In this paper, we address H2 optimal control problem in a behavioral framework. Firstly, we formalize H2 optimal control problem addressed here under some assumptions. Secondly, we provide a necessary and sufficient condition for a control law to be admissible : namely, it guarantees the nonrestrictiveness of the disturbance and the boundedness of H2 norm of the transfer function from disturbance variables to regulated ones as well as the stability of the interconnected system. Thirdly, we provide a necessary and sufficient condition for a control law to be H2 optimal : namely, it is an admissible control law and minimizes H2 norm of the transfer function from disturbance variables to regulated ones after the interconnection. By using this condition, we also provide a parameterization of H2 optimal control laws and then a concrete algorithm for obtaining them. Finally, an illustrative example is also given in order to show the effectiveness of our result.
In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of nonlinear systems with constrained input is presented. When designed the control, a constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. Parameters of the control are suboptimally selected by maximizing the stable region in the sense of Lyapunov with the aid of a genetic algorithm.
In this paper, we propose a new reinforcement learning (RL) method for dynamical systems that have continuous state and action spaces. Our RL method has an architecture like the actorcritic model. The critic tries to approximate the Q-function, and the actor tries to approximate a stochastic soft-max policy dependent on the Q-function. An on-line EM algorithm is used to train the critic and the actor. We apply this method to two control problems. Computer simulations in two tasks show that our method is able to acquire good control after a few learning trials.
This paper treats the simulation problem of multi-modal piecewise affine systems with binary-switches. First, it is pointed out by numerical examples that some problems on the event detection occur in the existing simulators when a solution of the system crosses an intersection of multiple switching hyperplanes. Next, a new simulation algorithm is proposed under the switch-based transition rule.
Recently, a robust method of stereo matching using coarse to fine hierarchical color segmentation on each scanline in stereo images has been proposed. However, the stereo matching method has a crucial problem that several fault correspondences cannot be removed in the case of that a lot of similar color segments exist on same scanlines. Therefore, this paper proposes an advanced approach to detect and remove fault correspondences by taking into account of consistency of disparity of region contours. Furthermore, by using a Delaunay triangulation and a finite element method, the proposed method can reconstruct 3D structures of surfaces with the contours which are obtained by a suboptimal 3D line segmentation.
A genetic algorithm (GA) is proposed to solve no-buffer jobshop scheduling problems. In the case of applying the standard GA to these problems, no-buffer constraint leads to a deadlock state in the meaning that an operation cannot be processed on the next machine in a pre-defined machine sequence. Because it causes many infeasible solutions, the standard GA cannot perform an efficient search. In this paper, we propose a new semi-active decoding method which avoids the deadlock to generate a feasible solution. Computer experiments on some modified benchmark problems show that the proposed GA performs efficient search and obtains equivalent or better schedules than other algorithms proposed previously.
Increasing the productivity by reducing waiting times for collision avoidance between adjacent robots has been an important issue in multi-robot welding systems. Minimization of welding job completion time by path optimization is one of the methods for the purpose. Optimization of directions and positions of workpieces when arranging them on the working stage is more effective method. In case of rectangular workpiece, the algorithm for optimizing directions by tabu search and positions by nonlinear programming has been developed in this research. Its effectiveness is shown by numerical simulations with practical workpieces.