Finite‐time nonsingular terminal sliding mode control of converter‐driven DC motor system subject to unmatched disturbances

Rauf, Arshad; Zafran, Muhammad; Khan, Awais Gul; Tariq, Abdul Rehman

doi:10.1002/2050-7038.13070

Cited by 18 publications

(27 citation statements)

References 25 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the meantime, papers based on the sliding mode control (SMC) were presented by Wei et al, Silva-Ortigoza et al, and Hernández-Guzmán et al in [19][20][21], respectively. More recently, other studies based on SMC were exhibited by Rauf et al in [22,23] and by Ravikumar and Srinivasan in [24]. Another solution was implemented by Khubalkar et al via fractional order PID controllers, whose tuning was executed with a dynamic particle swarm optimization (dPSO) technique [25], with an improved dPSO technique [26], and by using an ant colony optimization technique [27].…”

Section: Unidirectional "Dc/dc Buck Converter-dc Motor" Systemmentioning

confidence: 99%

“…The simulation results associated with this desired trajectory are exhibited in Figure 8. Simulation results when the second desired angular velocity profile, given by (23), is contemplated for the system in closed-loop.…”

Section: Experimental and Simulation Testmentioning

confidence: 99%

“…(b) Tracking errors of υ, for the desired trajectory ω * declared in (22). (c) Tracking errors of ω, when the desired profile ω * is given by (23). (d) Tracking errors of υ, when the desired profile ω * is given by (23).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System

et al. 2022

View full text Add to dashboard Cite

By developing a robust control strategy based on the differential flatness concept, this paper presents a solution for the bidirectional trajectory tracking task in the “full-bridge Buck inverter–DC motor” system. The robustness of the proposed control is achieved by taking advantage of the differential flatness property related to the mathematical model of the system. The performance of the control, designed via the flatness concept, is verified in two ways. The first is by implementing experimentally the flatness control and proposing different shapes for the desired angular velocity profiles. For this aim, a built prototype of the “full-bridge Buck inverter–DC motor” system, along with Matlab–Simulink and a DS1104 board from dSPACE are used. The second is via simulation results, i.e., by programming the system in closed-loop with the proposed control algorithm through Matlab–Simulink. The experimental and the simulation results are similar, thus demonstrating the effectiveness of the designed robust control even when abrupt electrical variations are considered in the system.

show abstract

Section: Unidirectional "Dc/dc Buck Converter-dc Motor" Systemmentioning

confidence: 99%

Section: Experimental and Simulation Testmentioning

confidence: 99%

See 1 more Smart Citation

Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Load torque uncertainties in PMDC motor system are inherently mismatched and hence SMC techniques have to be accordingly tailored to handle these classes of uncertainties. In reference [32], a non‐singular terminal sliding mode control is proposed for the velocity tracking in converter driven motor system. Here, the sliding surface is designed taking into account the higher order dynamics and disturbance observer mechanism in compensating the uncertainties in finite time.…”

Section: Introductionmentioning

confidence: 99%

Enhanced dynamic performance in DC–DC converter‐PMDC motor combination through an intelligent non‐linear adaptive control scheme

Nizami

Chakravarty

Mahanta

et al. 2022

IET Power Electronics

View full text Add to dashboard Cite

A novel neuro‐adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on‐board (adaptive) learning to counteract the unknown non‐linear time‐varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non‐parametric uncertainties. In order to verify the performance of the proposed neuro‐adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real‐time scenarios. Results are obtained for start‐up, time‐varying angular velocity tracking and under the influence of highly non‐linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

show abstract

“…In the past decade, the control strategy based on the adaptive control problem of nonlinear systems got significant attention from the control engineering community while addressing the unmanned aerial vehicles (UAVs), robotic system and industrial control system as well. It was the major concern to attain better control performance through using sliding mode control, model predictive control and state feedback control [3]- [5]. The limitations of mentioned control techniques become dominant while addressing practical systems from real life that are based on complex nonlinear characteristics such as uncertain nonlinear dynamics, nonaffine nonlinear dynamics.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of liquid level in a tank system based on fuzzy logic controller

Ilyas¹,

Shah²,

Rauf

et al. 2022

IJRA

View full text Add to dashboard Cite

<p>Process industry needed a fast executed automatic control system capable of handling uncertain, vague problems and nonlinear control variables. Liquid level control is one of the emerging control problems getting the interest of technical experts in the area of control. This paper is based on a fuzzy logic control strategy to maintain and stabilize the liquid level in a tank system that deals with pumping of liquid in tanks as well as regulating liquid level and pushing off the liquid into another tank. Fuzzy controller attains optimum performance by eliminating perturbation in steady state and vanishing the overshoot as compared to proportional, integral, and derivative (PID) controller. The proposed fuzzy logic controller shows minimal steady error as compared to PID controller. The defuzzification of the proposed scheme is based on the centroid method to obtain optimum results. The settling time is nearly 50 second while using fuzzy logic control as compared to 80 seconds in PID control strategy. The overshoot observed is minimal, nearly less than 1% using a fuzzy logic control scheme.</p>

show abstract

Finite‐time nonsingular terminal sliding mode control of converter‐driven DC motor system subject to unmatched disturbances

Cited by 18 publications

References 25 publications

Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System

Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System

Enhanced dynamic performance in DC–DC converter‐PMDC motor combination through an intelligent non‐linear adaptive control scheme

Stabilization of liquid level in a tank system based on fuzzy logic controller

Contact Info

Product

Resources

About