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Path Tracking Control of Lagrange Systems with Obstacle Avoidance
Kazunori Sakurama,Kazushi Nakano 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.1
This paper addresses a path tracking problem with obstacle avoidance for Lagrange systems. The proposed method is based on field potential methods in combination with navigation functions for obstacle avoidance. First, it is shown that a simple combination of the navigation function with the conventional path tracking controller does not work. Therefore, in order to cope with this problem, a new feedback law is proposed for a path parameter which characterizes the reference path. It is proved that the proposed controller achieves both path following and collision avoidance. Moreover, since the method adopts bounded navigation functions, the proposed controllers generate bounded input signals even when target systems approach obstacles. Finally, an experimental evaluation is performed with a two-link manipulator to illustrate the effectiveness of the proposed method.
Parameter Determining Method of Robust Digital Controller for PWM Power Amplier
Tokubai Ki,Kohji HIGUCHI,Kazushi NAKANO,Tatsuyoshi KAJIKAWA,Satoshi YOSHIZAWA,Koji MATSUSHITA,Fumiho CHINO 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper a parameter determing method of digital controller is proposed for spreading capacitance load range more and improving transient charateristics for inductance load. In the previous proposed controller, the conguration of the controller is the same to all loads. At inductive load, there is a possibility that the transient characteristics may become bad and the specication may not be satised. Therefore, a load currentis estimated at inductive load, and the controller which also uses load cur-rent for feedback is congured. ADSP is implemented to this digital controller. It is demonstrated from experiments that the digital controller of which parameters are deter-mined by the proposed method satises the given speci-cations.
Jorge Ivan Medina Martinez,Kazushi Nakano,Kohji Higuchi 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper describes the parameter estimation and update in neural networks (NN) using a modified version of simultaneous perturbation stochastic approximation (SPSA) algorithm in order to obtain a low computational costand better performance in the proposed system here. Also, this SPSA is used as learning rule applied to a neuro-controller (NC). In this paper, we apply a direct inverse control scheme by a NN. The NN must learn an inverse system of the objective plant. When using a type of gradient method as a learning rule of the NN, the Jacobian of the plant is required. On the other hand, this control scheme described here does not require any information about the plant Jacobian, because the modified version of SPSA estimates the gradient using only values of the error defined by output of the plant and its desired one. We propose to reduce the oscillation in the single flexible link used as plant in this paper in order to confirm the feasibility of the proposed method.