In an attempt to robustify the celebrated recursive least-squares algorithm, a H∞-norm bounded recursive least-squares algorithm for parameter estimation of linear regression models is presented in this paper. This a...
详细信息
In an attempt to robustify the celebrated recursive least-squares algorithm, a H∞-norm bounded recursive least-squares algorithm for parameter estimation of linear regression models is presented in this paper. This algorithm is a special case of the H∞ filter algorithm developed recently, and guarantees estimates with the smallest possible estimation error energy, over all possible modelling errors of fixed energy. Governed by a robust criterion function, it is shown that the proposed recursive algorithm makes cautious information updates leading to active and accelerated estimation. Connections with the ordinary recursive least-squares algorithm are pointed out. Given prior bounds on modelling errors, formulae are derived to compute ellipsoidal parameter error bounds thus providing for deterministic robust estimation.
In this paper, a novel method for reducing a Simplified Volterra Series (SVS) model size is proposed for GaN RF Power Amplifier (PA) Digital Predistorter (DPD) design. Using the SVS-modified model, the number of coeff...
详细信息
In this paper, a novel method for reducing a Simplified Volterra Series (SVS) model size is proposed for GaN RF Power Amplifier (PA) Digital Predistorter (DPD) design. Using the SVS-modified model, the number of coefficients needed for the PA behavioral modeling and predistortion can be reduced by 60 % while maintaining acceptable performances. Simulation and implementation tests are performed for a Class AB GaN PA and Doherty GaN PA using a 20-MHz Long Term Evolution-Advanced (LTE-A) signal. The Adjacent Channel Power Ratio (ACPR) attains -40 dB and -41 dB for the Doherty and Class AB GaN PAs, respectively. The implementation complexity is also studied and the obtained results prove the capability of the proposed model to linearize PA using 3% of the Slice LUTs and 87% of the DSP48E1 available in the Xilinx Zynq-7000 FPGA.
Strong Lyapunov functions for two classical problems in adaptive control and parameter identification are presented. These Lyapunov functions incorporate in their structure the classical persistency of excitation cond...
详细信息
Strong Lyapunov functions for two classical problems in adaptive control and parameter identification are presented. These Lyapunov functions incorporate in their structure the classical persistency of excitation conditions, allowing to show global uniform asymptotic stability of the associated adaptive systems under sufficient and necessary conditions. (C) 2020 Elsevier Ltd. All rights reserved.
In order to identify one system (module) in an interconnected dynamic network, one typically has to solve a Multi-Input-Single-Output (MISO) identification problem that requires identification of all modules in the MI...
详细信息
In order to identify one system (module) in an interconnected dynamic network, one typically has to solve a Multi-Input-Single-Output (MISO) identification problem that requires identification of all modules in the MISO setup. For application of a parametric identification method this would require estimating a large number of parameters, as well as an appropriate model order selection step for a possibly large scale MISO problem, thereby increasing the computational complexity of the identification algorithm to levels that are beyond feasibility. An alternative identification approach is presented employing regularized kernel-based methods. Keeping a parametric model for the module of interest, we model the impulse response of the remaining modules in the MISO structure as zero mean Gaussian processes (GP) with a covariance matrix (kernel) given by the first-order stable spline kernel, accounting for the noise model affecting the output of the target module and also for possible instability of systems in the MISO setup. Using an Empirical Bayes (EB) approach the target module parameters are estimated through an Expectation-Maximization (EM) algorithm with a substantially reduced computational complexity, while avoiding extensive model structure selection. Numerical simulations illustrate the potentials of the introduced method in comparison with the state-of-the-art techniques for local module identification. (C) 2021 The Author(s). Published by Elsevier Ltd.
Data reconciliation is a well-known method in on-line process control engineering aimed at estimating the true values of corrupted measurements under constraints. An adaptive nonlinear dynamic data reconciliation (AND...
详细信息
Data reconciliation is a well-known method in on-line process control engineering aimed at estimating the true values of corrupted measurements under constraints. An adaptive nonlinear dynamic data reconciliation (ANDDR) method is proposed that includes the application to processes with an unknown statistical model. ANDDR enables gross error detection (GED) as well. Finally, a novel smart tracking system is combined to ameliorate the problem of delay seen in both the original and later NDDR methods. This package with its smart tracking features is suggested for use in distributed control systems (DCSs) for process control and manufacturing applications such as paper making.
Traditional control of intersections through traffic lights does not reach the possible flow of vehicles over the intersection. If one puts vehicle detectors around an intersection, the control program can adapt to th...
详细信息
Traditional control of intersections through traffic lights does not reach the possible flow of vehicles over the intersection. If one puts vehicle detectors around an intersection, the control program can adapt to the current traffic situation. For an optimal control of the intersection it must be possible to interpret the available measurements as well as possible: therefore position and speed of each vehicle must be determined. The paper presents a process for the continuous estimate of position and speed on driveways to intersections on which vehicle detectors are installed. Measurements in the city of Zurich are shown and discussed.
Wiener-Hammerstein systems consist of a linear dynamic system followed by a static nonlinearity, followed by another linear dynamic system. These models are difficult to identify due to the presence of two dynamic sys...
详细信息
Wiener-Hammerstein systems consist of a linear dynamic system followed by a static nonlinearity, followed by another linear dynamic system. These models are difficult to identify due to the presence of two dynamic systems. Usually, a nonlinear estimation procedure is used to estimate the parameters of the different parts. This nonlinear estimation procedure needs good starting values to converge quickly and/or reliably to a global minimum. This paper proposes a method to compute a first estimate based on one measurement record only.
Being widely used in industrial systems and manufacturing lines, precision position control systems need to use high feedback control gains to reject disturbances. However, phase-lag in velocity estimation resulting f...
详细信息
Being widely used in industrial systems and manufacturing lines, precision position control systems need to use high feedback control gains to reject disturbances. However, phase-lag in velocity estimation resulting from encoder measurement imposes a limitation on maximum allowable feedback gains, when system stability and control smoothness are concerned. In this paper, use of velocities derived from both acceleration and position measurements is suggested. The derived velocity possesses a much higher bandwidth without having theoretical phase-lag. Experimental results reveal that the use of velocities derived from practical accelerometers and encoders allows a typical position control system to substantially increase its feedback gains without compromising stability and control smoothness. It in turn results in much smaller tracking errors, compared to scenarios when velocities are created from position sensors only.
In this paper, an algorithm for the construction of nonlinear optimal observers is proposed. The key feature is the definition of an elementary problem, which is a scalar optimal control problem. The resolution of thi...
详细信息
In this paper, an algorithm for the construction of nonlinear optimal observers is proposed. The key feature is the definition of an elementary problem, which is a scalar optimal control problem. The resolution of this problem is done in two stages: 1) Solution of a scalar Hamilton-Jacobi equation to find an optimal cost function of the unknown initial state and 2) Choosing the initial state to minimize this function.
The point-mass method for nonlinear state estimation is re-examined. Several aspects of the method are treated and algorithms for multi-step prediction and smoothing based on the point-mass filtering algorithm are der...
详细信息
The point-mass method for nonlinear state estimation is re-examined. Several aspects of the method are treated and algorithms for multi-step prediction and smoothing based on the point-mass filtering algorithm are derived. Thus solution of all three estimation problems, i.e. filtering, prediction and smoothing, is unified within the pointmass framework. An anticipative technique for adaptation of support grid is presented. This technique automatically sets the number of grid points according to a future behaviour of the system and with respect to the user-defmed accuracy of pdf approximation.
暂无评论