Bias compensation based partially coupled recursive least squares identification algorithm with forgetting factors for MIMO systems: Application to PMSMs

Shi, Zhenwei; Wang, Ziyun; Ji, Zhicheng

doi:10.1016/j.jfranklin.2016.05.021

Cited by 17 publications

(19 citation statements)

References 44 publications

(53 reference statements)

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“…So et al [5] proposed the gradient variable forgetting factor RLS (GVFF-RLS) to improve MSE analysis for dynamic equations. Shi et al [6] proposed a novel algorithm that can realize unbiased estimates under color noise and improve the tracking effect of time-varying parameters. Zhang and Yan [7] designed a method by training the weight coefficients of the multidimensional Taloy network (MTN) online with VFFRLS, and they provided an identification method for nonlinear timevarying systems.…”

Section: Introductionmentioning

confidence: 99%

A Method for Parameter Identification of Combined Integrating Systems

Fan

Ren

Chen

et al. 2020

Mathematical Problems in Engineering

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This manuscript presents a novel structure of combined integration in the process industry and proposes an efficient method for identifying its parameters. Combined integrating processes are delay-time processes that widely exist in the industry. Conventional identification methods have a low-identification accuracy, a large vibration amplitude of the identification curve, and a poor effect for this kind of process. In this paper, a new variable forgetting factor recursive least squares method was adopted to ameliorate this problem. The method could quickly track the mutation of the ideal parameters of the process and accurately identify which of the parameters has high precision, small oscillation, and a smooth curve. The simulation results indicate that the proposed method is a significant improvement compared to the ordinary recursive least squares method and the recursive least squares with a fixed forgetting factor method, and a concise program can be verified. The experimental simulation based on the actual cut tobacco rebaking industrial process shows that the proposed method has improved identification precision and the best following effect.

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Section: Introductionmentioning

confidence: 99%

A Method for Parameter Identification of Combined Integrating Systems

Fan

Ren

Chen

et al. 2020

Mathematical Problems in Engineering

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“…Due to the linearly weighted combiner, the weights can be adjusted using least mean square (LMS) and recursive least square (RLS) algorithms. However, there are several limitations and drawbacks in these algorithms . The purpose of avoiding instability has obliged designers to seek other solutions.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are several limitations and drawbacks in these algorithms. 36,37 The purpose of avoiding instability has obliged designers to seek other solutions. Furthermore, if the initial values are arbitrarily selected, the optimization search may stop at a local minimum of the cost function in the parameter space.…”

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confidence: 99%

Multidimensional Taylor network adaptive control for MIMO time‐varying uncertain nonlinear systems with noises

Zhang

Yan

2019

Intl J Robust & Nonlinear

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Summary In this paper, an adaptive control approach based on the multidimensional Taylor network (MTN) is proposed here for the real‐time tracking control of multiple‐input–multiple‐output (MIMO) time‐varying uncertain nonlinear systems with noises. Two MTNs are used to formulate the optimum control and adaptive filtering approaches. The feed‐forward MTN controller (MTNC) is developed to realize the precise tracking control. The closed‐loop errors between the filtered outputs and expected values are directly chosen as the MTNC's inputs. A valid initial value selection scheme for the weights of the MTNC, which can ensure the initial stability of adaptive process, is introduced. The proposed MTNC can update its weights online according to errors caused by system's uncertain factors, based on stable learning rate. The resilient backpropagation algorithm and the adaptive variable step size algorithm via linear reinforcement are utilized to update the MTNC's weights. The MTN filter (MTNF) is developed to eliminate measurement noises and other stochastic factors. The proposed adaptive MTN filtering system possesses the distinctive properties of the Lyapunov theory–based adaptive filtering system and MTN. Lyapunov function of the filtering errors between the measured values and MTNF's outputs is defined. By properly choosing the weights update law in the Lyapunov sense, the MTNF's outputs can asymptotically converge to the desired signals. The design is independent of the stochastic properties of the input disturbances. Simulation of the MTN‐based control is conducted to test the effectiveness of the presented results.

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“…The analysis of the results is to remove noise from the measured signals and unmeasurable disturbances . Nevertheless, the convergence rate of RLS algorithms is slow due to the matrix inversion at each recursion, resulting in a high computational cost . The EKF technique works by linearization of the dynamic system using a first‐order Taylor expansion.…”

Section: Introductionmentioning

confidence: 99%

An online algebraic estimation approach of parameters and variable mechanical torque in shunt DC motors

Beltrán-Carbajal

Tapia‐Olvera²,

Aguilar-Mejía³

et al. 2017

Int Trans Electr Energ Syst

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SummaryClassical control design methods have been successfully applied to several practical engineering systems for regulation tasks around a desired equilibrium operation state. Nevertheless, real-time information of some system parameters and mechanical torque could be required to guarantee an efficient and robust control performance for variable operation conditions. In this paper, an online and algebraic scheme is proposed for simultaneous estimation of parameters and variable mechanical load torque for nonlinear shunt DC motors. An adaptive PI velocity tracking control scheme is also described to verify the effectiveness and efficiency of the parametric estimation approach. Control dynamic gains are computed online by applying a B-spline neural network algorithm. Computational simulation results confirm the fast estimation of parameters and variable mechanical torque.

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Bias compensation based partially coupled recursive least squares identification algorithm with forgetting factors for MIMO systems: Application to PMSMs

Cited by 17 publications

References 44 publications

A Method for Parameter Identification of Combined Integrating Systems

A Method for Parameter Identification of Combined Integrating Systems

Multidimensional Taylor network adaptive control for MIMO time‐varying uncertain nonlinear systems with noises

An online algebraic estimation approach of parameters and variable mechanical torque in shunt DC motors

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