Minoru Hayase

Design of H∞ controller for precise positioning and comparison with PID control

A new control scheme based on H∞ control theory is proposed for precise positioning. In comparison with PID control, the validity of the proposed scheme has been confirmed by simulations at the micron scale. The effects of disturbances such as Coulomb friction and mechanical resonance mode are treated explicitly at the controller design stage. The proposed control scheme exhibits rapid response with good suppression of the effects of resonance vibration and Coulomb friction. Root locus simulation results for the closed-loop transfer function have shown that H∞ control is able to stabilise the mechanical resonance frequency mode, whereas PID control is not. Frequency response simulation results for the closed-loop transfer function of H∞ control have shown that the cut-off frequency increases and the mechanical resonance mode stabilises simultaneously when the H∞ norm condition, γ, is decreased.

Design of controllers and observers for mimo nonlinear systems

This paper proposes new methods for design ing observers and controllers for nonlinear systems. The design procedure is reduced to find proper ob server and controller gains. The validity of the proposed methods is verified by designing output feed back controllers for the positioning control of two link robot arms.

Integration of Linear Systems and Neural Networks for Identification and Output Feedback Control of Nonlinear Systems

Abstract This paper analyzes the integration of linear systems and neural networks for the identification, state estimation and output feedback control of weakly nonlinear systems. Considering previous knowledge about the system given by approximated linear state-space models, linear observers and linear controllers; training algorithms for neuro-identification, state neuroestimation and neuro-control were derived considering the dynamics of the nonlinear system. It was found that the integrated linear-neuro model can identify the dynamics of the system much more accurately than a purely linear model or a purely neuro model. It was also found that the state estimation and control performance of the system with integrated linear-neuro output feedback control is better than the system with linear control or neuro-control.

Mixed H/sub 2//H//sub/spl infin// output feedback controller

This paper presents the solution to the output feedback mixed H/sub 2/H//sub/spl infin// control problem via the solution of an associated Nash game. Consider ing a two-player nonzero sum game, two perfor mance criteria are used, one reflecting an H//spl/sub infin// constraint and the another reflecting an H/sub 2/ optimal ity requirement. Assuming that the mixed H/sub 2/H//sub/spl infin// controller has the separation structure of H/sub 2/ and H//sub/spl infin// controllers, the feedback and observer gains of the mixed H/sub 2/H//sub/spl infin// controller are determined by solving five coupled Riccati equations.

Integration of linear systems and neural networks for identification and control of nonlinear systems

This paper analyzes the integration of linear sys tems and neural networks for the identification and optimal control of weakly nonlinear systems. Considering previous knowledge about the system given by approximated linear state-space equation mod els and linear controllers, training algorithms for identification and control were derived considering the dynamics of the nonlinear system. It was found that the integrated linear-neuro model can identify the dynamics of the system much more accurately than a purely linear model or a purely neuro model. It was also found that the vibration isolation pefor mance of the system with integrated linear-neuro control is much better than the system with linear control or neuro-control.

Shortest Trajectory Control of Autonomous Mobile Robots Using Nonlinear Observers

Abstract This paper deals with two problems related to autonomous mobile robots. 1 lie first problem is to determine the control strategy so that the robot describes the shortest trajectory linking an arbitrary position inside a working area and a path to be followed. This problem is solved by considering an equivalent minimum-time control problem and the control switching functions for obtaining the shortest trajectory has been determined in a general form. The second problem is the navigation problem and consists in designing a nonlinear observer for the dynamic estimation of the variables required to implement the shortest-trajectory control. A Luenberger-like observer for MIMO nonlinear systems is proposed and analyzed. Theoretical and experimental analyses show that the observer converges fastly enough so that path tracking can be efficiently implemented.