter to step up a fluctuating solar panel voltage to a higher constant DC voltage. Based on the converter functioning principle, a sliding mode controller (SMC) is proposed. Then, a method for SMC parameters selection using simplex and PSO techniques is given. The simplex method allows obtaining the admissible ranges for SMC parameters while taking into account practical considerations about the converter. Then, theses ranges will be used by the particle swarm optimization technique (PSO) to find optimal values for controller parameters.

Design of an adaptive fuzzy sliding mode controller for non-linear hysteretic systems

In this paper, a new adaptive fuzzy sliding mode controller is developed for a class of non-linear hysteretic systems. First, the problem is formulated where the hysteresis behavior is described by a backlash-like model. Then, a fuzzy system is used to approximate the unknown system dynamics and an adaptive sliding mode controller is constructed. Finally, an application example to show the performance of the proposed approach.