Robust stabilisation of rotary inverted pendulum using intelligently optimised nonlinear self-adaptive dual fractional-order PD controllers

Saleem, Omer; Mahmood-ul-Hasan, Khalid

doi:10.1080/00207721.2019.1615575

Cited by 30 publications

(27 citation statements)

References 38 publications

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“…It strengthens the controller's damping against overshoots and reduces transient-recovery time under exogenous disturbances. The benefits afforded by the proposed adaptive controller are verified via credible hardware-in-the-loop experiments conducted on the QNET 2.0 Rotary Pendulum (Rot-Pend) board [14].…”

Section: Introductionmentioning

confidence: 91%

“…where, V m is voltage signal applied to control the DC motor. The nominal state-space model of the Rot-Pend system is defined in (5), [14].…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The system's model parameters are clearly described and their values are identified in Table 1 [14].…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The performance limitations associated with the conventional controllers for rotary pendulums can be rectified by augmenting the baseline feedback controller with an online adaptation law that dynamically alters the controller's characteristics in real-time [14]. Such adaptive mechanisms enhance the system's response speed, damping strength, and resilience against exogenous disturbances [15].…”

Section: Introductionmentioning

confidence: 99%

“…There are two main categories of nonlinear-scaling functions that are widely used. The most commonly used category involves adaptive parameter adjustment using only the magnitude of the state-error variables [14]. The second category involves the online parameter adjustment based on the magnitude as well as the directionof-variation (or phase) of the state-error [31].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Indirect Adaptive State-Feedback Control of Rotary Inverted Pendulum Using Self-Mutating Hyperbolic-Functions for Online Cost Variation

Saleem

Mahmood-ul-Hasan

2020

IEEE Access

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This paper presents the development of an indirect adaptive state-feedback controller to improve the disturbance-rejection capability of under-actuated multivariable systems. The ubiquitous Linear-Quadratic-Regulator (LQR) is employed as the baseline state-feedback controller. Despite its optimality, the LQR lacks robustness against parametric uncertainties. Hence, the main contribution of this paper is to devise and retrofit the LQR with a stable online gain-adjustment mechanism that dynamically adjusts the state weighting-coefficients of LQR's quadratic cost-function via state-error dependent nonlinear-scaling functions. An original self-mutating phase-based adaptive modulation scheme is systematically formulated in this paper to self-adjust the state weighting-coefficients. The scheme employs pre-calibrated secanthyperbolic-functions whose waveforms are dynamically reconfigured online based on the variations in magnitude and polarity of state-error variables. This augmentation dynamically alters the solution of the Riccati-Equation which modifies the state-feedback gains online. The proposed adaptation flexibly manipulates the system's control effort as the response converges to or diverges from the reference. The efficacy of proposed adaptive controller is validated by conducting hardware-in-the-loop experiments to vertically stabilize the QNET 2.0 Rotary Pendulum system. As compared to the standard LQR, the proposed adaptive controller renders rapid transits in system's response with improved damping against oscillations, while maintaining its asymptotic-stability, under bounded exogenous disturbances. INDEX TERMS Hyperbolic functions, linear quadratic regulator, cost-function, rotary inverted pendulum, self-tuning control, state weighting-coefficients.

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Section: Introductionmentioning

confidence: 91%

“…where, V m is voltage signal applied to control the DC motor. The nominal state-space model of the Rot-Pend system is defined in (5), [14].…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The system's model parameters are clearly described and their values are identified in Table 1 [14].…”

Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning