Table of contents

2009 2009 17th Mediterranean Conference on Control and Automation  
This paper deals with sufficient conditions of asymptotic stability for non linear discrete-time 2D systems represented by a Takagi-Sugenofuzzy model of Roesser type with state feedback control. This work is based on common and multiple Lyapunov functions. The results are presented in LMI's form. The problem of state-feedback control design for discrete-time Takagi-Sugeno fuzzy systems is investigated in this paper. The strategy relies on the use of a quadratic in the state Lyapunov function
more » ... t presents a homogeneous polynomial dependence of arbitrary degree g on the first instant of time of the premise variables and a multi-affine dependence on the successive instants of time of the premise variables until a maximum instant of time M. The tests cast in the form of LMI relaxations parametrized on both g and M and a feasible solution yields a non-PDC controller based on homogeneous polynomial matrices. Numerical examples show that the approach can be less conservative and more efficient than other methods available in the literature. Ant colony optimization (ACO) is one of the swarm intelligence (SI) techniques. It is a bio-inspired optimization method that has proven its success through various combinatorial optimization problems. This paper proposes an ant colony optimization algorithm for tuning fuzzy PID controllers. First, the design of typical Takagi-Sugeno (TS) fuzzy PID controllers is investigated. The tuning parameters of these controllers have physical meaning which makes its tuning task easier than conventional PID controllers. Simulation examples are provided to illustrate the efficiency of the proposed method. This paper deals with the problem of delay-dependent stability and stabilization of Takagi-Sugeno (T-S) fuzzy systems with a time-varying delay. A new method which bring three great advantages for the T-S delaydependent stabilization problem is developed. The first is that this method is less conservative than other existing ones. The second is the reduction of computational complexity when the number of IF-THEN rules r is control direction. The proposed adaptive neural network control is free of control singularity problem. Simulation results are provided to show the effectiveness of the proposed approach. 10:40-11:00 WeA2.4 Robust Adaptive Fuzzy Tracking Control for a Class of Nonlinear Systems, pp. 49-54 Essounbouli, Najib Reims Univ. Hamzaoui, Abdelaziz IUT of Troyes This paper deals with the design of a robust adaptive fuzzy tracking control for a class of uncertain and disturbed nonlinear systems. In addition to the desired performances and the convergence of the tracking error, the proposed approach guarantees the uniformly ultimately boundedness of the resulting closed-loop system. Furthermore, it allows overcoming many problems related to adaptive fuzzy controllers like singularity problem and constraints on the control gain, and reducing the number of used parameters, which encourage the realtime implementation. Finally two simulation examples are presented to show the effectiveness and the performances of the proposed approach. 11:00-11:20 WeA2.5 Adaptive Fuzzy Controller for Loop Control in a Distributed Control System, pp. 55-60 Abdel-geliel, Mostafa Arab Acad. for Science and Tech. Khalil, Alaa Eldin Ahmed Arab Acad. for Science & Tech. to simplify the control task and reduce the computation burden of control system, Distributed Control system (DCS) becomes the most suitable control system structure especially for medium and large size of industrial processes. In DCS system the control task is distributed among some controllers, which communicate to each other via communication network, such as PLC or/and industrial PC. In most DCS system, each controlled variable is manipulated in an individual loop, which is called control loops in DCS. Since it is difficult to design a control function that can handle all the circumstances of operations at the start phase, the control function needs to be adapted online. Adapted fuzzy controlled is suggested here in order to handle the control loops of a DCS system. An Experimental setup simulates the master loops of Liquefied Petroleum Gases (LPG) subsystem in a refining petroleum industry. The controller is implemented using HP-VEE software and Matlab packages. 11:20-11:40 WeA2.6 Adaptive Predictive Control Using Recurrent Neural Network Identification, pp. 61-66 Akpan, Vincent Aristotle Univ. of Thessaloniki Hassapis, George Aristotle Univ. of Thessaloniki This paper presents a new adaptive predictive control algorithm which consists of an on-line process identification part and a predictive control strategy which is updated every time a process model change is identified. The identification method is based on recurrent neural network nonlinear AutoRegressive with eXternal input (NNARX) model derived from dynamic feedforward neural network by adding feedback connection between output and input layers. Two model-based predictive control strategies have been studied: the generalized predictive controller (GPC) and nonlinear adaptive model predictive controller (NAMPC). The neural network training and validation data are obtained from the open-loop simulation of a validated first principles plant model. The identified neural network (NN) model is validated using the following three different validation algorithms: (1) one-step ahead cross-validation of the training and test data predicted by the trained network; (2) Akaike's final prediction error (AFPE) estimate of the average generalization error; (3) 5-step prediction simulations. The algorithm has been applied to the temperature control of a fluidized bed furnace reactor of the steam deactivation unit of a fluid catalytic cracking (FCC) pilot plant used to evaluate catalyst performance. The validation results show that the RNN models the reactor to a high degree of accuracy. Simulation results show that the proposed NAMPC control strategy outperforms the GPC at the expense of extra computation time. WeA3 Room 3 Mobile Robots (Regular Session) Chair: Parlangeli, Gianfranco Univ. degli studi di Lecce Co-Chair: Zohar, Ilan Ben-Gurion Univ. of the Negev 09:40-10:00 WeA3.1 Controllers for Mobile Robot Dynamic Models: Trajectory Tracking with Applications to Convoy-Like Vehicles, pp. 67-72 Zohar, Ilan Ben-Gurion Univ. of the Negev Ailon, Amit Ben Gurion Univ. of The Negev Rabinovici, Raul Ben-Gurion Univ. of the Negev This paper establishes control strategies for a wheeled mobile robot model that includes the kinematic and dynamic equations of motion. The vehicle model accounts also for the actuator dynamics. The paper proposes simple control schemes for tracking a time-parameterizing path. Applications of the tracking controller for convoylike vehicles are presented. Simulation results and demonstrations of the controller performances are discussed. This paper proposes an algorithm for planning mathcal{C}^infty paths with bound curvature and curvature derivative linking two fixed (initial and final) configurations and passing through a given number of intermediate via-points. The proposed solution is derived solving an optimization problem such that a smooth curve of bounded curvature and curvature derivative approximates Dubin's shortest paths. The effectiveness of such strategy is verified by simulations. Preliminary experimental results are also briefly described. WeA3.5 Modeling and Motion Control of an Articulated-Frame-Steering Hydraulic Mobile Machine, pp. 92-97 Ghabcheloo, Reza Tampere Univ. of Tech. Hyvonen, Mika Tampere Univ. of Tech. The paper addresses autonomous motion control (path-following in particular) of an articulated-frame-steering (AFS) hydraulically actuated mobile machine. We first propose a kinematic model of the vehicle, together with a simple model for steering hydraulic actuator. The kinematic model is derived under simplifying assumptions that there are no slipping and no skidding. The accuracy of the model is then validated using an elaborated semiempirical hardware-in-the-loop simulator (GIMsim) of an AFS machine built at IHA/TUT. A motion control strategy is then proposed and a path-following control law is derived. Finally, the efficacy of the methodology is shown using GIMsim.
doi:10.1109/med.2009.5164498 fatcat:bi37lbkbhfaihj7lbc64vvplo4