196,300 research outputs found
On stability and stabilization of singular uncertain Takagi-Sugeno fuzzy systems
No full textLink to a related website: https://re.public.polimi.it/bitstream/11311/1028786/2/On%20stability%20and%20stabilization_11311-1028786_Karimi.pdf, Open Access via UnpaywallAbstract not availableM. Chadli, H.R. Karimi, P. Sh
Fault detection, isolation, and tolerant control of vehicles using soft computing methods
No full textHamid Reza Karimi, Mohammed Chadli, Peng Shi, Lixian Zhan
Discrete-time H-/H-infinity sensor fault detection observer design for nonlinear systems with parameter uncertainty
No full textAbstract not availableS. Aouaouda, M. Chadli, P. Shi and H. R. Karim
Fuzzy predictive controller design using ant colony optimization algorithm
No full textIn this paper, an approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the Ant Colony Optimization (ACO) is studied. On-line adaptive fuzzy identification is used to identify the system parameters. These parameters are used to calculate the objective function based on predictive approach and structure of RST control. The optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to calculate a sequence of future RST control actions. The obtained simulation results show that proposed approach provides better results compared with Proportional Integral-Ant Colony Optimization (PI-ACO) controller and adaptive fuzzy model predictive control (AFMPC)
New developments in mathematical control and information for fuzzy systems
No full textHamid Reza Karimi, Mohammed Chadli and Peng Sh
Faults diagnosis based on proportional integral observer for TS fuzzy model with unmeasurable premise variable
No full textIn this work, we focus on the synthesis of a Proportional Integral (PI) observer for the actuators and sensors faults diagnosis based on Takagi-Sugeno (TS) fuzzy model with unmeasurable premise variables. The faults estimation method is based on the assumption that these faults act as unknown inputs under polynomials form whose their kth derivatives are bounded. The convergence conditions of the observer as well as the faults reconstruction are established on the basis of the Lyapunov stability theory and the L2 optimization technique, expressed as Linear Matrix Inequalities (LMI) constraints. In order to validate the proposed approach, a hydraulic system with two tanks is proposed
Robust Predictive Control of a variable speed wind turbine using the LMI formalism
No full textThis paper proposes a Robust Fuzzy Multivariable Model Predictive Controller (RFMMPC) using Linear Matrix Inequalities (LMIs) formulation. The main idea is to solve at each time instant, an LMI optimization problem that incorporates input, output and Constrained Receding Horizon Predictive Control (CRHPC) constraints, and plant uncertainties, and guarantees certain robustness properties. The RFMMPC is easily designed by solving a convex optimization problem subject to LMI conditions. Then, the derived RFMMPC applied to a variable wind turbine with blade pitch and generator torque as two control inputs. The effectiveness of the proposed design is shown by simulation results
Design of unknown inputs proportional integral observers for TS fuzzy models
No full textIn this paper the design of unknown inputs proportional integral observers for Takagi-Sugeno (TS) fuzzy models subject to unmeasurable decision variables is proposed. These unknown inputs affect both state and output of the system. The synthesis of these observers is based on two hypotheses that the unknown inputs are under the polynomials form with their kth derivatives zero for the first one and bounded norm for the second one, hence two approaches. The Lyapunov theory and L2-gain technique are used to develop the stability conditions of such observers in LMIs (linear matrix inequality) formulation. A simulation example is given to validate and compare the proposed design conditions for these two approaches. © 2013 Elsevier B.V
Fuzzy control for Electric Power Steering System with assist motor current input constraints
No full textFriction and disturbances of the road are the main sources of nonlinearity in the Electric Power Steering (EPS) System. Consequently, conventional linear controllers design based on a simplified linear model of the EPS system will result in poor dynamic performance or system instability. On the other hand, a brush-type DC motor is more used in EPS control with an input current that is limited in practice. The control laws designed without taking into account the saturation effect may have undesirable consequences on the system stability. In this paper, a Takagi-Sugeno (T-S) fuzzy is used to represent the nonlinear behavior of an EPS system, and stabilization conditions for nonlinear EPS system with both constrained and saturated control input cases are proposed in terms of linear matrix inequalities (LMI). Simulation results show that both the saturated and constrained controls can stabilize the resulting closed-loop EPS system and provide a stable driving in the presence of nonlinear friction, disturbance of the road and actuator saturation
Actuator and sensor faults estimation based on proportional integral observer for TS fuzzy model
No full textThis paper presents a novel method to address a Proportional Integral observer design for the actuator and sensor faults estimation based on Takagi–Sugeno fuzzy model with unmeasurable premise variables. The faults are assumed as time-varying signals whose kth time derivatives are bounded. Using Lyapunov stability theory and L2 performance analysis, sufficient design conditions are developed for simultaneous estimation of states and time-varying actuator and sensor faults. The Proportional Integral observer gains are computed by solving the proposed conditions under Linear Matrix Inequalities constraints. A simulation example is provided to illustrate the effectiveness of the proposed approach
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