Synthesized ADRC for One-Level Inverted Pendulum System through Combination of Separating and Assembling

Inverted pendulum system is the ideal study object of nonlinear system. The ADRC has good estimate for disturbances, strong robustness and using static decoupling instead of dynamical decoupling. The one-level inverted pendulum system can be regarded as composing of the pendulum angel system and the cart position system. The former is faster and the later is slower. The synthesized ADRC for one-level inverted pendulum system is built through combination of separating and assembling to reduce difficulty in optimizing ADRC parameters of the inverted pendulum system. The synthesized controller is simulated by Matlab under different parameters of the inverted pendulum. Simulation results show that the pendulum angle and the cart position are well controlled.

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Stabilization of a Cart-Inverted Pendulum system using a 2-periodic controller: Simulation results

2013 IEEE Conference on Systems, Process & Control (ICSPC) ◽

10.1109/spc.2013.6735126 ◽

2013 ◽

Cited By ~ 1

Author(s):

Abhishek Dey ◽

Sayantan Chakraborty ◽

Sarit K Das

Keyword(s):

Inverted Pendulum ◽

Pendulum System ◽

Inverted Pendulum System ◽

Simulation Results

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Analysis and modeling of an inverted pendulum system

Automation. Modern Techologies ◽

10.36652/0869-4931-2021-75-7-326-330 ◽

2021 ◽

Author(s):

Keyword(s):

Mathematical Model ◽

Nonlinear System ◽

Inverted Pendulum ◽

System Analysis ◽

Dimensional System ◽

Two Dimensional ◽

System Operation ◽

Pendulum System ◽

Inverted Pendulum System ◽

Two Dimensional System

A nonlinear system, which consists of an inverted pendulum mounted on a cart with an electric drive, is considered. A mathematical model is created, its analysis and modeling of the investigated two-dimensional system operation is carried out. Keywords mathematical model; inverted pendulum; system analysis; state space

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Exquisite disturbance attenuation control for a rotary inverted pendulum

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331216667809 ◽

2016 ◽

Vol 40 (3) ◽

pp. 812-818 ◽

Cited By ~ 1

Author(s):

Shuaipeng He ◽

Chuanlin Zhang ◽

Ning Yang ◽

Hui Li

Keyword(s):

Inverted Pendulum ◽

Disturbance Attenuation ◽

Control Performance ◽

Pendulum System ◽

Nonlinear Disturbance Observer ◽

System A ◽

Inverted Pendulum System ◽

Nonlinear Disturbance ◽

Simulation Results ◽

Rotary Inverted Pendulum

This paper investigates the problem of active disturbance attenuation control for a rotary inverted pendulum system. A nonlinear disturbance observer is first constructed to estimate the lumped disturbances, and then a feedback domination technique is integrated to handle the nonlinearities in a novel step-by-step way. Hence an exquisite composite controller can be constructed with strong robustness while the nominal control performance can be maintained. By utilizing a recursive stability analysis based on a Lyapunov function, the effectiveness of the proposed controller is assured. Numerical simulation results demonstrate the effectiveness of the proposed algorithm.

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LMI-based Multiobjective Integral Sliding Mode Control for Rotary Inverted Pendulum System Under Load Variations

Jurnal Teknologi ◽

10.11113/jt.v73.4444 ◽

2015 ◽

Vol 73 (6) ◽

Cited By ~ 1

Author(s):

Fairus, M. A. ◽

Mohamed, Z. ◽

Ahmad, M. N. ◽

Loi, W. S.

Keyword(s):

Nonlinear System ◽

Inverted Pendulum ◽

Sliding Mode ◽

Space Representation ◽

Linear Matrix ◽

Pendulum System ◽

Integral Sliding Mode ◽

State Space Representation ◽

Inverted Pendulum System ◽

Rotary Inverted Pendulum

This paper presents a multiobjective integral sliding mode controller (ISMC) for a rotary inverted pendulum system under the influence of varying load. Firstly, the nonlinear system is approximated to facilitate the desired control design via extended linearization and deterministic approach. By using both of these techniques, the nonlinear system is formulated into a nonlinear state-space representation where the uncertainties are retained in the model. Next, the design objectives are formulated into linear matrix inequalities (LMI) which are then solved efficiently through convex optimization algorithms. With proper selection variables, numbers of the decision variables for LMIs are reduced. Hence, it will reduce the numerical burden and believes the calculated values more viable in practice. Finally, simulation works are conducted and comparison is made between the proposed controller, such as normal ISMC and LQR. The simulation results illustrate the effectiveness of the proposed controller and the performance is evaluated through integral of absolute-value error (IAE) performance index.

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