Fuzzy Swing Up Control and Optimal State Feedback Stabilization for Self-Erecting Inverted Pendulum

Susanto, Erwin; Wibowo, Agung Surya; Rachman, Elvandry Ghiffary

doi:10.1109/access.2019.2963399

Cited by 41 publications

(23 citation statements)

References 27 publications

(46 reference statements)

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“…The authors reported a settling time of 5 seconds. In [16], the authors utilized a hybrid controller approach using fuzzy logic control for swing up controller, switching to state feedback control for stabilization, and using LQR (guaranteed cost control) for uncertainty handling. The authors reported a settling time of 7.7 seconds.…”

Section: Comparison Of Proposed Controller With State-of-the-artmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Swing-Up Control of Non-Linear Inverted Pendulum Using Lyapunov Based Optimized Fuzzy Logic Control

et al. 2021

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This paper investigates the efficacy of an optimized fuzzy logic controller for real-time swing-up control and stabilization to a rigidly coupled twin-arm inverted pendulum system. The proposed fuzzy controller utilizes Lyapunov criteria for controller design to ensure system stability. The membership functions are further optimized based on the entropy function. The controller design is based on the black-box approach, eliminating the need for an accurate mathematical model of the system. The experimental results shows an improvement in the transient and steady-state response of the controlled system as compared to other state-of-the-art controllers. The proposed controller exhibits a small settling time of 4.0 s and reaches the stable swing-up position within 5 oscillations. Various error indices are evaluated that validates an overall improvement in the performance of the system.

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Section: Comparison Of Proposed Controller With State-of-the-artmentioning

confidence: 99%

“…The controller gain matrix was further optimized by adding Kalman filter. In [16], authors developed a fuzzy controller based on a guaranteed cost control objective function for swing up control of the inverted pendulum system. The controller is built around the linearized model of the inverted pendulum system.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Swing-Up Control of Non-Linear Inverted Pendulum Using Lyapunov Based Optimized Fuzzy Logic Control

et al. 2021

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“…Hence, in this research, the value of m is fixed at unity. The selected coefficients of Q and R matrices are shown in (9).…”

Section: Primary State-feedback Controllermentioning

confidence: 99%

“…However, the offline selection of fractional orders is an ill-posed problem. The fuzzy controllers, despite their flexibility, require elaborate qualitative logical rules and offline tuning of a multitude of parameters to deliver robust control effort [9]. The sliding mode controllers, despite their robustness, unavoidably inject chattering in the system's response [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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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“…A robustness test has also been conducted to ensure that the determination of controller type is proper. An impulse disturbance can be applied to test the robustness of a controller [19]. The test considers that balancing bicopter is initially in a hovering position.…”

Section: B Ziegler-nichols Based Pid Controllermentioning

confidence: 99%

Performance Evaluation of Balancing Bicopter using P, PI, and PID Controller

Apriaskar

Fahmizal

Salim

et al. 2019

Jurnal Teknik Elektro

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Due to potential features of unmanned aerial vehicles for society, the development of bicopter has started to increase. This paper contributes to the development by presenting a performance evaluation of balancing bicopter control in roll attitude. It aims to determine the best controller structure for the balancing bicopter. The controller types evaluated are based on Ziegler-Nichols tuning method; they are proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers. Root locus plot of the closed-loop balancing bicopter system is used to decide the tuning approach. This work considers a difference in pulse-width-modulation (PWM) signal between the left and right rotors as the signal control and bicopter angle in roll movement as the output. Parameters tuned by the method are Kp, Ti, and Td which is based on the ideal PID structure. The performance test utilizes rising time, settling time, maximum overshoot, and steady-state error to determine the most preferred controller. The result shows that PI-controller has the best performance among the other candidates, especially in maximum overshoot and settling time. It reaches 8.34 seconds in settling time and 3.71% in maximum overshoot. Despite not being the best in rising time and resembling PID-controller performances in steady-state error criteria, PI-controller remains the most preferred structure considering the closeness of the response to the desired value.

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Fuzzy Swing Up Control and Optimal State Feedback Stabilization for Self-Erecting Inverted Pendulum

Cited by 41 publications

References 27 publications

Real-Time Swing-Up Control of Non-Linear Inverted Pendulum Using Lyapunov Based Optimized Fuzzy Logic Control

Real-Time Swing-Up Control of Non-Linear Inverted Pendulum Using Lyapunov Based Optimized Fuzzy Logic Control

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

Performance Evaluation of Balancing Bicopter using P, PI, and PID Controller

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