This paper addresses both the problems of identification and state estimation of the class of nonlinear fractional systems. Using the combined state and parameter estimation approach, a new method of estimation servin...
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This paper addresses both the problems of identification and state estimation of the class of nonlinear fractional systems. Using the combined state and parameter estimation approach, a new method of estimation serving to estimate simultaneously the unknown parameters, the unknown fractional orders and the inaccessible states, is proposed for the discrete fractional-order Wiener systems. The principle is that the estimation of the states uses the estimates of the parameters and the identification of the parameters utilizes the estimated states. By minimizing the defined criterion, which is non-convex and nonlinear in the parameters, the model parameters are firstly identified using the recursiveleastsquares. Then, the fractional orders are determined with the Levenberg-Marquardt algorithm. Next, the estimates of the parameters and the orders will be used to estimate the immeasurable states based on the extended Luenberger observer. To prove the consistence of the proposed algorithm, a complete convergence analysis is developed. Finally, the effectiveness of the suggested method is illustrated in simulation examples.
This study investigates the aerodynamic effects and the tracking control problem of quadrotor-type unmanned aerial vehicles. The authors first present the on-line identification of the aerodynamic parameters by using ...
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This study investigates the aerodynamic effects and the tracking control problem of quadrotor-type unmanned aerial vehicles. The authors first present the on-line identification of the aerodynamic parameters by using the recursive least squares algorithm based on the measurement outputs of the accelerometer. Then, the non-linear discrete-time trajectory tracking controllers with aerodynamic compensation have been designed. Through identifying and compensating the external aerodynamics on line, the simulation results show that the tracking performance has been enhanced, especially when the vehicle is in some flight envelopes where the aerodynamics have significant effects on the quadrotor dynamics, such as the large-acceleration flight regime.
The performance of autonomous robots in varying environments needs to be improved. For such incremental improvement, here we propose an incremental learning framework based on Q-learning and the adaptive kernel linear...
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The performance of autonomous robots in varying environments needs to be improved. For such incremental improvement, here we propose an incremental learning framework based on Q-learning and the adaptive kernel linear (AKL) model. The AKL model is used for storing behavioral policies that are learned by Q-learning. Both the structure and parameters of the AKL model can be trained using a novel L2-norm kernel recursiveleastsquares (L2-KRLS) algorithm. The AKL model initially without nodes and gradually accumulates content. The proposed framework allows to learn new behaviors without forgetting the previous ones. A novel local epsilon-greedy policy is proposed to speed the convergence rate of Q-learning. It calculates the exploration probability of each state for generating and selecting more important training samples. The performance of our incremental learning framework was validated in two experiments. A curve-fitting example shows that the L2-KRLS-based AKL model is suitable for incremental learning. The second experiment is based on robot learning tasks. The results show that our framework can incrementally learn behaviors in varying environments. Local epsilon-greedy policy-based Q-learning is faster than the existing Q-learning algorithms.
Conventionally, least mean square rule which can be named CMAC-LMS is used to update the weights of CMAC. The convergence ability of CMAC-LMS is very sensitive to the learning rate. Applying recursiveleastsquares (R...
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Conventionally, least mean square rule which can be named CMAC-LMS is used to update the weights of CMAC. The convergence ability of CMAC-LMS is very sensitive to the learning rate. Applying recursiveleastsquares (RLS) algorithm to update the weights of CMAC, we bring forward an algorithm named CMAC-RLS. And the convergence ability of this algorithm is proved and analyzed. Finally, the application of CMAC-RLS to control nonlinear plant is investigated. The simulation results show the good convergence performance of CMAC-RLS. The results also reveal that the proposed CMAC-PID controller can reject disturbance effectively, and control nonlinear time-varying plant adaptively.
We propose a parametric estimator of the Poisson intensity, based on a set of successive interarrival times. The estimator is asymptotically unbiased and consistent. We use recursiveleastsquares to estimate the para...
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We propose a parametric estimator of the Poisson intensity, based on a set of successive interarrival times. The estimator is asymptotically unbiased and consistent. We use recursiveleastsquares to estimate the parameters. We modify the estimator using the instrumental variable method when the system residual is autocorrelated.
An important and hard problem in signal processing is the estimation of parameters in the presence of observation *** this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is cons...
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An important and hard problem in signal processing is the estimation of parameters in the presence of observation *** this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is considered and two developed bias compensation leastsquares （BCLS） methods are *** introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed *** exploiting the statistical properties of the cross-correlation function between the leastsquares （LS） error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be *** results are presented to illustrate the good performances of the proposed algorithms.
An extension to the concept of the standard recursiveleastsquares (RLS) algorithm is considered. The improved RLS algorithm is described and has been implemented using experimental data from an underwater acoustic c...
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An extension to the concept of the standard recursiveleastsquares (RLS) algorithm is considered. The improved RLS algorithm is described and has been implemented using experimental data from an underwater acoustic communication system. Results obtained show that the improved RLS algorithm has faster convergence and improved tracking performance than both the standard RLS and least mean squares (LMS) algorithms.
In this paper we propose a mobile positioning method based on a recursiveleastsquares (RLS) algorithm for suppressing the non-line of sight (NLOS) effects in cellular systems. The proposed method finds the position ...
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In this paper we propose a mobile positioning method based on a recursiveleastsquares (RLS) algorithm for suppressing the non-line of sight (NLOS) effects in cellular systems. The proposed method finds the position of a,nobile station from TOAs measured by three BSs. Simulation results show that the proposed method has a fast convergence time and greatly reduces the positioning error especially in NLOS situations. Thus it is expected that the proposed method can be effectively used in a dense urban environment.
For the fractional-order Hammerstein system with white noise, the difficulty of identification is that the parameters of the linear and the nonlinear blocks and the fractional order are unknown and the intermediate va...
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For the fractional-order Hammerstein system with white noise, the difficulty of identification is that the parameters of the linear and the nonlinear blocks and the fractional order are unknown and the intermediate variable and the states are unmeasurable. To overcome this difficulty, we transform the system from an input nonlinear pseudo-state-space system to an input-output representation and we develop an algorithm based on the recursiveleastsquares, the Levenberg-Marquardt and the Auxiliary Model Principle. The convergence of the identified parameters is studied. The performance of the proposed algorithm are tested by two numerical examples.
This paper proposes a novel maximum likelihood based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise. The unknown noises in the information vector are replaced by their estimates, a...
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This paper proposes a novel maximum likelihood based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise. The unknown noises in the information vector are replaced by their estimates, and then the parameters can be obtained by using the proposed algorithm through the noise estimates. Compared with the maximum likelihood-based recursive least squares algorithm, the proposed algorithm has less computation burden. Furthermore, the performance of the proposed algorithm is analysed and compared using a simulation example.
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