Design methodologies of finite-dimensional adaptive H∞ consensus control of multi-agent systems composed of a class of infinite-dimensional systems are provided in this paper. The proposed control schemes are derived as solutions of certain H∞ control problems, where the effects of neglected infinite-dimensional modes and the imperfect knowledge of the leader are regarded as external disturbances to the process. It is shown that the resulting control systems are robust to uncertain system parameters and that the desirable consensus tracking is achieved approximately via finite-dimensional adaptive control schemes.
In this study, the authors report results of month-long experiments conducted in two elderly recipients' houses with a new reminder system to keep recipient medication condition up to date. This system consisted of three main components: a new intelligent medicine storage case, wireless sensing devices, and a database server. The medicine case recognized the presence of medicine in its storage compartments by image processing and estimated the recipients' present living state (i.e., current activity) on the basis of a fuzzy inference algorithm. The sensing devices were distributed throughout the houses to collect big data for living state estimation. The server accumulated results of calculation for remote monitoring of medication compliance and collection of historical data. From the experiments, the authors concluded that the proposed system was more effective in inappropriate medication intake time prevention than conventional systems based on warning accuracy. The rate of medicine recognition was 100%, and the overall F-measure of living state estimation was approximately 70%. As a result, the proposed system could detect a mistake when an elderly recipient forgot to take medicine at the prescribed time.
This paper addresses switching controller design for hybrid electric vehicle systems. The merit of using switching control scheme is that sub-controllers specialized for various driving conditions improve the fuel efficiency. A simultaneous perturbation stochastic approximation (SPSA) based method is used to optimize the design parameters of the switching controller. The design method is applied to the JSAE-SICE benchmark problem which are developed using GT-SUITE of Gamma Technologies, Inc. and integrated with Simulink / MATLAB. Experimental results illustrate that the proposed controller can achieve almost 47% improvement in fuel efficiency, compared with the sample controller of the benchmark problem.
The paper introduces concepts called algebraic controllability and algebraic observability for nonlinear differential algebraic systems with geometric index one. To characterize them, controllable trajectory and observable trajectory are also introduced. It is shown that every linearized algebraically controllable system along any (periodic) controllable trajectory is (uniformly) completely controllable. As a dual result, it is shown that every linearized algebraically observable system along any (periodic) observable trajectory is (uniformly) completely observable.
In this paper, the authors propose a markerless visual servo controller for a micro helicopter with an onboard wireless camera. The state of the helicopter is derived by a robust visual tracking algorithm based on phase correlation. The tracking algorithm is useful over a tiled carpet and a uniform carpet where other visual tracking algorithms do not work. The proposed controller achieves long-time hovering flights over both of the tiled carpet and the uniform carpet. The controller does not require any specified markers, prior texture information, or any settings other than flat floors.
This paper considers event-triggered and self-triggered control for discrete-time consensus problems. The event-triggered approach for multi-agent systems has attracted great attention in terms of reducing computation resources. In this paper, we show sufficient conditions to achieve an average consensus for centralized and distributed discrete-time event-triggered protocols. The results are then extended to self-triggered control where each agent computes its next update time based on the measurement errors of its neighbor agents.
This paper introduces a novel model predictive control of battery management for a plug-in hybrid electric vehicle. The new features of this study are as follows: (1) the apparent relationship between the battery power and the future road load is addressed in the cost function of the fuel economy optimal control problem with a simplified plug-in hybrid electric vehicle energy management system model, (2) the fuel economy improvements using the proposed approach were confirmed quantitatively compared with those using the stochastic dynamic programming approach, (3) the proposed controller can be constructed without the trip distance information which is required in the conventional dynamic control method, (4) all of the plug-in hybrid electric vehicle operating modes: idle stop, engine charge, engine start, electric vehicle, motor assist and electric continuously variable transmission, and regenerative braking, were realized using the proposed model predictive control approach. Effectiveness of the proposed control method is validated on a vehicle simulator provided by the JSAE-SICE benchmark problem 2.