Balance control of two-wheeled self-balancing robot based on Linear Quadratic Regulator and Neural Network

2013 ◽  
Author(s):  
Chenxi Sun ◽  
Tao Lu ◽  
Kui Yuan
Keyword(s):  
Neural Network ◽  
Balance Control ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Balancing Robot

scholarly journals Stabilized controller of a two wheels robot

2020 ◽  
Vol 9 (4) ◽  
pp. 1357-1363
Author(s):  
Ahmad Fahmi ◽  
Marizan Sulaiman ◽  
Indrazno Siradjuddin ◽  
I Made Wirawan ◽  
Abdul Syukor Mohamad Jaya ◽  
...  
Keyword(s):  
Control Method ◽  
Control Function ◽  
Balance Control ◽  
Linear Quadratic Regulator ◽  
Zero Point ◽  
Stability Control ◽  
Pole Placement ◽  
Linear Quadratic ◽  
Balancing Robot ◽  
The Stability

The Segway Human Transport (HT) robot, it is dynamical self-balancing robot type. The stability control is an important thing for the Segway robot. It is an indisputable fact that Segway robot is a natural instability framework robot. The case study of the Segway robot focuses on running balance control systems. The roll, pitch, and yaw balance of this robot are obtained by estimating the Kalman Filter with a combination of the pole placement and the Linear Quadratic Regulator (LQR) control method. In our system configuration, the mathematical model of the robot will be proved by Matlab Simulink by modelling of the stabilizing control system of all state variable input. Furthermore, the implementation of this system modelled to the real-time test of the Segway robot. The expected result is by substitute the known parameters from Gyro, Accelero and both rotary encoder to initial stabilize control function, the system will respond to the zero input curve. The coordinate units of displacement response and inclination response pictures are the same. As our expected, the response of the system can reach the zero point position. 

scholarly journals Neural Network Based Contact Force Control Algorithm for Walking Robots

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 287
Author(s):  
Byeongjin Kim ◽  
Soohyun Kim
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Control Algorithm ◽  
Position Control ◽  
Linear Quadratic Regulator ◽  
Walking Robots ◽  
Test Model ◽  
Linear Quadratic ◽  
Contact Force Control

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

Fuzzy neural network control algorithm for asymmetric building structure with active tuned mass damper

2020 ◽  
Vol 26 (21-22) ◽  
pp. 2037-2049
Author(s):  
Xiao Yan ◽  
Zhao-Dong Xu ◽  
Qing-Xuan Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Linear Quadratic Regulator ◽  
Tuned Mass Damper ◽  
Linear Quadratic ◽  
Mass Damper ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Damper Control

Asymmetric structures experience torsional effects when subjected to seismic excitation. The resulting rotation will further aggravate the damage of the structure. A mathematical model is developed to study the translation and rotation response of the structure during seismic excitation. The motion equations of the structures which cover the translation and rotation are obtained by the theoretical derivations and calculations. Through the simulated computation, the translation and rotation response of the structure with the uncontrolled system, the tuned mass damper control system, and active tuned mass damper control system using linear quadratic regulator algorithm are compared to verify the effectiveness of the proposed active control system. In addition, the linear quadratic regulator and fuzzy neural network algorithm are used to the active tuned mass damper control system as a contrast group to study the response of the structure with different active control method. It can be concluded that the structure response has a significant reduction by using active tuned mass damper control system. Furthermore, it can be also found that fuzzy neural network algorithm can replace the linear quadratic regulator algorithm in an active control system. Because fuzzy neural network algorithm can control the process on an uncertain mathematical model, it has more potential in practical applications than the linear quadratic regulator control method.

scholarly journals Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot

2017 ◽  
Vol 7 (4) ◽  
pp. 2008 ◽  
Author(s):  
Modestus Oliver Asali ◽  
Ferry Hadary ◽  
Bomo Wibowo Sanjaya
Keyword(s):  
Mathematical Model ◽  
Inverted Pendulum ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Modeling Simulation ◽  
Mathematic Modeling ◽  
Lqr Controller ◽  
Output System ◽  
Balancing Robot ◽  
Cart Model

Two-wheeled self-balancing robot is a popular model in control system experiments which is more widely known as inverted pendulum and cart model. This is a multi-input and multi-output system which is theoretical and has been applied in many systems in daily use. Anyway, most research just focus on balancing this model through try-on experiments or by using simple form of mathematical model. There were still few researches that focus on complete mathematic modeling and designing a mathematical model based controller for such system. This paper analyzed mathematical model of the system. Then, the authors successfully applied a Linear Quadratic Regulator (LQR) controller for this system. This controller was tested with different case of system condition. Controlling results was proved to work well and tested on different case of system condition through simulation on matlab/Simulink program.

scholarly journals Improve Linear Quadratic Regulator by Particle Swarm Optimization Algorithms for Two Wheeled Self Balancing Mobile robot

2017 ◽  
Vol 13 (2) ◽  
pp. 173-179
Author(s):  
Ekhlas Karam ◽  
Noor Mjeed
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Method ◽  
Control Method ◽  
Particle Swarm ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Balancing Robot ◽  
The Stability ◽  
Multivariable Feedback

The aim of this paper is to suggest a methodical smooth control method for improving the stability of two wheeled self-balancing robot under effect disturbance. To promote the stability of the robot, the design of linear quadratic regulator using particle swarm optimization (PSO) method and adaptive particle swarm optimization (APSO). The computation of optimal multivariable feedback control is traditionally by LQR approach by Riccati equation. Regrettably, the method as yet has a trial and error approach when selecting parameters, particularly tuning the Q and R elements of the weight matrices. Therefore, an intelligent numerical method to solve this problem is suggested by depending PSO and APSO algorithm. To appraise the effectiveness of the suggested method, The Simulation result displays that the numerical method makes the system stable and minimizes processing time.

scholarly journals LQR optimized BP neural network PI controller for speed control of brushless DC motor

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096898
Author(s):  
Tingting Wang ◽  
Hongzhi Wang ◽  
Huangshui Hu ◽  
Chuhang Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Linear Quadratic Regulator ◽  
Speed Control ◽  
Response Speed ◽  
Back Propagation Neural Network ◽  
Pi Controller ◽  
Bldc Motor ◽  
Linear Quadratic ◽  
Brushless Dc

This paper proposes a linear quadratic regulator (LQR) optimized back propagation neural network (BPNN) PI controller called LN-PI for the speed control of brushless direct current (BLDC) motor. The controller adopts BPNN to adjust the gain [Formula: see text] and [Formula: see text] of PI, which improves the dynamic characteristics and robustness of the controller. Moreover, LQR is adopted to optimize the output of BPNN so as to make it close to the target PI gains. Finally, the optimized control output is inputted into the BLDC motor system to achieve speed control. The performance analysis of the proposed controller is presented to compare with traditional PI controller, neural network PI controller and LQR optimized PI controller under MATLAB/Simulink, the results shows that the proposed controller effectively improves the response speed, reduces the steady-state error and enhances the anti-interference ability.

An improved linear quadratic regulator control method through convolutional neural network–based vibration identification

2020 ◽  
pp. 107754632093375
Author(s):  
Xinzheng Lu ◽  
Wenjie Liao ◽  
Wei Huang ◽  
Yongjia Xu ◽  
Xingyu Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Wavelet Coefficient ◽  
Linear Quadratic Regulator ◽  
Optimal Parameters ◽  
Linear Quadratic ◽  
Negative Effects ◽  
Environmental Vibration

An efficient vibration control can reduce negative effects induced by environmental vibrations and thereby improve the performance of precision instruments and the qualities of manufacture. The performance of the widely used linear quadratic regulator control algorithm, a classical active control methodology, depends on the parameters of the control algorithm. Consequently, a set of fixed parameters cannot satisfy the demand for controlling various types of environmental vibrations. Therefore, this study proposes a vibration identification method based on a convolutional neural network. This method helps to optimize the linear quadratic regulator algorithm by selecting corresponding optimal parameters according to the identification results, thereby achieving the objective of optimal control subjected to various types of vibration inputs. Specifically, environmental vibration signals are collected, and the preliminary features of the vibrations (i.e. wavelet coefficient matrices or images) are adopted as input samples for the convolutional neural network. A genetic algorithm is used to optimize the parameters of the linear quadratic regulator algorithm for each type of vibration; subsequently, the trained convolutional neural network model with the best performance is used to identify the vibration and select the corresponding optimal parameters of the linear quadratic regulator algorithm under different types of vibration inputs. Case studies show that the performance of the improved linear quadratic regulator control method is significantly better than that of the conventional linear quadratic regulator algorithm with fixed parameters.

scholarly journals Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092461
Author(s):  
Hong Zhan ◽  
Dianye Huang ◽  
Zhaopeng Chen ◽  
Min Wang ◽  
Chenguang Yang
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Linear Quadratic Regulator ◽  
Adaptive Dynamic Programming ◽  
Environment Interaction ◽  
Linear Quadratic ◽  
Adaptive Dynamic ◽  
The Neural Network ◽  
Network Weight ◽  
The Stability

The problem of optimal tracking control for robot–environment interaction is studied in this article. The environment is regarded as a linear system and an admittance control with iterative linear quadratic regulator method is obtained to guarantee the compliant behaviour. Meanwhile, an adaptive dynamic programming-based controller is proposed. Under adaptive dynamic programming frame, the critic network is performed with radial basis function neural network to approximate the optimal cost, and the neural network weight updating law is incorporated with an additional stabilizing term to eliminate the requirement for the initial admissible control. The stability of the system is proved by Lyapunov theorem. The simulation results demonstrate the effectiveness of the proposed control scheme.

Advanced Proportional-Integral-Derivative Control Compensation Based on a Grey Estimated Model in Dynamic Balance of Single-Wheeled Robot

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 326
Author(s):  
Mao-Lin Chen ◽  
Chun-Yen Chen ◽  
Chien-Hung Wen ◽  
Pin-Hao Liao ◽  
Kai-Jung Chen
Keyword(s):  
Mathematical Modeling ◽  
Control Method ◽  
Reference Model ◽  
Balance Control ◽  
Lagrange Equation ◽  
Linear Quadratic Regulator ◽  
Dynamic State ◽  
Linear Quadratic ◽  
Wheeled Robot ◽  
The One

This paper aims to design a one-wheeled robot as regards its pitch freedom and balance control on the one hand and to assess the application feasibility of the GM (1,1) swing estimation controller on the other. System control focuses mainly on one-wheeled robot stability, body swings in position, and speed control. Mathematical modeling and GM (1,1) prediction control are under investigation. The mathematical modeling is firstly conducted through referencing to the Newtonian mechanics and the Lagrange equation, from which the robot transfer function and state-space differential equation are derived. Next, the linear quadratic regulator is applied as the control rule at the balance point. Applying GM (1,1) to assess the robot gyro signal at a dynamic state is a discussion. Next, model reference estimation control is processed, and a mathematical model of the balance control method is completed. Finally, a simulation is conducted to verify the feasibility of the GM (1,1) estimation reference model. The linear quadratic regulator, which is credited with tenacity, can provide pitch swing and balance control of the one-wheeled robot.

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