Volume 3, No. 3, July 2007        ISSN: 1743-9310

Title: ITERATIVE LEARNING CONTROL SCHEMES FOR ROBOT MANIPULATORS
Authors: F. Bouakrif, D. Boukhetala, F. Boudjema ............................................pp. 104-112
Abstract: This paper deals with iterative learning control “ILC” design to solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances, and performing repetitive tasks. Two ILC schemes are considered and analysed, the first controller contains a feedback action plus an iteratively updated term, and the second is an iterative learning control without adding the feedback controller. The proof of the asymptotic stability is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed control schemes. Finally, simulation results on two-link manipulator are provided to illustrate the effectiveness of the proposed controller.
Keywords: Iterative Learning Control, Lyapunov Method, Robot Manipulator.

Title: INTELLIGENT INTEGRATED CONTROL OF THE POWER FLOWS INTO AN ENERGY GENERATION SYSTEM
Authors: N. Bizon ,............................................................................................pp. 113-125
Abstract: This paper presents a new integrated control of the power flow into an Energy Generation System (EGS) under load pulse using fuzzy controller. The proposed fuzzy control integrates two controls in one in order to decrease cost as only three transducers are used, and to increase efficiency. The input control variables are: fuel cell current, ultracapacitors stack voltage and batteries stack voltage. Fuzzy rules base is obtained by interblending the fuzzy rules bases of the two separated fuzzy controllers: first for power flow control between fuel cell and batteries stack, and second for power flow control between ultracapacitor stack and batteries stack. The simulation results show that the EGS behavior can be well controlled using a fuzzy controller with a well designed 3D control surface.
Keywords: Energy Generation System, Fuel Cell, Batteries and Ultracapacitors Stack, Power Converter, Boundary Control, Fuzzy Controls.

Title: A FUZZY MODEL REFERENCE LEARNING CONTROLLER OF ASMES TO IMPROVE TRANSIENT POWER SYSTEM STABILITY
Authors: A. Naceri, Y. Ramdani, H. Bounouna.,.................................................pp. 126-133
Abstract: The power system models for transient stability studies are nonlinear and complex. Their parameters slowly change with time due to environmental effects or rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. Many control techniques that using Advanced Super-conducting Magnetic Energy Storage (ASMES) to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a Fuzzy Model Reference Learning Controller (FMRLC) is proposed in this paper, it investigates multi-input multi-output (MIMO) FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, where the focus is on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., use online data to continually learn the fuzzy controller which will ensure that the performance objectives are met). Simulation results show that the proposed controller is able to work with a non-linear and non-stationary power system (i.e., single machine - infinite bus system), under various fault conditions and disturbances.
Keywords: Transient Power System Stability, ASMES, Current Source Inverter (CSI), MIMO Fuzzy Controllers, Self-Learning, Reference Model, Adaptive Control.

Title: TWO APPROACHES FOR DIRECT TORQUE CONTROL USING A THREE-LEVEL VOLTAGE SOURCE INVERTER WITH REAL TIME ESTIMATION OF AN INDUCTION MOTOR STATOR RESISTANCE
Authors: R. Zaimeddine, E. M. Berkouk, L. Refoufi,............................................pp.134-142
Abstract: This paper studies a new control structure for sensorless induction machine dedicated to electrical drives using a three-level voltage source inverter (VSI) where the amplitude and the rotating speed of the flux vector can be freely controlled, and both fast torque response and optimal switching logic can be achieved. The selection is based on the value of the stator flux and the torque. We propose two approaches, the first is the new simple method derived from Takahash’s strategy in which we divide the dq-plane into 6 regions. In the second approach, we enhance the response of torque and flux with optimal switching strategies. A new DTC scheme of induction motors is proposed in order to develop a suitable dynamic. Direct Torque Control (DTC) uses only a multi-level comparator to perform both torque and flux dynamic control. In addition, to improve the system performance a fuzzy resistance estimator is proposed to solve this common problem in DTC control. Both approaches for DTC controller are simulated for an induction motor. The results obtained indicate superior performance without the need of any mechanical sensor.
Keywords: Induction Motor, Direct Torque Control, Fast Torque Response, Sensorless Vector Control, Fuzzy Control, Switching Strategy Optimisation, Three-Level Neutral-Point Clamped.

Title: CONTROL OF SEVEN-LEVEL OPTIMIZED VOLTAGE INVERTER BASED ON SELECTIVE HARMONICS ELIMINATION AND ARTIFICIAL NEURAL NETWORK
Authors: K. Imarazene, H. Chekireb, E. M. Berkouk ..........................................pp.143-150
Abstract: This paper deals with artificial neural network control of the seven level optimized inverter based on selective harmonics elimination (SHE). We determine the adequate switching angles according to modulation index for the control of the 3, 5 and 7 levels inverter based on SHE strategy. For the seven level inverter, there exist, a double solution for the switching angles and we select the switching curves offering the best total harmonic distortion (THD). The optimization carried out for the seven level inverter is based on the selection of the switching curves offering the best THD among the switching curves corresponding to the three, five or seven level inverter. So, this optimized seven level inverter can operate on the 3, 5 or 7 levels with the best THD while ensuring the elimination of the undesirable harmonics. Moreover, a static neural network is dimensioned by supervised training and is able to generate, in real time, the switching curves of this seven level optimized inverter.
Keywords: Multilevel, Inverter, PWM Strategy, Harmonic Elimination, ANN, THD.