In preview control problems, efficient use of future reference or disturbance signals is investigated to improve control performances. This paper addresses a class of H∞ preview control problems where information on the state variables and exogenous disturbance is only partially available, and reveals the clear preview-feedforward structure of the controller. To that end, the J-spectral factorization technique in the literatures is reviewed with explicit consideration of the PDE realization of the time-delay element, and a state-space interpretation is given by proposing new state-variable transformations. The explicit solution of the control-type operator Riccati equation is also constructed by the state-variable transformations.
The aim of this paper is to present a stochastic extremum-seeking algorithm for one-dimensional and multivariate optimization of static systems. Extremum-seeking algorithms estimate the optimum value of a function using perturbation signals. The authors propose three schemes (a basic scheme, an annealing parameter scheme, and a high-pass filter scheme) for the one-parameter problem and one scheme (a high-pass filter scheme) for the multivariate problem. These methods employ Wiener processes for the perturbation signals. In this paper, the proposed methods are shown to converge by means of a stability analysis of stochastic systems. The paper presents some numerical examples to demonstrate the effectiveness of the methods.
Ignoring input limitation in control systems often causes closed-loop instability or performance degradation. This paper proposes a sum of squares approach to a problem of state feedback controller synthesis for polynomial nonlinear systems with input saturation. A polytope representation of the saturation function gives tractable sufficient stability conditions for the problem. The design procedure follows two steps which are not iterative. In the first step, the matrix sum of squares relaxation is directory applied to the problem. In the second step, the annihilator of polynomials decreases the conservativeness of the first design to expand an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The result is an extension of a linear matrix inequality approach, which has been developed for linear systems with input saturation, to polynomial nonlinear systems. Numerical examples illustrate the proposed design procedure.
Recently, the generating function method for the optimal control problems was proposed. It provides a family of optimal trajectories for different boundary conditions. This paper proposes a method to compute a family of optimal trajectories by using a pair of generating functions. The proposed method reduces the on-line computational effort in calculating the numerical integration required in the conventional generating function method. It is useful for on-line trajectory generation problems.
In this paper, a numerical optimization problem for µ-synthesis with a reduced order controller is studied. A local solution is searched for starting from a reduced order controller, where µ-analysis and a local search method for a reduced order H∞ controller are applied, alternately. A main different point from the standard DK-iteration is that the procedure is all conducted for frequency response data and no representation by state equations is required. Therefore, not the plant order but the number of frequency gridding mainly affects computation time. In order to develop a more efficient algorithm, a method of reducing the number of LMIs in the calculation of a descent direction is given. Numerical examples show that computation time can be reduced drastically compared with the author's previous algorithm.
This paper describes a concept and a method of effectively applying information-theoretic cryptography to real-time system communications. Information-theoretic security, also known as unconditional security, is independent of the computing power or time an opponent can bring to bear. These properties are suitable for power systems which require both few computational resources and long life time. This paper shows some case studies to apply this scheme into several electrical power system applications, the experimental results and the examinations of key management also.
This research builds an artificial market, which was composed of stylized traders and of market makers, to study the market impact under different trading frequencies of market makers. In addition, the authors implemented a learning algorithm for market makers to search the optimal trading frequency. The results indicate that the market makers have a positive effect on reducing the price volatility, but that, when the market is highly volatile, the market making strategy can cause a crash of the market. The learning algorithm may intensify the crash/spike.
This paper proposes a new automatic calibration method for the sensor networks which measure the distribution of physical fields, such as in-room thermal temperature fields. In case of measuring the distribution of room's temperature, the regular calibration of the sensors is necessary for obtaining reliable information. However, it is not an easy task in the case of a large scale sensor network, because the manual calibration in such a system is time consuming and costly. To solve the problem, this present study proposes the new method which is based on the two concepts of regression analysis and cross validation. The new method is explained and the efficient extensions are also proposed, and the performance of the proposed methods is verified by experiments and simulations.