This paper proposes an identification method for discrete-time linear systems based on the quantized input-output (I/O) data in the presence of measurement noises. First, the parameter estimation error due to quantization of I/O data is analyzed from the statistical viewpoint when we adopt an instrumental variable (IV) method and the least squares (LS) one. Then, based on the result, an IV method suitably adjusted for quantized data is proposed. The validity of the proposed method is evaluated through a numerical example.
Trajectory planning is well-known control technique for differentially flat systems. However this technique requires an accurate state space model of the system, therefore it is difficult to apply this technique directly for the control of actual uncertain systems. In this paper, we proposes a new control technique based on the trajectory planning for differentially flat systems with input uncertainty in which the trajectory planning is repeated to reduce the influence of the uncertainty. We verify the performance of the proposed control technique by the numerical case studies for a two wheel vehicle model with input uncertainty, and then discuss the stability and the performance of the control system from the theoretical point of view.
This paper concerns with a new class of adaptive gain-scheduled H∞ control of linear parameter-varying (LPV) systems. The plants in this manuscript are assumed to be polytopic LPV systems, but the time-varying parameters in those plants are not available for measurement, and thus, the conventional gain-scheduled control strategy cannot be applied. In the proposed adaptive schemes, the estimates of those unknown parameters are obtained recursively, and the current estimates are fed to the controllers to stabilize the plants and to attain H∞ control performance adaptively. Stability analysis of the adaptive control systems is carried out by utilizing Lyapunov approaches based on linear matrix inequalities in the bounded real lemma.
Recently, the risk that garbages in space (space debris) conflict with a running space satellite or fall to the ground is increasing. To prevent the accident beforehand the Japan Aerospace Exploration Agency observes the space debris by a single radar whose operation plan is obtained by a heuristic technique. However, since the technique does not use global information of a prediction of the space debris orbit, the plan is far from the optimum. In this paper we propose a new technique using the global information. The method obtains an ordering of an observation of the debris on the basis of the longest path of a graph whose nodes corresponds to the debris. The number of nodes of the longest path provides an upper bound of the optimum. We experimented the proposed technique for actual prediction data. The results showed that the proposed technique can make a plan that observes twice the space debris than those of the existing technique, and that the differences from the upper bound are 0 or 1.
An evolutionary Multi-Agent System is discussed as an autonomous decentralized system in which each agent is capable of adapting to its embodied environment by means of artificial evolution. This type of system is a promising approach to adaptive system design, although it is undoubtedly difficult to analyze what they are doing or how they interact with each other to achieve a given task. Moreover, it is also difficult to understand how they evolved their function. In this paper, the ecological method is experimentally applied to a multi-agent system to analyze the system dynamics. A food collecting problem is selected to illustrate how to use the ecological method to grasp characteristics of agents in a Multi-Agent System. After the consideration of the analytical results, we succeeded in improving the performance of the system. This proves that the ecological method for natural systems can effectively be applied to multi-agent systems.