As a representative approach to control nonlinear system, sliding mode control (SMC) is widely used. SMC is one of the useful control methods also for the fractional order systems. However, SMC cannot handle the disturbance presence on a mismached input channel. In case mismatched disturbance exists, a control system needs to be constructed by backstepping technique. In this paper, to solve the mismatched disturbance problem of SMC for fractional order systems, we propose a novel backstepping technique using fractional order differentiation. And, we confirm the availability of our proposed backstepping technique by numerical simulation.
We examine clamp-on ultrasonic flowmeters for gas, in particular, low-pressure gases at atmospheric pressure. For clamp-on ultrasonic flowmeters, the ultrasonic wave is generated outside the plumbing, transmitted into the gas inside the plumbing, and received on the outside of the plumbing. In this situation, specific acoustic impedances differ greatly for the gas and the metallic plumbing. Therefore, it is necessary to transmit and receive ultrasonic waves efficiently. In addition, separation of two routes of ultrasonic waves is important — those transmitted to the internal gas in a straight route and those transmitted to the metallic plumbing in a circuitous route. We transmitted and received ultrasonic waves aslant to the plumbing using an ultrasonic transducer, which had a curvature corresponding to the radius of the metallic plumbing. Moreover, we were able to separate the ultrasonic wave propagating in the gas and the metallic plumbing. We verified the signal transmitted inside of the gas clearly, with approximately 4 times the signal-to-noise ratio. Therefore, we have acquired useful data for the development of clamp-on ultrasonic flowmeters for low-pressure gases.
In this paper, we study H∞ performance limitation analysis for continuous-time SISO systems using LMIs. By starting from an LMI that characterizes a necessary and sufficient condition for the existence of desired controllers achieving a prescribed H∞ performance level, we represent lower bounds of the best H∞ performance achievable by any LTI controller in terms of the unstable zeros and the unstable poles of a given plant. The transfer functions to be investigated include the sensitivity function (1+PK)-1, the complementary sensitivity function (1+PK)-1PK, and (1+PK)-1P, the first and the second of which are well investigated in the literature. As a main result, we derive lower bounds of the best achievable H∞ performance with respect to (1+PK)-1P assuming that the plant has unstable zeros. More precisely, we characterize a lower bound in closed-form by means of the first non-zero coefficient of the Taylor expansion of the plant P(s) around its unstable zero.
Glaucoma is a disease that causes optic nerve damage and loss of the field of vision. It is characterized by an inability to see part of the visual field, or blurring of the visual field, as if by a mist. We therefore took static visual field measurements in individuals with unimpaired vision and those with glaucoma, both confirmed in standard visual acuity and visual field ophthalmological examinations, and then compared gaze movements during driving simulations. In this experiment, EMR-9 and EMR-8 eye mark recorders (NAC Image Technology Inc.) were used for eye tracking. All gaze points during driving were measured, and the ellipse containing 95% of the measured points was drawn. Among participants with glaucoma, the area of the ellipse containing 95% of gaze points was relatively small, indicating that they could only see a narrow range. While this range also widened on the course with colliding vehicles for the glaucoma participants, the increase was not as large as that for unimpaired participants.
This paper proposes a distributed multi-agent optimization protocol to minimize the average of objective functions of the agents in the network with satisfying equality and inequality constraints of each agent. The exact penalty method is adopted to obtain a linear distributed optimization protocol. The proposed protocol works only with the decision variables and does not need any additional variables. The proof of the consensus and convergence of the proposed protocol is provided as well as the boundedness under mild assumptions. The protocol is also illustrated by a numerical example.