Disturbance Observer Based Robust Sliding Mode Control of Permanent Magnet Synchronous Motor

Ali, Nihad; Rehman, Ateeq ur; Alam, Waqar; Maqsood, Hamid

doi:10.1007/s42835-019-00256-0

Cited by 27 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Realizing high-performance dynamic control of motors under multifactor constraints has been widely investigated to overcome complex factors, such as strong coupling, nonlinear, and multitime variation [8][9][10][11][12][13][14][15][16][17][18][19][20] integral-derivative (PID) controllers occupy a dominant position in the existing industrial control field because its structure is simple and easy to implement. Maslan et al [8] applied a PID controller to an LSRM by combining three different heuristic optimization techniques namely, PIDparticle swarm optimization (PSO), fuzzy PSO, and genetic algorithm (GA)-PSO.…”

Section: State Of the Artmentioning

confidence: 99%

“…The control performance is poor when the rules are excessively few, whereas the performance requires a long time period when the rules are excessive. Modern control methods, such as adaptive, sliding mode, backstepping, and passive control, have been applied to the position tracking control of motors [11][12][13][14][15][16][17][18][19]. Ammar et al [11] adopted an adaptive control strategy, namely, model reference adaptive controller (MRAC) to adjust the torque and stator flux of asynchronous motors to ensure robust control and small sensitivity of machine parameters compared with traditional PI controllers.…”

Section: State Of the Artmentioning

confidence: 99%

“…However, the dependence on stator resistance is strong. A load disturbance observer was designed by Ali et al [14] to improve the inherent "buffeting" phenomenon caused by traditional sliding mode control to motor drive systems, thereby effectively improving the performance of system control. This method has high estimation accuracy when the motor is running at high speed but cannot meet the requirements at low-speed operation.…”

Section: State Of the Artmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Linear Switched Reluctance Motor for Single Neuron Adaptive Position Tracking Control based on Fruit Fly Optimization Algorithm

Xu¹,

Tang²,

Yang³

et al. 2020

JESTR

View full text Add to dashboard Cite

A linear switched reluctance motor (LSRM) drive system is a complex and strongly coupled nonlinear system with time-varying parameters, friction and external disturbances. An intelligent position tracking control based on fruit fly optimization algorithm (FOA) was proposed in this study to effectively solve the effects of parameter uncertainty, load disturbance, thrust fluctuation, and friction on the performance of the LSRM system. A mathematical model of LSRM was constructed on the basis of its structure and characteristics, and spatial discretization was conducted. A single neuron adaptive controller was designed for the discretized LSRM, and its parameters were adjusted and optimized online using the FOA. The accuracy of the model was verified through experiments. Results show that the propose controller has high tracking (rotor position steady-state error is less than 10%) and strong anti-interference performance (restores to the desired rotor position instruction in 0.01 s). The proposed controller can better track the rotor position when the rotor position instruction changes, with smaller overshoot (less than 10%) and can control the PID up to 30% compared with proportional-integral-derivative (PID) controllers. The proposed method has high position tracking accuracy and strong robustness to external disturbances.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Linear Switched Reluctance Motor for Single Neuron Adaptive Position Tracking Control based on Fruit Fly Optimization Algorithm

Xu¹,

Tang²,

Yang³

et al. 2020

JESTR

View full text Add to dashboard Cite

show abstract

“…To circumvent this obstacle, a first-order filter combined with the backstepping control approach [ 5 ] is created by Swaroop, called dynamic surface control [ 6 ]. Despite dynamic surface control methods [ 7 , 8 , 9 ], that can reduce computing efforts, various nonlinear factors such as time delays, external disturbances, and physical constraints are ubiquitous in real industrial scenarios [ 10 , 11 ], which may diminish the controlling precision of the PMSM systems. Researchers have proposed proportional integral derivative (PID) control [ 12 ], neural network (NN) [ 5 ], time delay control [ 13 , 14 ], disturbance observer (DO) [ 15 , 16 ], and constraint control [ 17 , 18 ] methods for different nonlinearities to reach satisfying control results.…”

Section: Introductionmentioning

confidence: 99%

Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays

Zhang

et al. 2022

Entropy

View full text Add to dashboard Cite

This paper suggests an adaptive funnel dynamic surface control method with a disturbance observer for the permanent magnet synchronous motor with time delays. An improved prescribed performance function is integrated with a modified funnel variable at the beginning of the controller design to coordinate the permanent magnet synchronous motor with the output constrained into an unconstrained one, which has a faster convergence rate than ordinary barrier Lyapunov functions. Then, the specific controller is devised by the dynamic surface control technique with first-order filters to the unconstrained system. Therein, a disturbance-observer and the radial basis function neural networks are introduced to estimate unmatched disturbances and multiple unknown nonlinearities, respectively. Several Lyapunov-Krasovskii functionals are constructed to make up for time delays, enhancing control performance. The first-order filters are implemented to overcome the “complexity explosion” caused by general backstepping methods. Additionally, the boundedness and binding ranges of all the signals are ensured through the detailed stability analysis. Ultimately, simulation results and comparison experiments confirm the superiority of the controller designed in this paper.

show abstract

“…Sliding mode observers (SMOs) have been previously proposed in the literature for sensorless control of motor drives. An observer was designed in [18] to estimate system disturbances for improved control of permanent magnet synchronous motors (PMSMs). Ammar et al [19] used an SM approach to design dual SMOs to estimate load torque, speed, and flux for space-vector-modulation-based DTC of the IMs.…”

Section: Introductionmentioning

confidence: 99%

Integral Super Twisting Sliding Mode Based Sensorless Predictive Torque Control of Induction Motor

et al. 2020

View full text Add to dashboard Cite

In the direct torque control (DTC) of induction motor (IM) drive systems utilizing model predictive torque control (MPTC) in the inner loop and classical proportional integral (PI) control in the outer loop is prone to weaknesses, such as uncertainties, external disturbances, parameter variations, and nonlinear dynamics. A high-performance control system for speed and torque is required to guarantee a smooth and robust control architecture that can withstand such unpredictable effects. In this study, we propose an integral super twisting sliding mode control (ISTSMC) to address the shortcomings of the classical PI used in the outer loop of DTC drive systems. A sliding mode speed observer is also designed and utilized to overcome problems associated with mechanical sensors. The robustness of the proposed control strategy and fast dynamic speed response are compared favorably with a benchmark PI controller and conventional sliding mode control (SMC). The ability of the proposed system to regulate both speed and torque was evaluated through simulations, under various fault perturbations, parameter variations, and load disturbances, conducted in MATLAB/ Simulink. Various performance indices were used to demonstrate the superior performance of the proposed strategy compared to a benchmark PI system and conventional SMC-based MPTC approaches.

show abstract

Disturbance Observer Based Robust Sliding Mode Control of Permanent Magnet Synchronous Motor

Cited by 27 publications

References 18 publications

Optimization of Linear Switched Reluctance Motor for Single Neuron Adaptive Position Tracking Control based on Fruit Fly Optimization Algorithm

Optimization of Linear Switched Reluctance Motor for Single Neuron Adaptive Position Tracking Control based on Fruit Fly Optimization Algorithm

Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays

Integral Super Twisting Sliding Mode Based Sensorless Predictive Torque Control of Induction Motor

Contact Info

Product

Resources

About