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Tatsuo Narikiyo,Michihiro Kawanishi,Tomohito Mizun 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
A mobile manipulator is expected to play an important role both in the production process of factory and in a medical care system of welfare business. To come up to this expectation, a mobile mani pulator is required to simultaneously track to both the desired position trajectory and force trajectory. These tracking performances are attained by hybrid position/force control. In this paper, we propose a new adaptive control scheme for a mobile manipulator. The proposed adaptive control scheme consists of adaptive tracking control to desired position/force trajectories and robust control for unknown bounded disturbance. Effectiveness of the proposed control scheme is demonstrated by numerical simulation.
Performance bounds for optimal control of polynomial systems
Tanagorn Jennawasin,Michihiro Kawanishi,Tatsuo Narikiyo 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
An approach for nonlinear optimal control of polynomial systems is considered in this paper. We relax the HJB equation to HJB inequalities and consider solutions of the resulting inequalities in order to compute an upper bound and a lower boundo the cost function. Computation of both the upper bound and lower bound can becast as robust SDPs, which can be efficiently solved by the existing numerical tools. The idea is based on representation of the given system inalinear-like form. Our approach can be applied to search for polynomial solutions of any degree of the HJB inequalities. Sub optimal controllers are obtained interms of the solutions of the HJB inequalities.
Iterative LMI Approach to Robust State-feedback Control of Polynomial Systems with Bounded Actuators
Tanagorn Jennawasin,Michihiro Kawanishi,Tatsuo Narikiyo,David Banjerdpongchai 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.4
This paper presents a novel approach to state-feedback stabilization of polynomial systems with bounded actuators. To overcome limitation of the existing approaches, we introduce additional variables that separate the system matrices and the Lyapunov matrices. Therefore, parameterization of the state-feedback controllers is independent of the Lyapunov matrices. The proposed design condition is bilinear in the decision variables, and hence we provide an iterative algorithm to solve the design problem. At each iteration, the design condition is cast as convex optimization using the sum-of-squares technique and can be efficiently solved. In addition, the novel parameter-dependent Lyapunov functions are readily applied to robust state-feedback stabilization of polynomial systems subject to parametric uncertainty. Effectiveness of the proposed approach is demonstrated by numerical examples.