Model identification and real-time implementation of a linear parameter–varying control scheme on lab-based inverted pendulum system

Barkat, Anila; Hamayun, Mirza Tariq; Ijaz, Salman; Akhtar, Saleem; Ansari, Ejaz A.; Ghous, Imran

doi:10.1177/0959651820935692

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…Alternatively, one can use adaptive controllers. Barkat et al 10 propose a linear parameter-varying (LPV) strategy derived in real-time for controlling a lab-based inverted pendulum system. Hang and Chen 11 derive an adaptive path tracking controller for a four-wheel-steering autonomous ground vehicle for different longitudinal velocities and road friction coefficients.…”

Section: Introductionmentioning

confidence: 99%

A comparison between gain-scheduling linear quadratic regulator and model predictive control for a manipulator with flexible components

Colombo

Silva

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Dynamic performance and energy efficiency can be improved by reducing the inertia of mechanical systems. However, the components of such a system may be flexible and, inevitably, subject to vibrations. Moreover, the relative motion between flexible components leads to time-varying boundary conditions characterizing configuration-dependent dynamics. Since robust controllers may be conservative for this problem, adaptive controllers should be considered since they can cope with performance design requirements. In this work, we present a comparison between a linear quadratic regulator gain-scheduling control and an adaptive model predictive control for a flexible industrial Cartesian manipulator with configuration-dependent dynamics. This study takes the system stability and the use of bounded actuation into account. Numerical results demonstrate that the adaptive model predictive control strategy achieved better performance than the linear quadratic regulator gain scheduled. Also, in applications with tighter actuation bounds, the difference between the performance of both approaches is even higher, reinforcing the model predictive control capability of dealing with limited actuation.

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Section: Introductionmentioning

confidence: 99%

A comparison between gain-scheduling linear quadratic regulator and model predictive control for a manipulator with flexible components

Colombo

Silva

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The arm can move in the horizontal plane as well as pendulum has movement in the vertical plane [6][7][8][9][10][11]. Various types of physical systems such as human's arm motion, control of position and attitude of aircrafts, and robot system have been originated from the model of RIP system [12][13][14]. For this reason, stability and control of RIP system is still in consideration [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Super-Twisting Sliding Mode Stability Control of Underactuated Rotational Inverted Pendulum With Experimental Validation

et al. 2022

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In this study, an adaptive proportional-integral-derivative (PID) sliding mode control technique combined with the super-twisting algorithm is planned for the stabilization of rotational inverted pendulum in the appearance of exterior perturbation. The state-space model of rotational inverted pendulum in the existence of exterior disturbance is attained. Then, the super-twisting PID sliding mode controller is designed for finite time stability control of the considered underactuated control system. The upper bounds of perturbation are presumed to be unknown; consequently, the adaptive control procedure is taken into account to approximate the uncertain bounds of external disturbances. The stability control of rotational inverted pendulum system is verified by means of the Lyapunov stability theory. In order to validate the accuracy and efficiency of the recommended control technique, some simulation outcomes are prepared and compared with other existing scheme. Finally, the experimental results are implemented to show the success of the designed method.INDEX TERMS Robust control; adaptive tuning; underactuated systems; sliding mode control; inverted pendulum.

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“…Various inverted pendulum systems have correspondingly become experimental beds for testing the feasibility and robustness of control theory. The inverted pendulum is a nonlinear, strongly coupled, and unstable system [4], and its stability control is a typical problem in control theory. In recent years, reinforcement learning has developed rapidly in the field of inverted pendulum control.…”

Section: Introductionmentioning

confidence: 99%

Balance Control of an Inverted Pendulum on a Quadruped Robot by Reinforcement Learning

Lei

2022

J. Phys.: Conf. Ser.

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When a quadruped robot crosses some uneven terrain, its stability is a very important consideration. In practical applications, it is often difficult for researchers to obtain accurate models of quadruped robot systems and need quadruped robots to complete high-stability tasks. Balancing an inverted pendulum on a quadruped robot is a good way to test their balancing ability, which is of great research and practical value. According to the nonlinear, uncertain, and strong coupling characteristics of the quadruped robot inverted pendulum system, this paper proposes a quadruped robot balancing inverted pendulum algorithm based on reinforcement learning, the Q-learning algorithm. The advantage of this algorithm is that it can learn effective balancing policy directly from experience, and it does not depend on the accurate model of the quadruped robot. It has the characteristics of high efficiency and flexibility. Through a lot of training, it can break through the performance limit brought by model error to traditional methods. In this paper, a comparative experiment of a set of balanced inverted pendulums of quadruped robots with different sizes is completed in the V-REP simulation software. The experimental results show that the algorithm can effectively improve the balance ability of quadruped robots, and it also shows that the algorithm has good adaptability.

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Model identification and real-time implementation of a linear parameter–varying control scheme on lab-based inverted pendulum system

Cited by 7 publications

References 13 publications

A comparison between gain-scheduling linear quadratic regulator and model predictive control for a manipulator with flexible components

A comparison between gain-scheduling linear quadratic regulator and model predictive control for a manipulator with flexible components

Robust Adaptive Super-Twisting Sliding Mode Stability Control of Underactuated Rotational Inverted Pendulum With Experimental Validation

Balance Control of an Inverted Pendulum on a Quadruped Robot by Reinforcement Learning

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