Design of State Feedback Controller with Optimal Parameters Using Bat Algorithm for Reaction Wheel Pendulum

Xuan, Chiem Nguyen; Tran, Thang Le

doi:10.1109/atc52653.2021.9598306

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Additional contributions aiming at position tracking and system stabilization of a single reaction wheel pendulum utilized the 4 th order discontinuous integral algorithm coupled with a homogeneous Lyapunov function to tune the PID gains [24], a 2 nd order sliding mode control verified by an Open Dynamic Engine (ODE) simulation [25], and a synthetic state feedback controller with parameters optimization based on the BAT algorithm optimized through the integral of a time-weighted absolute error objective function [26].…”

Section: Introductionmentioning

confidence: 99%

Counter-Rotating Hoop Stabilizer and SVR Control for Two-Wheels Vehicle Applications

et al. 2023

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The matter of stabilization has always attracted attention from both academia and industry. In the case of bikes, kickboards, and scooters, simple stabilizer designs have been explored but are rarely applied in commercial products. In this research, a small-scale, customizable, two-wheels design, is proposed and connected to a retroactive closed-loop control unit for the automatic correction of the destabilization during the motion. The design is based on two counter-rotating wheels, spun into motion at the beginning of the ride and controlled by an Inertial Measurement Unit (IMU) sensor. The two DC motors controlling the spinning of the balancing wheels are adjusted by means of Pulse Width Modulation (PWM) input in the 0 ~ 255 PWM range. The control is based on an ARDUINO Uno Rev3 microcontroller and on a Support Vector Regression (SVR) model coupled with a Radial Basis Function (RBF) kernel. If an angular deviation outside the user-defined range is detected by the IMU sensor, the trained SVR-RBF model predicts the required PWM value to reestablish equilibrium and sends the signals to one or both DC motors. The proposed architecture was trained and validated in a ±21º range, resulting in a 100% correction accuracy up to a ±23º range, whereas, for greater angles up to ±30º, a drop in performances was observed. In addition to that, when a random acceleration in the ±6°/s 2 range was applied, the proposed design showed a remarkable capability of predicting the right PWM values, for both reaction wheels, capable of reestablishing equilibrium in the system within an average intervention time equal to 1.28s.

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Section: Introductionmentioning

confidence: 99%

Counter-Rotating Hoop Stabilizer and SVR Control for Two-Wheels Vehicle Applications

et al. 2023

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“…This paper uses the BAT algorithm, which is one of the PSO algorithms, but it has advantages for multivariate problems [54]- [80]. The BAT algorithm learns from the movement of bats in searching for prey and avoiding obstacles to build a method to find the extrema for the objective function [62]- [64]. In terms of the mathematical model of the system, the LQR controller is designed based on swarm optimization and takes advantage of the searchability of the swarm algorithm (BAT) to optimize the matrices.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

Chiem,

Thang

2023

Journal of Robotics and Control

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In this study, a controller design method based on the LQR method and BAT algorithm is presented for the Furuta pendulum stabilization system. Determine the LQR controller, it is often based on the designer's experience or using trial and error to find the Q, R matrices. The BAT search algorithm is based on the characteristics of the bat population in the wild. However, there are advantages to finding multivariate objective functions. The BAT algorithm has an improvement for the LQR controller to optimize the linear square function with fast response time, low energy consumption, overshoot, and a small number of oscillations. Swarm optimization algorithms have advantages in finding global extrema of multivariate functions. Therefore, with a large number of elements of the Q and R matrices, they can also be quickly found and these matrices still satisfy the Riccati equation. The controller with optimal parameters is verified through simulation results with different scenarios. The performance of the proposed controller is compared with a conventional LQR controller and implemented on a real system.

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Design of State Feedback Controller with Optimal Parameters Using Bat Algorithm for Reaction Wheel Pendulum

Cited by 2 publications

References 6 publications

Counter-Rotating Hoop Stabilizer and SVR Control for Two-Wheels Vehicle Applications

Counter-Rotating Hoop Stabilizer and SVR Control for Two-Wheels Vehicle Applications

Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

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