Robust adaptive H∞ position control via a wavelet-neural-network for a DSP-based permanent-magnet synchronous motor servo drive system

El-Sousy, Fayez F. M.

doi:10.1049/iet-epa.2009.0156

Cited by 22 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As indicated in (15), these weights w ik are proportional to the parameters η ik , so the large values of η ik lead to those of w ik . Meanwhile, the gain matrix K of the control term u fb is achieved by solving the LMIs (7) or (22). Finally, the design parameters K and η ik can be systematically tuned as follows:…”

Section: Stability Analysismentioning

confidence: 99%

“…This is a major constraint for industrial applications. Furthermore, the NFC algorithms of [22][23] are quite complex when the reference models of the online selftuning algorithm are utilized.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the maximum torque per ampere (MTPA) control is combined with the proposed controller to maximize the torque generation in the constant torque region. Unlike the previous online selftuning algorithms [17][18][19][20][21][22][23] in which the quadratic cost function to be minimized includes only the speed error, the proposed control technique chooses the time derivative of the quadratic Lyapunov function as the cost function to be minimized. Thus, the global stability analysis can be simple and systematic.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Dang¹,

Vu²,

Choi³

et al. 2013

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.

show abstract

Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Dang¹,

Vu²,

Choi³

et al. 2013

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…If r=¥, this is the case of a minimum error tracking control without disturbance attenuation [42][43][44][45][46][47][48][49][50]. Then, the desired robust tracking performance in (73) can be achieved for a prescribed attenuation level r.…”

Section: Rwit2fnn Estimator and Compensated Controllermentioning

confidence: 99%

Robust Recurrent Wavelet Interval Type-2 Fuzzy-Neural-Network Control for DSP-Based PMSM Servo Drive Systems

El-Sousy¹

2013

Journal of Power Electronics

View full text Add to dashboard Cite

In this paper, an intelligent robust control system (IRCS) for precision tracking control of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The IRCS comprises a recurrent wavelet-based interval type-2 fuzzy-neural-network controller (RWIT2FNNC), an RWIT2FNN estimator (RWIT2FNNE) and a compensated controller. The RWIT2FNNC combines the merits of a self-constructing interval type-2 fuzzy logic system, a recurrent neural network and a wavelet neural network. Moreover, it performs the structure and parameter-learning concurrently. The RWIT2FNNC is used as the main tracking controller to mimic the ideal control law (ICL) while the RWIT2FNNE is developed to approximate an unknown dynamic function including the lumped parameter uncertainty. Furthermore, the compensated controller is designed to achieve L 2 tracking performance with a desired attenuation level and to deal with uncertainties including approximation errors, optimal parameter vectors and higher order terms in the Taylor series. Moreover, the adaptive learning algorithms for the compensated controller and the RWIT2FNNE are derived by using the Lyapunov stability theorem to train the parameters of the RWIT2FNNE online. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed IRCS. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the IRCS grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.

show abstract

“…The WNN is appropriate for the approximation of unknown nonlinear functions and fast variations [18] [19]. As a result, WNN has been demonstrated to be better than the other neural networks in that the structure can present more potential to enhance the mapping relationship between the inputs and outputs [20]. The paper is organized as follows: section 2 is designated to the problem statement.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Wavelet Neural Network Particle Swarm Optimization Based MRPID of Interior Permanent Magnet Synchronous Motor Drives

Rajamanickam¹,

Mohideen²

2016

IJIES

View full text Add to dashboard Cite

Abstract:In this paper, a Multi wavelet neural network (MWNN)-Particle Swarm Optimization (PSO) based MRPID controller is proposed for the speed control of an interior permanent magnet synchronous motor (IPMSM) drive system. In the proposed controller, the multi wavelet transform is used to decompose the available error signal from the actual speed and command speed signal into different frequency components. The parameters of the proposed controller are optimized by PSO algorithm. Then the calculated error coefficients are multiplied with their respective gains to generate an overall control signal. The proposed model is implemented in MATLAB/SIMULINK working platform and the speed control performance of a proposed controller is evaluated. The IPMSM motor drive with MWNN-PID controller through simulation results proves a better performance and stability when compared to that of conventional PID and WNN-PID controllers.

show abstract

Robust adaptive H∞ position control via a wavelet-neural-network for a DSP-based permanent-magnet synchronous motor servo drive system

Cited by 22 publications

References 12 publications

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Robust Recurrent Wavelet Interval Type-2 Fuzzy-Neural-Network Control for DSP-Based PMSM Servo Drive Systems

A Multi-Wavelet Neural Network Particle Swarm Optimization Based MRPID of Interior Permanent Magnet Synchronous Motor Drives

Contact Info

Product

Resources

About