Swinging up and stabilization of a real inverted pendulum

IEEE Access

This paper investigates the efficacy of an optimized fuzzy logic controller for real-time swing-up control and stabilization to a rigidly coupled twin-arm inverted pendulum system. The proposed fuzzy controller utilizes Lyapunov criteria for controller design to ensure system stability. The membership functions are further optimized based on the entropy function. The controller design is based on the black-box approach, eliminating the need for an accurate mathematical model of the system. The experimental results shows an improvement in the transient and steady-state response of the controlled system as compared to other state-of-the-art controllers. The proposed controller exhibits a small settling time of 4.0 s and reaches the stable swing-up position within 5 oscillations. Various error indices are evaluated that validates an overall improvement in the performance of the system.

Information

The double-inverted pendulum (DIP) constitutes a classical problem in mechanics, whereas the control methods for stabilizing around the equilibrium positions represent the classic standards of control system theory and various control methods in robotics. For instance, it functions as a typical model for the calculation and stability of walking robots. The present study depicts the controlling of a double-inverted pendulum (DIP) on a cart using a fuzzy logic controller (FLC). A linear-quadratic controller (LQR) was used as a benchmark to assess the effectiveness of our method, and the results showed that the proposed FLC can perform significantly better than the LQR under a variety of initial system conditions. This performance is considered very important when the reduction of the peak system output is concerned. The proposed controller equilibration and velocity tracking performance were explored through simulation, and the results obtained point to the validity of the control met...

Applied Sciences, 2020

In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The stability of the closed-loop system is proven via the Lyapunov theory, and boundedness of the solutions is guaranteed. The proposed controller is heuristically tuned and its performance is tested via simulation and real-time experimentation. For this reason, a tuning method is investigated via evolutionary algorithms: particle swarm optimization, firefly algorithm and differential evolution in order to optimize the performance and verify which technique produces better results. First, a model-based simulation is carried out to improve the parameter tuning of the fuzzy systems, and then the results are transferred to real-time experiments. The optimization procedure is presented as well as the experimental results, which ...

International Journal of Electrical and Computer Engineering (IJECE), 2018

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error. Keyword: Adaptive neuro-fuzzy inference system (ANFIS) Intelligent control Inverted pendulum (IP) Linear quadratic regulator (LQR) Sugeno FIS

ArXiv, 2019

Complexity and nonlinear behaviours of inverted pendulum system make its control design a very challenging task. In this paper, a hybrid fuzzy adaptive control system using model reference approach is designed for inverted-pendulum system control. The proposed method is developed to achieve position control and later simultaneous control of position and pendulum angle in the same control loop. Also, the control algorithm is applied to achieve control objective of reference tracking, disturbance rejection and robustness to parameter variation. The performance of the proposed control scheme was compared with conventional PID and LQR controllers. The simulation results showed that the proposed control scheme provides high-performance dynamic characteristics and is robust with regard to parametric variations, disturbance and reference tracking compared to the comparatives

In this paper a real time control for swing-up stabilization of inverted pendulum is designed using Fuzzy logic. In the model proposed here a single rulebase is used to control both position and angle simultaneously during both swing-up and stabilization. The proposed fuzzy control scheme successfully fulfills the control objectives and also has an excellent stabilizing ability to overcome the external impact acting on the pendulum system. The effectiveness of this controller is verified by experiments on a simple inverted pendulum with fixed cart length.

2013 Students Conference on Engineering and Systems (SCES), 2013

Robust Control has been used in various applications to improve the performance of the system. The Inverted pendulum (also called "Cart-Pole system) is a classical example of nonlinear and unstable control system. In This paper we present different design techniques of controller for stabilizing the inverted pendulum (cart system) problem and there comparative analysis of performance and reliability which is done through simulation on MATLab-Simulink. Robust control (Hco) in association with fuzzy produce better response as compared to fuzzy controller.

Journal of Vibration and Control, 2021

Accurate modeling and efficient control of inverted pendulums have always been a challenge for researchers. So, the current research aims to achieve the following objectives: (I) proposing a comprehensive dynamic model for the inverted pendulums which accounts for the flexibility of the pendulum bar and (II) suggesting an appropriate supervisory fuzzy-pole placement control strategy for stabilizing the pendulum system. Using a Lagrangian formulation, the equations of motion are derived and linearized. Then, a state feedback controller with a reduced-order observer is designed to stabilize the system. Closed-loop simulations reveal that at least six modes shall be considered in the dynamic equations. To improve the quality of the transient response, a novel fuzzy system is developed for real-time assignment of the controller poles. Simulation results demonstrate that the control quality is significantly improved by adding a supervisory fuzzy system to the control loop. The developed ...

2013

The application of different types of FLC and conventional PID controllers to the Inverted pendulum problem is presented in this paper. The fuzzy logic controllers have been used to control many nonlinear systems. They are designed in various forms in the MatlabSimulink environment with Mamdani type fuzzy inference system. The Inverted Pendulum system (also called “cart-pole system”) is a challenging,nonlinear and unstable control system. By controlling the force applied to the cart in the horizontal direction, the inverted pendulum can be kept in various unstable equilibrium positions. Fuzzy control in association with PID control is found better amongst the fuzzy PD and fuzzy PD+I control.

International Journal of Computer Applications, 2013

This paper proposes an intelligent control approach towards Inverted Pendulum in mechanical engineering. Inverted Pendulum is a well known topic in process control and offering a diverse range of research in the area of the mechanical and control engineering. Fuzzy controller is an intelligent controller based on the model of fuzzy logic i.e. it does not require accurate mathematical modelling of the system nor complex computations and it can handle complex and non linear systems without linearization. Our objective is to implement a Fuzzy based controller and demonstrate its application to Inverted Pendulum. Model design and simulation are done in MATLAB SIMULINK ® software.

Proceedings of the 17th IFAC World Congress, 2008, 2008

The hybrid solution to the pendulum swinging-up and stabilizing problem introduced byÅström and Furuta is based in two steps: an energy injection and a linear stabilization around the desired inverted position. However the energy injection stage only considers the pendulum, and not the motion of the pivot. Furthermore, for the stabilization stage linear law, only a very small basin of attraction can be guaranteed. In this paper the energy controller is enlarged to cope with the pivot dynamics and a nonlinear controller is introduced for the stabilization stage with a larger basin of attraction. The approach proposed allows to cope both with the pendulum on a cart and the Furuta one. Experiments with a laboratory Furuta pendulum are included.

International Journal of Information Technology and Computer Science, 2016

This paper illustrates a comparison study for control of highly non-linear Double Inverted Pendulu m (DIP) on cart. A Matlab-Simu link model of DIP has been built using Newton's second law. The Neuro-fuzzy controllers stabilizes pendulums at vert ical position while cart moves in horizontal d irect ion. This study proposes two soft-computing techniques namely Fuzzy logic reasoning and Neural networks (NN's) for control of DIP systems. The results shows that Fuzzy controllers provides better results as compared to NN's controllers in terms of settling time (sec), maximu m overshoot (degree) and steady state error. The regression (R) and mean square error (MSE) values obtained after training of Neural network were satisfactory. The simulation results proves the validity of proposed techniques.

An inverted pendulum is a pendulum that has its center of mass above its pivot point. It is unstable and without external support will fall over. It can be suspended stably in this inverted position by using a control system to monitor the angle of the pendulum and move the pivot point horizontally back under the center of mass when it starts to fall over, keeping it balanced. This Paper present the intelligent methods based on fuzzy logic for tuningPID controller. Simulation results reveals that intelligent methods provide better performance than the conventional methods

arXiv (Cornell University), 2018

In this paper, a nonlinear rotational inverted pendulum with time-varying parameters is controlled using the indirect adaptive fuzzy controller design. This type of controller is chosen because this particular system performance is highly sensitive to unavoidable unknown model changes. So, a conventional controller is firstly designed through feedback linearization method, and applied to the system. Feedback linearization method here is used for two purposes; to attain an approximation of necessary system dynamics and to assess the performance of the proposed adaptive fuzzy controller by comparing the results of both adaptive fuzzy and feedback linearization controllers. An indirect adaptive fuzzy controller, resistant to parameter variations is then proposed. The general structure of the adaptive controller is specified in the first stage. In the second stage, its parameters are regulated with the aid of two fuzzy systems. Lyapunov stability theorem is used to regulate the system parameters such that the closed loop system is stabilized and zero tracking error is attained. Finally, the results of the proposed and the conventional approaches are compared. Results showed that the adaptive fuzzy controller performed more efficiently than the classical controller, with existing parameters variations.

Abstract—Inverted pendulum is a system having a nonlinear mathematic model, when inspected properly perishable balance condition pendulum angle and the vehicle position can be controlled by an input applied to the vehicle and dynamically unstable. This type of non-linear system control applications of conversion capabilities of fuzzy logic based controllers are successful. In this study, the non-linear dynamic inverted pendulum system based on fuzzy logic control method of different developed to control system performance and effects will be explored. Keywords— Fuzzy logic based controller, inverted pendulum.