This paper propose a method to enlarge the measurement range for Brillouin optical correlation domain analysis (BOCDA) system simplified with time-division pump-probe generation scheme. In this system, the pump and the probe lightwaves, which are modulated in frequency with a sinusoidal waveform to localize the stimulated Brillouin scattering (SBS) periodically, are additionally gated temporally to select only one localized position. We successfully measured distribution of fiber Brillouin gain spectrum over 250 m measurement range with 8 cm spatial resolution. We also propose an optimization of the time-gating scheme by applying separate time gates for the pump and the probe waves and introducing unbalanced Mach-Zehnder delay line, by which the performance was enhanced to 7 cm spatial resolution and 1 km measurement range.
The growing need for controlling complex behaviors of versatile robots working in unpredictable environment has revealed the fundamental limitation of model-based control strategy that requires precise models of robots and environments before their operations. This difficulty is fundamental and has the same root with the well-known frame problem in artificial intelligence. It has been a central long standing issue in advanced robotics, as well as machine intelligence, to find a prospective clue to attack this fundamental difficulty. The general consensus shared by many leading researchers in the related field is that the body plays an important role in acquiring intelligence that can conquer unknowns. In particular, purposeful behaviors emerge during body-environment interactions with the help of an appropriately organized neural computational scheme that can exploit what the environment can afford. Along this line, we propose a new scheme of neural computation based on compound control which represents a typical feature of biological controls. This scheme is based on classical neuron models with local rules that can create macroscopic purposeful behaviors. This scheme is applied to a bipedal robot and generates the rhythm of walking without any model of robot dynamics and environments.
Transfer functions of linear, time-invariant finite-dimensional systems with more outputs than inputs, as arise in factor analysis (for example in econometrics), have, for state-variable descriptions with generic entries in the relevant matrices, no finite zeros. This paper gives a number of characterizations of such systems (and indeed square discrete-time systems with no zeros), using state-variable, impulse response, and matrix-fraction descriptions. Key properties include the ability to recover the input values at any time from a bounded interval of output values, without any knowledge of an initial state, and an ability to verify the no-zero property in terms of a property of the impulse response coefficient matrices. Results are particularized to cases where the transfer function matrix in question may or may not have a zero at infinity or a zero at zero.
An impedance controller using fuzzy instruction to support human's operation in human-vehicle interaction is studied in this paper. Fuzzy instruction is a fuzzy set of control instruction candidates and is composed of satisfaction rating as membership value with the candidates. In order to examine the support performance of impedance controller, an effective car-like driving training system is established, and an intelligent cooperative system using fuzzy instruction is constructed. The intelligent cooperative system is applied to the training system to help trainee learn driving a vehicle safely, availably and quickly. The experimental results demonstrate that the intelligent cooperative system cooperates with trainee's operation according to the surrounding variation situation, and does adaptive support flexibly on a wide/narrow road through the car-like driving training system.
In the present paper, a digital redesign problem that converts continuous-time controllers into digital controllers is considered. A new discretization method based on matching of the frequency response of a continuous-time control system is proposed. The frequency response is matched using the least squares method based on data of discrete frequency points. The matching problem is described using an LMI, and the stability condition of the closed-loop system and the redesigned controller are described as additional conditions of LMIs. An optimizing problem described by some LMI conditions is solved by an iterative procedure. Examples are presented that prove the superior performance of the proposed method.
In this paper, we study a property of opacity in the language-based framework of discrete event systems. The problem of synthesizing a supervisor that enforces opacity in a maximally permissive way is addressed. A maximally permissive opacity-enforcing supervisor is realized by an automaton that generates the supremal closed controllable and opaque sublanguage of the generated language of the system. This motivates the study on computability of the supremal sublanguage. We present a formula for computing the supremal sublanguage under some assumption on uncontrollable events. Whenever the languages under consideration are regular, the supremal sublanguage is effectively computed using the presented formula.
This paper presents a feasibility study of iterative learning control for a class of redundant multi-joint robotic systems when a desired motion trajectory is specified in task-space with less dimension than that of joint space. First, it is shown that if the desired trajectory described in task-space for a time interval t ∈ [0,T] is twice continuously differentiable then a unique control signal describable in task-space exists despite of the system joint-redundancy. Second, a learning control update law is constructed through transpose of the Jacobian matrix of task-space coordinates with respect to joint coordinates by using measured data of motion trajectories in task-space. Third, the convergence of trajectory trackings through iterative learning is proved theoretically on the basis of original nonlinear robot dynamics in joint space.
The present paper describes a model representation of multi-cyclic phenomena for a multi-cylinder engine system. The model is simplified for implementation as a practical engine controller. The simplified model with physically meaningful variables can be used in design considering practical objectives and constraints more effectively. The proposed approach consists of two steps. First, an approximate analytical discrete crank angle model (i.e., a periodically time-varying state space model) is derived from the conservation laws. Second, the concept of role state variables is proposed to transform the periodically time-varying state space model into a time-invariant state space model. The stabilizability and optimality of the time-invariant state space model imply those of the periodically time-varying state space model. The time-invariant state space model is used to design cold start feedforward and feedback controllers.
The present paper proposes a new design method for a dual-rate system, in which a plant in continuous time is controlled by updating a control input in discrete time, in which the sampling interval of the plant output differs from the update interval of the control input. A dual-rate control law that stabilizes a discrete-time closed-loop system is extended. One of the advantages of such an extension is that the dual-rate control law can be redesigned to be independent of the discrete-time closed-loop system. Consequently, the intersample response can be improved by making it independent of the sampled response using the newly introduced design parameters. The present paper proposes a design method for design parameters to improve the intersample response in the steady state. Numerical examples are presented to confirm the effectiveness of the proposed method.