Abderrahmen Zaafouri

DSP-based adaptive backstepping using the tracking errors for high-performance sensorless speed control of induction motor drive.

This paper presents a modified structure of the backstepping nonlinear control of the induction motor (IM) fitted with an adaptive backstepping speed observer. The control design is based on the backstepping technique complemented by the introduction of integral tracking errors action to improve its robustness. Unlike other research performed on backstepping control with integral action, the control law developed in this paper does not propose the increase of the number of system state so as not increase the complexity of differential equations resolution. The digital simulation and experimental results show the effectiveness of the proposed control compared to the conventional PI control. The results analysis shows the characteristic robustness of the adaptive control to disturbances of the load, the speed variation and low speed.

Electric Drive Control with Rotor Resistance and Rotor Speed Observers Based on Fuzzy Logic

Many scientific researchers have proposed the control of the induction motor without speed sensor. These methods have the disadvantage that the variation of the rotor resistance causes an error of estimating the motor speed. Thus, simultaneous estimation of the rotor resistance and the motor speed is required. In this paper, a scheme for estimating simultaneously the rotor resistance and the rotor speed of an induction motor using fuzzy logic has been developed. We present a method which is based on two adaptive observers using fuzzy logic without affecting each other and a simple algorithm in order to facilitate the determination of the optimal values of the controller gains. The control algorithm is proved by the simulation tests. The results analysis shows the characteristic robustness of the two observers of the proposed method even in the case of variation of the rotor resistance.

Real time PI-backstepping induction machine drive with efficiency optimization

This paper describes a robust and efficient speed control of a three phase induction machine (IM) subjected to load disturbances. First, a Multiple-Input Multiple-Output (MIMO) PI-Backstepping controller is proposed for a robust and highly accurate tracking of the mechanical speed and rotor flux. Asymptotic stability of the control scheme is proven by Lyapunov Stability Theory. Second, an active online optimization algorithm is used to optimize the efficiency of the drive system. The efficiency improvement approach consists of adjusting the rotor flux with respect to the load torque in order to minimize total losses in the IM. A dSPACE DS1104 R&D board is used to implement the proposed solution. The experimental results released on 3kW squirrel cage IM, show that the reference speed as well as the rotor flux are rapidly achieved with a fast transient response and without overshoot. A good load disturbances rejection response and IM parameters variation are fairly handled. The improvement of drive system efficiency reaches up to 180% at light load.

Modeling, Identification and Control of Irrigation Station with Sprinkling: Takagi-Sugeno Approach

The spray under pressure is an effective save on water. This task should be automated and controlled in order to limit the water waste and the facilities of damages. For this reason, it’s necessary to find a mathematical model describing the irrigation process. In order to facilitate this step the Takagi-Sugeno fuzzy model is the best approaches of nonlinear systems representation. Various techniques are used in the literature of such systems; the clustering technique is one of the best solutions. In this paper, we’ll model the irrigation station with the T-S algorithm and use the fuzzy c-means (FCM) algorithm and present the results of simulation and some validation tests and we present the stability of T-S irrigation station model.

A sensor-less speed control of induction motor based on robust rotor flux observer

The induction motor is the principal source of the workhorse [1] in the industry. However, due to its high non linearity, a high-performance control of induction motor remains a challenge for the automation. In this paper, we present a robust solution for the observer and control of the induction machine taking into account the iron losses. The proposed technique is based on Lyapunov theory and application of the bilinear matrix inequalities framework. Thus, an adaptive mechanism is introduced to cover not only uncertainty parameters but also the rotor speed which is derived from the satisfaction of the first condition of the Lyapunov theory. The observer gain is calculated by solving the bilinear matrix inequality (BMI) to satisfy the second condition of Lyapunov. Some simulation results are given to demonstrate the robustness and performance of the proposed solution.

Speed sensorless indirect field-oriented of induction motor using two type of adaptive observer

In this paper, the indirect vector control speed sensorless is presented, two adaptive mechanisms have been proposed to estimate the rotor speed. The first adaptive observer based on sliding mode, a study was made to present the steps needed to design a sliding mode observer. The second has been developed from the backstepping technique to design an observer for the rotor speed. Finally, tests show the robust performance of the control law obtained by these two types of adaptive observers.

An improved heterogeneous multi-swarm PSO algorithm to generate an optimal T-S fuzzy model of a hydraulic process:

In this paper, a new methodology to develop an Optimal Fuzzy model (OptiFel) using an improved Multi-swarm Particle Swarm Optimization (MsPSO) algorithm is proposed with a new adaptive inertia weight based on Grey relational analysis. Since the classical MsPSO suffers from premature convergence and can be trapped into local optima, which significantly affects the model accuracy, a modified MsPSO algorithm is presented here. The most important advantage of the proposed algorithm is the adjustment of fewer parameters in which the main parameter is the inertia weight. In fact, the control of this parameter could facilitate the convergence and prevent an explosion of the swarm. The performance of the proposed algorithm is evaluated by adopting standard tests and indicators which are reported in the specialized literature. The proposed Grey MsPSO is first applied to solve the optimization problems of six benchmark functions and then, compared with the other nine variants of particle swarm optimization. In order to demonstrate the higher search performance of the proposed algorithm, the comparison is then made via two performance tests such as the standard deviation and central processing unit time. To further validate the generalization ability of the Improved OptiFel approach, the proposed algorithm is secondly applied on the Box–Jenkins Gas Furnace system and on a irrigation station prototype. A comparative study based on Mean Square Error is then performed between the proposed approach and other existing methods. As a result, the improved Grey MsPSO is well adopted to find an optimal model for the real processes with high accuracy and strong generalization ability.

Advances in efficiency optimisation control of electrical servo drive

The development of legislative acts in the world in favour of environmental protection requires the reversion of electrical drives’ specifications. Indeed, the efficiency of electrical systems has become a crucial goal in the design of control systems (Farhani et al., 2012). In this perspective, this paper proposes a new control strategy of the association of the asynchronous machine and its driver aims to reduce the energy losses taking into account the algorithmic simplicity and the robustness and stability of the control system. This method ensures the minimisation of energy losses in the induction machine. In fact, based on the operating point defined by the electromagnetic torque and the rotating speed, the controller optimises the magnitude of the rotor flux. It optimises the inverter losses by adapting the switching sequence of electronic switches according to the power factor. Numerical simulation in Simulink was used to validate the proposed approach. The results show the effectiveness of the proposed method, which can be easily adapted to any other machine, without much complexity.

Real time induction motor efficiency optimization

Abstract The induction machine efficiency optimization issue is the subject of many contributions in the literature. Few solutions, have shown interest in the rapidity-stability dilemma regarding the presence of sudden load torque variations. This paper presents a new useful hybrid approach for efficiency optimization of the direct vector-controlled induction motor drives. The efficiency improvement approach consists of adjusting the rotor flux with respect to the load torque in order to minimize total losses in the induction machine. The optimal rotor flux is defined in two steps. Firstly, the controller defines a suboptimal operating point by a fast analytical algorithm based on the model of induction machine. The suboptimal flux helps to minimize the size of possible solutions space of the optimization issue. The global optimal flux is achieved in the second step using a simulated annealing method. This latter adjusts the flux level in order to reduce the power input at its minimum. This choice overcomes the influence of parameters uncertainty on the controller stability. The proposed controller has been experimentally tested and validated on a 3 kW squirrel age induction motor.

Modelling and optimal output feedback control for discrete-time systems: Multi-model approach

This paper presents a contribution to study and synthesis an optimal output feedback controller for a class of discrete-time nonlinear systems that can be represented by a Takagi-Seguno (T-S) fuzzy models. First, we interest for modelling and identification of the studied systems by using the clustering method. In particular, we use Gustafson-Kessel (GK) clustering algorithm. Second, we address the problem of optimal controller synthesis. The optimality of the proposed control technique reside on the minimization of a quadratic criterion reflecting a compromise between fast convergence of the controlled system and the control law who must be admissible formulated as a quadratic output feedback control. Thus, the gradient technique is applied to the Lagrange function in order to obtain necessary conditions to perform the optimal control matrices. Finally, this methodology is implemented on an inverted pendulum system.