1,721,773 research outputs found
PID control design with exhaustive dynamic encoding algorithm for searches (eDEAS)
No full textThis paper proposes a simple but effective design method of PID control using a numerical optimization method. In order to achieve both stability and performance, gain and phase margins and performance indices of step response directly compose of the cost function. Hence, the proposed approach is a multiobjective optimization problem. The main effectiveness of this approach results from the strong capability of the used optimization method. A one-dimensional example concerning gain margin illustrates the practical applicability of the optimization method. The present approach has many degrees of freedom in controller design by only adjusting related weight constants. The attained PID controller is compared with Wang's and Ho's methods, IAE, and ISE for a high-order process, and the simulation result for various design targets shows that the proposed approach achieves desired time-domain performance with a guarantee of frequency-domain stability.X113sciescopuskc
Consistent normalized least mean square filtering with noisy data matrix
No full textWhen the ordinary least squares method is applied to the parameter estimation problem with noisy data matrix, it is well-known that the estimates turn out to be biased. While this bias term can be somewhat reduced by the use of models of higher order, or by requiring a high signal-to-noise ratio (SNR), it can never be completely removed. Consistent estimates can be obtained by means of the instrumental variable method (IVM),or the total/data least squares method (TLS/DLS). In the adaptive setting for the such problem, a variety of least-mean-squares (LMS)-type algorithms have been researched rather than their recursive versions of IVM or TLS/DLS that cost considerable computations. Motivated by these observations, we propose a consistent LMS-type algorithm for the data least square estimation problem. This novel approach is based on the geometry of the mean squared error (MSE) function, rendering the step-size normalization and the heuristic filtered estimation of the noise variance, respectively, for fast convergence and robustness to stochastic noise. Monte Carlo simulations of a zero-forcing adaptive finite-impulse-response (FIR) channel equalizer demonstrate the efficacy of our algorithm.X1127sciescopu
Parameter identification of induction motors using dynamic encoding algorithm for searches (DEAS)
No full textIn this paper, a newly developed optimization algorithm, called the dynamic encoding algorithm for searches (DEAS), is introduced and applied to the parameter identification of an induction motor for vector control and fault detection. Digital simulations are conducted on startup with no load and normal operation with load perturbations. DEAS is compared with the continuous-time prediction error method and the genetic algorithm via identification performance using the startup signals. The capability of onload identification using the proposed technique is also verified with transient signals. Consequently, DEAS is shown to locate more precise parameter values than both the compared methods especially with much faster execution time than the genetical algorithm.X1132Nsciescopu
Simple extension of a numerical algorithm for feedback linearization to multi-input nonlinear systems
No full textObtaining a linearizing feedback and a coordinate transformation map is very difficult, even though the system is feedback linearizable. It is known that finding a desired transformation map and feedback is equivalent to finding an integrating factor for an annihilating one-form for single input nonlinear systems. It is also known that such an integrating factor can be approximated using the simple C.I.R method and tensor product splines. In this paper, it is shown that m integrating factors can always be approximated whenever a nonlinear system with m inputs is feedback linearizable. Next, in zero-forms can be constructed by utilizing these m integrating factors and the same methodology in the single input case. Hence, the coordinate transformation map is obtained.open11sciescopu
Mobile Robot Localization Using Biased Chirp Spread Spectrum Ranging
No full textIn this paper, we propose a method of mobile robot localization based on chirp-spread-spectrum (CSS) ranging. By using the CSS system, the distances between a mobile robot and CSS nodes fixed at known coordinates can be measured according to the time of flight of radio frequency signals. Based on the measured distances, the coordinates of a mobile robot can be calculated by the method of trilateration. To deal with measurement noise, an extended Kalman filter (EKF) can be applied to estimate the coordinate of the mobile robot. These measured distances, however, are not only noisy but also biased. Therefore, the estimated coordinates of the mobile robot represent inconsistent values. To solve the problem of bias, we define a scaling factor, which corresponds to the change of the magnitude of a measured distance vector that is due to biases. Based on the scaling factor, we develop a new biased measurement model and apply the EKF to our model for estimating the coordinates of a mobile robot. Through localization experiments, we evaluate the performance of the proposed algorithm.X115059sciescopu
New encoding/converting methods of binary GA/real-coded GA
No full textThis paper presents new encoding methods for the binary genetic algorithm (BGA) and new converting methods for the real-coded genetic algorithm (RCGA). These methods are developed for the specific case in which some parameters have to be searched in wide ranges since their actual values are not known. The oversampling effect which occurs at large values in the wide range search are reduced by adjustment of resolutions in mantissa and exponent of real numbers mapped by BGA. Owing to an intrinsic similarity in chromosomal operations, the proposed encoding methods are also applied to RCGA with remapping (converting as named above) from real numbers generated in RCGA. A simple probabilistic analysis and benchmark with two ill-scaled test functions are carried out. System identification of a simple electrical circuit is also undertaken to testify effectiveness of the proposed methods to real world problems. All the optimization results show that the proposed encoding/converting methods are more suitable for problems with ill-scaled parameters or wide parameter ranges for searching.open112sciescopu
A longitudinal control system for a platoon of vehicles using a fuzzy-sliding mode algorithm
No full textRecently much interest has been concentrated on the development of intelligent vehicle highway systems (IVHS) since they are considered to have the ability to effectively handle the traffic problems of the current industrialized society. In this context, this paper presents a control algorithm for a platoon of vehicles, which is one of the most important research areas of IVHS. The suggested control algorithm consists of a headway controller and a velocity/ acceleration controller. The headway distance to the preceding vehicle and its changing rate along with the velocity of the leading vehicle are used to derive the headway control laws without using headway information from other vehicles. The velocity/acceleration controller, which controls the throttle and the brake of the controlled vehicle according to commands from the headway controller, is designed by using a fuzzy-sliding mode control (FSMC) algorithm, which does not require exact models of vehicles. It is shown that the proposed control algorithm guarantees string stability under several conditions even when each vehicle has different performance, The good performance of the suggested control algorithm is illustrated by simulations and road tests in which vehicles follow one another at 10-m spacings at a peak velocity of 80 km/h. (C) 2001 Elsevier Science Ltd. All rights reserved.X1129sciescopu
Variable step-size normalized LMS algorithm by approximating correlation matrix of estimation error
No full textIn this letter, we propose a variable step-size normalized least mean square (NLMS) algorithm. We study the relationship among the NLMS, recursive least square and Kalman filter algorithms. Based on the relationship, we derive an equation to determine the step-size of NLMS algorithm at each time instant. In steady state, the convergence of the proposed algorithm is verified by using the equation, which describes the relationship among the mean-square error, excess mean-square error, and measurement noise variance. Through computer simulation results, we verify the performance of the proposed algorithm and the change in the variable step-size over iterations. (C) 2010 Elsevier B.V. All rights reserved.X111012sciescopu
A new sliding surface design method of linear systems with mismatched uncertainties
No full textSliding mode control (SMC) is known to be robust with respect to matched uncertainties. However, it does not guarantee stability of systems with mismatched uncertainties. In this paper, we propose a new method to design a sliding surface for linear systems with mismatched uncertainties. The proposed sliding surface provides a new stability criterion of the reduced-order system origin with respect to mismatched uncertainties. A numerical example is given to illustrate the effectiveness of the proposed method.open119sciescopu
Analysis of phase models for two coupled Hodgkin-Huxley neurons
No full textA small coupled network of physiology-based neurons provides a nice paradigm to study collective synchronous dynamics arising from mutual cooperation between nonlinear oscillators. We study a system of two identical Hodgkin-Huxley neurons with symmetric synaptic coupling and symmetric external de current input using the phase reduction method to compute the effective interaction between two phases of neurons from synaptic coupling. With the help of the phase model, we analyze phase-locking dynamics of the synchrony, the out-of-phase state, and the anti-phase state of the system, and study the transitions between them in the parameter space of the synaptic reversal potential and the time delay. The phase diagrams for phase shifts and combined firing rates computed from the phase model are found to be consistent with ones from direct numerical simulations of the full system of two weakly coupled neurons. Finally, implications of our results on cortical dynamics modeling and the validity of the phase model are discussed.X1124sciescopu
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