Tadanari Taniguchi

Model construction, rule reduction, and robust compensation for generalized form of Takagi-Sugeno fuzzy systems

This paper presents a systematic procedure of fuzzy control system design that consists of fuzzy model construction, rule reduction, and robust compensation for nonlinear systems. The model construction part replaces the nonlinear dynamics of a system with a generalized form of Takagi-Sugeno fuzzy systems, which is newly developed by us. The generalized form has a decomposed structure for each element of A/sub i/ and B/sub i/ matrices in consequent parts. The key feature of this structure is that it is suitable for constructing IF-THEN rules and reducing the number of IF-THEN rules. The rule reduction part provides a successive procedure to reduce the number of IF-THEN rules. Furthermore, we convert the reduction error between reduced fuzzy models and a system to model uncertainties of reduced fuzzy models. The robust compensation part achieves the decay rate controller design guaranteeing robust stability for the model uncertainties. Finally, two examples demonstrate the utility of the systematic procedure developed.

On improvement of stability conditions for continuous Mamdani-like fuzzy systems

This paper is concerned with continuous fuzzy systems with singleton consequents, called type II fuzzy systems. It first introduces the canonical form of an unforced type II fuzzy system and its stability theorem presented in the previous study. Then, improving the stability theorem, it gives necessary and sufficient conditions such that a type II fuzzy system is stable with respect to a global quadratic Lyapunov function.

Stabilization of nonlinear systems with piecewise bilinear models derived from fuzzy if-then rules with singletons

This paper deals with the modeling and stabilization of nonlinear control systems approximated by fuzzy if-then rules with singleton consequents. The approximated model is found to be piecewise bilinear. First, an optimal method of piecewise bilinear approximation of nonlinear functions is shown. Then, the method of input-output feedback linearization is applied to stabilize piecewise bilinear control systems. An illustrative example is given to show the validity of the proposed methods.

Stabilization of nonlinear systems based on piecewise Lyapunov functions

This work presents a stabilization algorithm for piecewise approximated nonlinear systems based on piecewise Lyapunov functions. The stabilizing conditions are expressed in terms of bilinear matrix inequalities (BMIs). A large-scale BMI problem is decomposed in regional BMI problems. Connecting regional solutions of BMIs, we propose a design algorithm for a semi-global stabilizing controller. Some illustrated examples are given to show the validity of the proposed method.

Quadrotor control using dynamic feedback linearization based on piecewise bilinear models

This paper proposes a tracking controller for a four rotors helicopter robot using dynamic feedback linearization based on piecewise bilinear (PB) models. The approximated model is fully parametric. Input-output (I/O) dynamic feedback linearization is applied to stabilize PB control system. Although the controller is simpler than the conventional I/O feedback linearization controller, the control performance based on PB model is the same as the conventional one. Examples are shown to confirm the feasibility of our proposals by computer simulations.

Structure-oriented design for a class of nonlinear systems

Presents a structure-oriented design for Takagi-Sugeno fuzzy control systems. Structure-oriented design conditions that simultaneously determine the feedback gains and the structure of a given control system are derived from a performance index optimization point of view. Furthermore, we discuss an LMI constraint on the time derivative of states from a practical actuator performance point of view. Simulation results show the effectiveness of the optimal feedback design and the structure-oriented design.

Model Following Control of a Unicycle Mobile Robot via Dynamic Feedback Linearization Based on Piecewise Bilinear Models

We propose a dynamic feedback linearization of a unicycle as a non-holonomic system with a piecewise bilinear (PB) model. Inputoutput (I/O) dynamic feedback linearization is applied to stabilize PB control system. We propose a method for nonlinear model following controller to the unicycle robot. Although the controller is simpler than the conventional I/O feedback linearization controller, the tracking performance based on PB model is the same as the conventional one. Examples are shown to confirm the feasibility of our proposals by computer simulations.

PB model-based FEL and its application to Driving Pattern Learning

Abstract A significant part of the vehicle control software is based on Look-Up-Tables (LUTs) that define the mappings from different combinations of multiple independent (input) variables to the values of one dependent (output). LUTs are used as feedforward controllers or as gain-scheduling parameters for feedback controllers. The LUT feedforward controllers can be viewed as inverse vehicle models capturing the strong nonlinearity and multimodal behaviors that can be (in many cases) formalized only by experimentally measured data under different operating conditions. The paper proposes a Feedback Error Learning (FEL) based method for adaptation of the LUT feedforward controllers in order to match the desired and actual vehicle performance. The FEL is an on-line learning strategy acquiring an inverse model of a plant through feedback control actions. In this paper we consider the driver demand LUTs as a feedforward controller defining the relationship between the accelerator pedal position, the engine speed, and corresponding brake torque and the driver as a feedback controller. The FEL scheme have been implemented through Piecewise Bilinear (PB) models which can be expressed as LUTs and are very convenient with regard to nonlinear modeling, control objective and on-line learning capability.

Robust Lookup Table Controller Based on Piecewise Multi-linear Model for Nonlinear Systems with Parametric Uncertainty

This paper proposes a robust lookup table controller based on piecewise multi-linear model for nonlinear systems with parametric uncertainty. We construct a piecewise multi-linear model of a nonlinear system. The model is a nonlinear approximation and the model can be derived from fuzzy if-then rules with singleton consequents. The piecewise model can be expressed as a lookup table. The model dynamics is described by multi-linear interpolation of the lookup table elements. We design a robust piecewise multi-linear controller for the piecewise model via feedback linearization. The robust piecewise controller can be also expressed as a lookup table. We apply the robust lookup table controller to ball and beam system as a nonlinear system with parametric uncertainty. Examples are shown to confirm the feasibility of our proposals by computer simulations.

Analysis and classification of university students’ educational skills using a computer-assisted web-interviewing questionnaire

Abstract In this study, we seek to develop an analytic method to set key educational skills (ESs), using a student self-assessment questionnaire. It is difficult to set key academic skills for classes since few systematic methods are available. A survey featuring 24 ESs was conducted in three ICT classes in our university using a computer-assisted web-interviewing (CAWI) technique. The questionnaire results were analyzed through Students’ T-test and multidimensional scaling (MDS). An MDS helps to visualize the similarities between these ESs. We demonstrated that the 24 skills can be classified into the following three skills: basic academic skills, practical academic skills, and interpersonal skills.