Nabil Derbel

Stability and robustness of fuzzy adaptive control of nonlinear systems

In this paper, we propose a new approach that guarantees the stability and robustness of an adaptive control law of a nonlinear system. The control diagram proposed contains a Takagi-Sugeno-Kang fuzzy controller (TSK-FC) and a training block allowing the online adaptation of the FC parameters. The adaptation algorithm used is based on the gradient with minimization of the quadratic error between the system output and that desired by using the direct method of Lyapunov. However, our approach considers the gradient step of each adaptive FC parameter to be bound. This approach was applied to the control of an inverted pendulum. The results obtained confirm well the validity of such an adaptation especially the guarantee of the pendulum stability and the robustness of its control with respect to the disturbances introduced on the FC parameters and the pendulum itself.

FPGA implementation of fuzzy wall-following control

The objective of this study concerns the design and implementation of a complete intelligent mechatronic system. The basic idea uses the concept of car maneuvers; control (fuzzy logic controller) and sensor-based behaviors together merged to implement the wall-following control algorithm. The fuzzy logic control algorithm (FLC) was considered as the heart of the controller due to the advantage of its easy implementation on an FPGA (field programmable gate array). The FLC is implemented on a compact custom FPGA board, which provides a powerful reconfigurable hardware platform and software design, at the same time. Complementing the system, a CPU synthesized on the FPGA takes care of interfacing with the external world. The FPGA board and the hardware network are demonstrated in the form of a controller embedded on the prototype car for a task of wall-following and obstacle avoidance. Experimental results on a car-like robot show that the algorithm proposed can successfully navigate the robot to follow the wall in an unknown and changing environment.

Optimal Control for Discrete Large Scale Nonlinear Systems Using Hierarchical Fuzzy Systems

This paper presents a method to compute optimal control strategies of discrete large scale nonlinear systems by using hierarchical fuzzy systems. The method is based on the decomposition principle of the global system into interconnected subsystems becoming easier to study. Then, the differential dynamic programming procedure is applied in order to obtain the rule basis. After that, we construct limpid-hierarchical Mamdani fuzzy system in order to compute optimal control laws, for each subsystem. Simulation results of a rotary crane show that the proposed method yields to satisfactory performances. The robustness of the proposed approach is verified.

Optimization of a rail to rail low voltage CCII for active filter applications

In this paper, we deal with an ultra Low voltage Rail to Rail high performance second generation current conveyor (CCII). An optimum transistor sizing was done applying a C++ software. The proposed circuit is able to operate at low voltage supply (plusmn 0.75TV). First, a frequency characterization of the optimized CCII is presented; PSPICE simulation for a 0.35mum CMOS technology show that best performances are obtained with the improved configuration. The current and voltage bandwidths are respectively 1.4834GHz and 2.035GHz, and the parasitic resistance at port X has a value of 87.65Omega for a control voltage of 0.35V. Then, these performances and the low power consumption are quite satisfactory for most RF applications: This CCII was used to realize a new current-mode Programmable Biquad filter and a Universal filter. PSPICE simulation results are included to demonstrate the results.

Variable structure neural networks for adaptive control of nonlinear systems using the stochastic approximation

Abstract This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. Referred to as a variable structure neural network, a novel neural network architecture, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable structure neural network, the number of basis functions can be either increased or decreased with time according specified design strategies so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GBRF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GBRFs are analysed using a new method inspired from the adaptive diffuse element method combined with a pruning algorithm. In the standard problem of a feedback based control, the cost to be minimized is a function of the output derivative. When the cost function depends on the output error, the gradient method cannot be applied to adjust the neural network parameters. In this case, the stochastic approximation approach allows the computation of the cost function derivatives. The developed weight adaptive laws use a stochastic approximation algorithm. This algorithm consists of the use of the Kiefer–Wolfowitz method.

Comparative study of voltage controlled current sources for biompedance measurements

For bioimpedance measurement, the design of a voltage controlled current source (VCCS) is important to have a good compromise between performance of final results and patient safety. VCCS should have simultaneously large output impedance and wide bandwidth. In this paper, we compare between the Tietze topology, with and without Negative Capacitance Circuit (NCC), and the Improved Howland Current pump ICSA topology. Best results were reached with a Tietze current source with NCC over a wideband frequency range from 10 KHz to 1 MHz. The experiments show that the Tietze VCCS is able to drive loads of 25 Ω to 10 KΩ with current amplitude below 0.5 mA within the accuracy of 0.57% in practice. This fulfills even the requirements for bioimpedance measurements especially at high frequency and high load impedance.

CONTROLLER DESIGN AND ANALYSIS FOR A TWO-CELL DC-DC CONVERTER IN THE PRESENCE OF SATURATION

In this work, a zero static error proportional controller for a two-cell DC-DC buck converter is synthesized and analyzed. Under a traditional proportional control scheme, the system presents a constant error of the current supplying the output load. As the proportional feedback gain is increased, the average static error decreases. However, subharmonic oscillations and chaotic behavior emerge beyond successive bifurcations. To achieve zero current and zero voltage static error, we suggest a modification of the traditional proportional controller. By optimizing the feedback gain, the settling time is also decreased. Then, using nonlinear analysis and Lyapunov stability theory, we prove that zero static error is achieved even in the presence of duty cycle saturation. Numerical simulations are presented to confirm our theoretical results.

Classification of rotor fault in induction machine using Artificial Neural Networks

In this paper, we have developed a feedforward neural networks to detect and to diagnosis rotor fault on induction motors using stator currents. In the first step, causes and effects of rotor fault have been studied, particularly, the number of broken bars has been considered. Then, in the second step, the number of broken rotor bars has been localized by Artificial Neural Networks (ANN), using the Fast Fourier Transform. Simulation results show that the Neural Network proposed approach presents a good tools for the diagnostic of induction machines.

A sliding mode control applied to whirling pendulum

In this paper, we present a VSS-based tracking control for a fixed point regulation of 2-DOF underactuated manipulators. The design approach is systematic and relies on the selection of appropriate choice of two sliding surface. The controller is designed for the both sliding surfaces are attractive. Lyapunov theory is used to study and demonstrate the stability of the system. Convergence of state space is proved and robustness issues to bounded disturbances are also investigated. Simulation results on 2-DOF whirling pendulum are presented to chow the effectiveness of this approach.

On the frequency compensation of simulated CCII based tunable floating inductance for LC ladder filters applications

In this paper, we introduce a new frequency compensation method of the CCII based floating inductance. In order to get tunable characteristics of the proposed inductance, a translinear CCII is implemented for its controllable series parasitic resistance at port X. After achieving a frequency characterization of the proposed CCII, we analyze the frequency limitations of the conventional CCII based tunable floating simulated inductance which is independent of tuning current. To overcome these limitations, a pole/zero compensation strategy is applied. Therefore, a series passive resistance Rs=650Q is used and a high active negative resistance is introduced by means of CCIIs. Simulation results show that the proposed compensation technique enlarges the tuning range of the inductance. Moreover, a special arrangement is proposed so that the compensation solution is insensitive to the control current tuning the inductance value.