This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning method, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear combination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.

We propose a weighted subtask controller and sufficient conditions for boundedness of the controller both velocity and acceleration domain. Prior to designing the subtask controller, a task controller is designed for global asymptotic stability of task space error and subtask error. Although the subtask error converges to zero by the task controller, the boundedness of the subtask controller is also important, therefore its boundedness conditions are presented. The weighted pseudo inverse is introduced to relax the constraints of the null-space of Jacobian. Using the pseudo inverse, we design subtask controller and propose sufficient conditions for boundedness of the auxiliary signal to show the existence of the inverse kinematic solution. The results of experiments using 7-DOF WAM show the effectiveness of the proposed controller.

This paper addresses the L-gain filtering problem for continuous-time linear systems with time-varying structured uncertainties and non-zero initial conditions. We propose a full order linear filter that renders the L-gain from disturbance to filtering error within a prescribed level by solving a linear matrix inequality (LMI) feasibility problem. The filter gain is specified by the solution to a set of LMI's. A numerical example is given to illustrate the proposed method.

In this paper, we propose a robust state estimation method using a particle filter (PF) for a class of nonlinear systems which have stochastic parameter uncertainties. A robust PF was designed using prediction and correction structure. The proposed PF draws particles from a simple proposal density function and corrects the particles with particle-wise correction gains. We present a method to obtain an error variance of each particle and its upper bound, which is minimized to determine the correction gain. The proposed method is less restrictive on system nonlinearities and noise statistics; moreover, it can be applied regardless of system stability. The effectiveness of the proposed robust PF is illustrated via an example based on Chua's circuit.

This paper presents a new fuzzy dynamic output feedback controller design technique for the Takagi Sugeno (T-S) fuzzy model with unknown-but-bounded time-varying modeling error. It is shown that the quadratic stabilization problem of the T-S fuzzy modeled system can be converted into an H control problem of the scaled polytopic Linear Parameter Varying (LPV) system. Then, a controller satisfying a prescribed H performance is designed for the stabilization of the T-S fuzzy modeled system.

The robust induced l-norm control problem is considered for uncertain discrete-time systems. We propose a state feedback and an output feedback controller that quadratically stabilize the systems and satisfy a given constraint on the induced l-norm. Both controllers are constructed by solving a set of scalar-dependent linear matrix inequalities (LMI's), and the gain matrices are characterized by the solution to the LMI's.

In this paper, we propose a new robust model predictive control (MPC) technique for linear parameter varying (LPV) systems expressed as linear systems with feedback parameters. It is based on the minimization of the upper bound of finite horizon cost function using a new parameter dependent terminal weighting matrix. The proposed parameter dependent terminal weighting matrix for norm-bounded uncertain models provides a less conservative condition for terminal inequality. The optimization problem that satisfies the terminal inequality is solved by semi-definite programming involving linear matrix inequalities (LMIs). A numerical example is included to illustrate the effectiveness of the proposed method.

This paper presents the stability analysis for continuous-time Takagi-Sugeno fuzzy systems using a fuzzy Lyapunov function. The proposed fuzzy Lyapunov function involves the time derivatives of states to include new free matrices in the LMI stability conditions. These free matrices extend the solution space for Linear Matrix Inequalities (LMIs) problems. Numerical examples illustrate the effectiveness of the proposed methods.

In this paper, we propose a delayed feedback guaranteed cost controller design method for uncertain linear systems with delays in states. Based on the Lyapunov method, an LMI optimization problem is formulated to design a delayed feedback controller which minimizes the upper bound of a given quadratic cost function. Numerical examples show the effectiveness of the proposed method.

This paper proposes an updating state dependent disturbance observer (USDDOB) to reject position dependent disturbances when parameters vary slowly, and input and output are time-delayed. To reject the effects of resultant slowly-varying position dependent disturbances, the USDDOB uses the control method of the state dependent disturbance observer (SDDOB) and time-invariance approximation. The USDDOB and a main proportional integral (PI) controller constitute a robust controller. Simulations and experiments using a 1-degree-of-freedom (1-DOF) tilted planar robot show the effectiveness of the proposed method.

We propose a motor speed ripple elimination method using a state dependent disturbance observer (SDDOB). The SDDOB eliminates the state dependent disturbance in the system regardless of the operation frequency, input time delay and output time delay. The SDDOB and a main proportional integral (PI) controller constitute a robust motor speed controller. Experimental results show the effectiveness of the proposed method.