Swing-Up and Stabilization Control Design for an Underactuated Rotary Inverted Pendulum System: Theory and Experiments

Yang, Xuebo; Zheng, Xiaolong

doi:10.1109/tie.2018.2793214

Cited by 79 publications

(40 citation statements)

References 22 publications

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“…We have the following result: Theorem 1. The sliding variable s described in (3) converges to zero in finite time if the controller gains k 1 and k 2 are defined as in (5). The finite convergence time to the sliding surface is approximated as…”

Section: ) Reaching Phase Stabilitymentioning

confidence: 99%

“…Various kinds of control approaches have been proposed to address these challenges, including linear control methods [3]- [5], generic nonlinear control approaches [6], [7], adaptive control methods [8], [9], fuzzy control techniques [10], [11], and sliding mode control approaches [12]- [15]. Interested readers are referred to [1], [2] for more details about the linear and the nonlinear stabilization controllers for the RotIPS.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-Based Super-Twisting Sliding Mode Stabilization Control for Under-Actuated Rotary Inverted Pendulum Systems

Nguyen

Kim

et al. 2020

IEEE Access

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This paper considers the stabilization problem for under-actuated rotary inverted pendulum systems (RotIPS) via a fuzzy-based continuous sliding mode control approach. Various sliding mode control (SMC) methods have been proposed for stabilizing the under-actuated RotIPS. However, there are two main drawbacks of these SMC approaches. First, the existing SMCs have a discontinuous structure; therefore, their control systems suffer from the chattering problem. Second, a complete proof of closed-loop system stability has not been provided. To address these two limitations, we propose a fuzzy-based (continuous) super-twisting stabilization algorithm (FBSTSA) for the under-actuated RotIPS. We first introduce a new sliding surface, which is designed to resolve the under-actuation problem, by combining the fully-actuated (rotary arm) and the under-actuated (pendulum) variables to define one sliding surface. Then, together with the proposed sliding surface, we develop the FBSTSA, where the corresponding control gains are adjusted based on a fuzzy logic scheme. Note that the proposed FBSTSA is continuous owing to the modified supertwisting approach, which can reduce the chattering and enhance the control performance. With the proposed FBSTSA, we show that the sliding variable can reach zero in finite time and then the closed-loop system state converges to zero asymptotically. Various simulation and experimental results are provided to demonstrate the effectiveness of the proposed FBSTSA. In particular, (i) compared with the existing SMC approaches, chattering is alleviated and better stabilization is achieved; and (ii) the robustness of the closed-loop system (with the proposed FBSTSA) is guaranteed under system uncertainties and external disturbances. INDEX TERMS Asymptotic stability, finite-time stability, fuzzy-based super-twisting sliding mode control, rotary inverted pendulum system, stabilization control.

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Section: ) Reaching Phase Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Super-Twisting Sliding Mode Stabilization Control for Under-Actuated Rotary Inverted Pendulum Systems

Nguyen

Kim

et al. 2020

IEEE Access

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“…Inverted pendulum mechanism is mostly used as a benchmark system for control algorithms because of its nonlinear and under actuated nature. Wide variety of control techniques have been used to control this mechanism such as PID (Proportional, Integral and Derivative) [1]- [3], [44], [46], LQR (Linear Quadratic Regulator) [4], [5], Sliding Mode Control [6]- [11], Backstepping Control [12], [13], [45], Grey Prediction Control [14], Energy Based Control [15], [16], FLC (Fuzzy Logic Control) [17]- [20], NN (Neural Network) [21] and ANFIS (Adaptive Neuro-Fuzzy Inference System) [22], [23]. Most of these control strategies are based on complex mathematical equations and the success of them is directly proportional to the precise acquisition of the mathematical model of the system [24].…”

Section: Introductionmentioning

confidence: 99%

On the Implementation of Fuzzy VMC for an Under Actuated System

Bicakcı

2019

IEEE Access

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Most of control strategies are based on complex mathematical equations and the success of them is directly related to the precise acquisition of the mathematical model of the system. Virtual Model Control (VMC) allows the system to be mechanically controlled like a puppet with intuitive approaches instead of complex mathematical equations. In this paper, a novel implementation of fuzzy VMC on inverted pendulum, which is an under actuated mechanism, is presented. The cart of the inverted pendulum is controlled by manipulating the vertical equilibrium point of the pendulum with two cascaded VMCs. A Takagi-Sugeno fuzzy parameter tuner is designed to improve the VMC performance. An LQR controller is also designed to compare the performances of the controllers. The complete control system is implemented on FPGA based embedded platform in real time. The simulation and experimental results indicated that the proposed VMC successfully controls the inverted pendulum. In addition, the VMC with fuzzy parameter tuner has lower rise time, settling time, IAE and ITAE when compared to the LQR which is widely used controller in the literature. INDEX TERMS Fuzzy logic, inverted pendulum, parameter tuner, virtual model control.

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“…Different nonlinear control design approaches for the Furuta pendulum can be found in literature, such as in (Zehar, Benmahammed & Behih, 2018) where the design and implementation of an adaptive sliding mode controller is discussed, along with other sliding mode variations in order to compare their performance within that class of controllers. Miguel A. Solis, Manuel Olivares, Héctor Allende Fuzzy logic controllers and other intelligent control techniques such as swing-up control by using trajectory planning and stabilization using artificial neural networks when the model is described through linear matrix inequalities, are discussed in (Yang & Zheng, 2018). Work in (Hassanzadeh & Mobayen, 2011) presents the use of evolutionary algorithms for PID control parameters tuning, where genetic algorithms, particle swarm optimization and ant colony optimization are applied.…”

Section: Introductionmentioning

confidence: 99%

“…shows the pendulum position control on the physical platform once it has reached the stabilizable region between15 − and15 degrees around the upward unstable position, starting from a swing-up behavior.…”

mentioning

confidence: 99%

A Switched Control Strategy for Swing-Up and State Regulation for the Rotary Inverted Pendulum

Solís¹,

Olivares²,

Allende³

2019

STUD INFORM CONTROL

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The rotary inverted pendulum (RIP) is an underactuated mechanical system that can be balanced in the upward position, by applying the linear control theory based on a nonlinear dynamical model linearized around that position. Its structure makes it possible to implement controllers designed for energy minimization in the non-linearizable region. Due to the limited validity of the resulting linear model, a nonlinear control is needed to automatically move the pendulum close to the linearized region, starting from its stable downward position. A RIP platform has been built to experimentally test a switched control strategy design, which combines an energy-based nonlinear controller for swinging up the pendulum and a linear quadratic regulator for the balancing task. Experimental results validate the performance of this approach on the physical prototype.

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Swing-Up and Stabilization Control Design for an Underactuated Rotary Inverted Pendulum System: Theory and Experiments

Cited by 79 publications

References 22 publications

Fuzzy-Based Super-Twisting Sliding Mode Stabilization Control for Under-Actuated Rotary Inverted Pendulum Systems

Fuzzy-Based Super-Twisting Sliding Mode Stabilization Control for Under-Actuated Rotary Inverted Pendulum Systems

On the Implementation of Fuzzy VMC for an Under Actuated System

A Switched Control Strategy for Swing-Up and State Regulation for the Rotary Inverted Pendulum

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