This paper presents an ecological vehicle synchronized driving control system that aims at reducing overall fuel consumption of the vehicles in a group. A centralized system for controlling the vehicles in a group has been developed using the model predictive control method considering vehicle-aerodynamics and the resistance due to road slopes. The ecological synchronized driving system is simulated on a typical road with up-down slopes for high speed driving. Its fuel saving performance is compared with a conventional vehicle following system. Computer simulation results reveal a significant improvement in fuel economy using the proposed ecological synchronized driving control system.
The air-fuel ratio is a key performance of engines. In multi-cylinder internal-combustion (IC) engines, imbalance in fuel paths between cylinders exists, which demands for modifying fuel injection of individual cylinders. This paper applies the model predictive control strategy to air-fuel ratio control through modifying the fuel injection command for individual cylinders. The control scale is with BDC (Bottom Dead Center) scale where BDC is the event that the piston reaches the bottom dead center. Experimental results show that the air-fuel ratio can be controlled to the objective value even when unknown disturbance on fuel injectors exists.
To implement and design an application of a real time fieldbus segment, engineers are obliged to estimate the communication load factor of the communication to secure the operation. According to the recent increase of the node number per segment, communication load management becomes a real issue. This paper gives the definition of the communication load and the procedure to estimate and measure the communication load of a real time fieldbus: FUNDATIONTM fieldbus and thus establish the theoretical and experimental base of the guidance to keep the periodical/stationary communication load less than 70%.
This paper investigates passivity-based visual feedback pose regulation whose goal is to control a vision camera pose so that it reaches a desirable configuration relative to a moving target object. For this purpose, we present a novel visual feedback estimation/control structure including a vision-based observer called visual motion observer under the assumption that a pattern of the target motion is available for control. We first focus on the evolution of the orientation part and the resulting estimation/control error system is proved to be passive from the observer/control input to the estimation/control error output. Accordingly, we also prove that the control objective is achieved by just closing the loop based on passivity. Then, we prove convergence of the remaining position part of the error system. We moreover extend the present velocity input to force/torque input taking account of camera robot dynamics. Finally, the effectiveness of the present estimation/control structure is demonstrated through simulation.
Gait recognition is a promising non-intrusive biometric method. A robust and compact gait model is desirable in many security applications from public facilities to personal devices. Shape cues are chosen in most current researches except a few adopting dynamical features exclusively. And most of these systems are velocity-dependent. In order to explore more features of gait and to fit the varying environments of different applications, a new gait recognition model which synthesizes dynamic model and statistical one is designed. A kind of dynamical features, angular variables with respect to ankle joint, are adopted as the model's input. The proposed model has a circular structure consisted of 2 pairs of correlated states. A constrained learning algorithm is proposed under the model's special structure configured according to a 2-link virtual passive walking model which plays an important role both in the initialization and in the updating step. By evaluating the recognition rates of different models, the velocity-robust characteristics of the new model and its low computational load compared with conventional HMM are verified.
This paper derives simple formulas in max-plus algebra to make a robust schedule for a project with atypical processes, based on the critical chain project management framework. The derived form is classified as state-space representation in control theory terminology, consisting only of simple algebraic operations. Two types of time buffers can be easily applied to achieve robustness.
This paper proposes a data-driven controller parameter tuning of the modified internal model control (IMC), which was proposed by Yamada in 1999, for unstable plants. Here the authors apply fictitious reference iterative tuning (FRIT) to the parameterized modified IMC with only one-shot experimental data. The proposed approach enables us to simultaneously obtain the optimal controller for a desired performance and an appropriate model of the actual plant, and it is applicable for unstable plants in both of the minimum phase and the non-minimum phase cases.
In most existing works on decentralized diagnosis of discrete event systems, it is implicitly assumed that diagnosis decisions of all local diagnosers are available to detect the failure. However, it may be possible that some local diagnosis decisions are not available due to some causes. Letting n be the number of local diagnosers, the notion of (n,k)-reliable codiagnosability guarantees that any occurrence of the failure can be detected by using arbitrary more than or equal to k local diagnosis decisions within a uniformly bounded number of steps. In other words, even if at most n-k local diagnosis decisions are not available, the failure can be detected by using the remaining diagnosis decisions. In this paper, a method for verifying (n,k)-reliable codiagnosability for any k is presented. Then, the delay bound within which any occurrence of the failure can be detected by using arbitrary more than or equal to k local diagnosis decisions is computed.
The authors consider the simultaneous localization and mapping (SLAM) problem with an H∞ filter and with an observation of a landmark that is known a priori. With this observation, the system satisfies observability, and the estimated error is suppressed and the determinant of its covariance matrix becomes small compared with that of the original H∞ filter. As a result, the proposed method avoids finite escape time, the divergence of the error covariance matrix that occurs in the estimation when using the original H∞ filter. We prove the convergence of the error covariance matrix. In addition, with simulations and experimental results, we confirm that finite escape time is avoided, that the derived theorems for the convergence are correct, and that we can accurately estimate the state of the robot and the environment.