Papers by Keyword: T-S Fuzzy Model

Abstract: In this paper, a new fuzzy model-based adaptive approach for synchronization of chaotic systems with unknown parameters. Theoretical analysis based on Lyapunov stability theory is provided to verify its feasibility. Takagi-Sugeno (T-S) fuzzy model is employed to express the chaotic systems. Based on this model, an adaptive fuzzy controller and the parameters update law are developed. With the proposed scheme, parameters identification and synchronization of identical or nonidentical chaotic systems can be achieved simultaneously. Numerical simulations further demonstrate the effectiveness of the proposed scheme.

Abstract: For the bearingless synchronous reluctance motor (BSRM) is a multivariable, strong coupling, multi-input and multi-output system, based on the adaptive inverse control theory, a decoupling control method based on the T-S fuzzy inverse model identification is put forward in this paper. According to the input and output information of the system, a fuzzy inverse model of the motor control system is established, then making the inverse model and the original control system in series forms pseudo linear hybrid system to realize the approximate linearization and dynamic decoupling of the motor control system. Building the composite system and proceeding research in the Matlab/Simulink environment, the simulation results show that the control strategy can realize dynamic decoupling among the electromagnetic torque subsystem and the radial suspension force subsystem and among the x- and y-direction of the suspension force, and with excellent static and dynamic performance and adaptive ability.

Abstract: This paper researches a fuzzy control method for passive flexible joint robot. Stability control method applying T-S fuzzy model is employed for single-link flexible joint robot. T-S fuzzy model is used to approximate the flexible joint robot firstly, and then fuzzy controller is developed based on the principle of parallel distributed compensation. The fuzzy controller is also applied to the passive properties of model error. The stability conditions are proposed by Lyapunov function and linear matrix inequalities are also applied to solve the controller parameters. Simulation results show that the proposed method of application value.

Abstract: The paper proposes a fuzzy passivity non-fragile control approach for flexible joint robot. The T-S fuzzy model is applied to approximate the flexible joint robot at first, and then the fuzzy controller is developed based on parallel distributed compensation principle. The passivity non-fragile performance of controller is also employed to limit the influence of model error. The conditions for the stability of the flexible joint robot control system are proposed by using Lyapunov function, and linear matrix inequality is applied to resolve the controller parameter. The simulation experiment results show the effectiveness of the proposed method.

Abstract: The problem of fuzzy sliding mode control of discrete chaotic system is studied. Discrete chaotic system is described based on T-S fuzzy models, and the system is translated into local linear model by fuzzy method. On the basis of Lyapunov stability theorem and approaching law method, a novel sliding mode controller is designed in the paper. The controller insures the stability of global fuzzy model. The controlled certain and uncertain Henon systems are simulated with Matlab, and the numerical results demonstrate the validity and robustness of the control scheme.

Abstract: A novel sliding mode controller based on Takagi and Sugeno (T-S) fuzzy system model and optimal reaching law is presented to design the output tracking controller for the nonlinear system. The T-S fuzzy logic theory is used to build a global fuzzy state-space linear model, the sliding surface is defined by using pole assignment method, and the optimal switch control law which can drive the state variables to the sliding surface as soon as possible is designed under the condition of minimizing the defined cost function. The gains of optimal switch control law designed by using fuzzy logic algorithm alleviate the chattering phenomenon. Lyapunov equation is applied to prove the stability of controlled system. The simulation results show that the proposed approach can achieve nonlinear trajectory tracking with better performance and less chattering problem.

Abstract: In order to obtain accurate prediction model and avoid solving nonlinear programming problem, a direct adaptive predictive control (DAPC) method is proposed. Firstly, a nonlinear system was described based on Takagi-Sugeno (T-S) fuzzy models. Assuming that that the antecedent parameters of T-S models were kept, the consequent parameters were identified on-line by using the weighted recursive least square (WRLS) method. Secondly, the identified parameters of fuzzy model were used to directly receive the model predicted output with direct iterative for the T-S model. Finally, the application results for continuous stirred tank reactor (CSTR) process show that the proposed algorithm is an effective control strategy with excellent tracing ability. The proposed algorithm is a good way to resolve the two major problems, modeling and optimization, and provides a guarantee for high-precision control of nonlinear systems.

Abstract: A soft sensor modeling method is presented in this paper，it selects optimal fuzzy rules by tuning the radius of a subtractive cluster center to generate a T-S fuzzy model. The radius of a cluster center is adjusted to select optimal number of fuzzy rules, to acquire a fuzzy model with perfect generalization capability. Then, the parameter is fine-tuned by means of a hybrid gradient descent (GD) and least-squares estimation (LSE) approach. Finally, the method is used to model a PDU Naphtha’s Dry Point, simulation results show that it can determine the optimal model quickly and achieve satisfactory prediction precision.

Abstract: An observer-based H_∞ control condition is proposed for T-S fuzzy model. The observer and controller are capable of disturbance-rejection. The fuzzy version of bounded real lemma (BRL) is adopted. Output H_∞ controller and observer are designed by solving a set of bilinear matrix inequalities. The condition is shown to be less conservative than some existed results.

Abstract: In the nonlinear networked control system (NCS), the conventional control method is difficult to achieve good control performance, due to the nonlinear problem associated with the disturbance factors, such as network induced time delay and data packet dropout. Considering this situation, this paper is aimed to propose a nonlinear networked control system based on T-S fuzzy model, which does not rely on specific network parameters or mathematical model. The nonlinear problem and the uncertainties of network can be both processed by the designed fuzzy controller. Then this approach is applied to nonlinear motor servo system, simulation of the example model is implemented in Matlab/Simulink associated with True Time toolbox. The results show that the proposed method not only is convenient for modeling, but also upgrade the performance of control system.