Control of Rotary Inverted Pendulum Using Model-Free Backstepping Technique

Huang, Jingwen; Zhang, Tingting; Fan, You; Sun, Jian‐Qiao

doi:10.1109/access.2019.2930220

Cited by 42 publications

(25 citation statements)

References 34 publications

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“…Furthermore, a neural network reference model control is presented in Wang (2017), which consists of neural network controller and neural network identifier to meet the control objectives required for stabilizing the rotary pendulum. Similarly, a model free back-stepping controller is proposed by Huang et al (2019) for RotIP, which uses the nominal system model while estimating the unknown dynamics. In fuzzy control (see Dang et al (2014)), a Takagi–Sugeno–based fuzzy approach is presented with reduced number of rules, which leads to a simplified controller for the RotIP system with real time implementation.…”

Section: Introductionmentioning

confidence: 99%

Underactuated rotary inverted pendulum control using robust generalized dynamic inversion

Mehedi

Ansari

Bajodah

et al. 2020

Journal of Vibration and Control

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The article applies the robust generalized dynamic inversion control methodology to the problem of stabilizing upright equilibrium configuration of the under-actuated rotary inverted pendulum system while tracking rotary motion of the actuated arm. The proposed robust generalized dynamic inversion control law comprised equivalent and switching control parts. The equivalent control part works to enforce a virtual constraint dynamics of the controlled state variables by means of Moore–Penrose generalized inversion. The switching control part is of the sliding mode type, and it improves robustness against unmodeled system dynamics, parametric uncertainties, and external disturbances. The robust generalized dynamic inversion control design on the linearized model of the under-actuated rotary inverted pendulum is shown to guarantee semi-global asymptotically stable tracking performance. Numerous computer simulations and experiments are conducted on the Quanser rotary inverted pendulum system, revealing that the proposed algorithm has better convergence and tracking performance than conventional sliding mode and generalized dynamic inversion control strategies when both are applied separately.

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Section: Introductionmentioning

confidence: 99%

Underactuated rotary inverted pendulum control using robust generalized dynamic inversion

Mehedi

Ansari

Bajodah

et al. 2020

Journal of Vibration and Control

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“…The Rot-Pend is an inherently unstable system with highly nonlinear dynamics, which make it an ideal candidate to examine and validate the robustness of the proposed phase-driven adaptive control system [34]. The hardware schema of the Rot-Pend setup is shown in Figure 1.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…where, x is the state-vector, y is the output-vector, u is the control input signal, A is the state-transition matrix, B is the input matrix, C is the output matrix, and D is the feed-forward matrix. The state-vector and the control input-vector of the Rot-Pend system are identified in (3) and (4), [34].…”

Section: Mathematical Modelmentioning

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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“…The associate editor coordinating the review of this manuscript and approving it for publication was Nishant Unnikrishnan. noises and uneven surface, etc which affect the controller's performance [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Stability and Direction Control of a Two-Wheeled Robotic Wheelchair Through a Movable Mechanism

2020

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A two-wheeled robotic wheelchair (TWRW) has a better manoeuvrability than a conventional four-wheeled wheelchair. However, it is not statically stable near the upright posture or a posture desired by the rider, and an active stability controller is required. Stability control becomes more challenging when a TWRW is also required to move in a desired direction. To rely on wheels' motions to achieve both stability and direction control tend to impose a large burden on the wheels' driving motors or other types of actuators in terms of their driving torque and power consumption. Various disturbances in the system also affect the performance of the controller. To solve these problems, this paper presents a stability and direction controller based on the motion of a pendulum-like movable mechanism added to assist the wheels to produce control actions. The dynamic model of the TWRW is established through the Euler-Lagrange formulation in which the disturbances caused by model uncertainties and rider's motion are considered. A robust second-order sliding mode control is then developed for the stability and the direction control of a TWRW. Simulation results are presented to validate the effectiveness of the proposed method. INDEX TERMS Two-wheeled robotic wheelchair, stability control, direction control, added movable mechanism, second-order sliding mode control.

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Control of Rotary Inverted Pendulum Using Model-Free Backstepping Technique

Cited by 42 publications

References 34 publications

Underactuated rotary inverted pendulum control using robust generalized dynamic inversion

Underactuated rotary inverted pendulum control using robust generalized dynamic inversion

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

Stability and Direction Control of a Two-Wheeled Robotic Wheelchair Through a Movable Mechanism

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