作者：
Minh-Due LeGraduate School of Engineering
Hiroshima University Higashi Hiroshima-shi Kagamiyama 1-4-1 Hiroshima 739-8527 Japan Tel. (Mobile): (81) 070-5671-2114.
Recursive least Square (RLS) algorithm applied to a Multivariate Auto-Regressive (MAR) process is used to estimate ship steering dynamics online. The estimation method is then linked to the Linear Quadratic (LQ) Algor...
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Recursive least Square (RLS) algorithm applied to a Multivariate Auto-Regressive (MAR) process is used to estimate ship steering dynamics online. The estimation method is then linked to the Linear Quadratic (LQ) algorithm to design an optimal autopilot for steering ships. The estimation method was applied to several ships and model ships and in all the cases the estimated parameters converged well. The design algorithm was used to construct a tracking system for course keeping and course changing manoeuvres. Simulation results for the ships show the robustness of the estimation method and prove that the autopilot has very good performance.
In this paper, a simple yet robust identification method for a linear monotonic process, derived from a step test, is proposed. New linear regression equations are derived, from which the parameters of a first-order p...
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In this paper, a simple yet robust identification method for a linear monotonic process, derived from a step test, is proposed. New linear regression equations are derived, from which the parameters of a first-order plus dead-time model can be obtained directly. No iterations in calculation are needed. The proposed method outperforms the existing estimation methods that use step-test responses. The estimation error is smaller in both the time domain and the frequency domain. Furthermore, the method is robust in the presence of large amounts of measurement noise. The effectiveness of the identification method has been demonstrated through a number of simulation examples and a real-time test. (C) 1999 Elsevier Science Ltd. All sights reserved.
The identification problem for multicomponent nonstationary ozonization processes with incomplete observable states is addressed. The corresponding mathematical model containing unknown parameters is used to simplify ...
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The identification problem for multicomponent nonstationary ozonization processes with incomplete observable states is addressed. The corresponding mathematical model containing unknown parameters is used to simplify the initial nonlinear model and to derive its observability conditions. To estimate the current concentration of each component, a dynamic neuro observer is suggested. Theorems concerning the observation error bound are presented. Based on the obtained neuro observer outputs, the continuous time version of LS -algorithm, supplied by special projection procedure, is applied to construct the estimates of unknown chemical reaction constants. Simulation results related to the identification of ozonization process illustrate the effectiveness of the suggested approach.
Stochastic adaptive d -step-ahead optimal control is analyzed in this paper. An adaptive controller using the leastsquaresalgorithm and an input matching technique is proposed to combine the globally convergent esti...
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Stochastic adaptive d -step-ahead optimal control is analyzed in this paper. An adaptive controller using the leastsquaresalgorithm and an input matching technique is proposed to combine the globally convergent estimation character of the adaptive tracking and optimally-based input specification behavior by using a d -step-ahead quadratic cost function. The identification convergence rate of the parameter estimation is based on the LS algorithm. With adaptive control, the d -step-ahead closed-loop stochastic system is globally stable and the adaptive controller converges to the d -step-ahead optimal controller.
For a class of nonlinear stochastic systems with parametric-strict-feedback form, global stabilization of closed-loop system is established by designing an adaptive controller based on the weighted least-squares algor...
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For a class of nonlinear stochastic systems with parametric-strict-feedback form, global stabilization of closed-loop system is established by designing an adaptive controller based on the weighted least-squares algorithm and by using a backstepping method where nonlineacity vectors appearing in the last state equation are permitted to have the growth rate faster than linear.
In order to open the field of autonomous mobile robotics to new applications such as the provision of assistance to disabled people, the research is being focused upon low-cost solutions. That implies the use of poor ...
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In order to open the field of autonomous mobile robotics to new applications such as the provision of assistance to disabled people, the research is being focused upon low-cost solutions. That implies the use of poor perception systems and low computing power. In such a context, the algorithms used have to be simple, if they are to be executed in real time, and proof against the weaknesses of the sensing systems. The localisation approach presented here is based on the fact that the higher the localisation algorithm speed is, the lower the error in the position and the orientation, due to the odometry. Any systematic errors in the relative localisation using odometry are corrected on-line by using a limited set of ultrasonic data. If a non-systematic error occurs, a more complex procedure is necessary. Both simulation and experimentation show that the systematic odometric errors become bounded, thanks to those algorithms. Moreover, they are robust to a high rate of false ultrasonic measures. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.
This paper provides a general overview of a real-time practical development of the Spanish industry, applicable to current and future platforms, for the optimisation and automation of the real time accurate position f...
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This paper provides a general overview of a real-time practical development of the Spanish industry, applicable to current and future platforms, for the optimisation and automation of the real time accurate position fixing of mobile platforms, and for the distribution of navigational data to remote end-users on board marine vehicles. The paper describes the major functional capabilities, configuration elements and interfaces of a Spanish Navigation System ( SP-NAVSYS ), as an integral part of the navigation system of a generic mobile marine vehicle ( mmv )
The plane leastsquaresalgorithm has a relative good convergence to the stationary value of a process. Not the same about those which detains random flow mode changes or parameter slithering with comparable orders wi...
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The plane leastsquaresalgorithm has a relative good convergence to the stationary value of a process. Not the same about those which detains random flow mode changes or parameter slithering with comparable orders with the process noises. The method has appeared as solution of a practical on-line identification problem of a technological flow for an accurate row materials dosage. The usage of the low performances existing computing resources is a sine-qua-non condition.
The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error ...
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The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo random binary sequence. A lower bound on the worst-case transfer function error ...
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The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo random binary sequence. A lower bound on the worst-case transfer function error shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
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