Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

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

Intelligent controllers for velocity tracking of two wheeled inverted pendulum mobile robot

Velocity tracking is one of the important objectives of vehicle, machines and mobile robots. A two wheeled inverted pendulum (TWIP) is a class of mobile robot that is open loop unstable with high nonlinearities which makes it difficult to control its velocity because of its nature of pitch falling if left unattended. In this work, three soft computing techniques were proposed to track a desired velocity of the TWIP. Fuzzy Logic Control (FLC), Neural Network Inverse Model control (NN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were designed and simulated on the TWIP model. All the three controllers have shown practically good performance in tracking the desired speed and keeping the robot in upright position and ANFIS has shown slightly better performance than FLC, while NN consumes more energy

Modeling and controller design of a single-linked inverted pendulum using optimized fuzzy logic controller approach

Inverted pendulum (IP) is an underactuated systems, since the input of the system is the force applied to the cart and the outputs are the cart position and pendulum angle (SIMO) system, which makes this system is highly nonlinear and unstable. Inverted pendulum considered as the one the most famous classical systems in the field of control and mechatronics. This project focuses on the design of a fuzzy controller to stabilize an inverted pendulum in a vertical position. A continuous correction mechanism is required to move the cart in a certain way in order to balance the pendulum to prevent it from falling down. This project started by a derivation of the mathematical model of the single linked inverted pendulum system by using Euler-Lagrange method. After that, a fuzzy logic controller (FLC) based Sugeno inference system was designed and genetic algorithm was used to tune the parameters of the controller using MATLAB software. Both controllers were tested using real time inverted pendulum. Experimental results showed that optimized FLC was much better than Sugeno FLC in terms of settling time, overshoot and steady state error

Control of a Two-wheeled Machine with Two-directions Handling Mechanism Using PID and PD-FLC Algorithms

This paper presents a novel five degrees of freedom (DOF) two-wheeled robotic machine (TWRM) that delivers solutions for both industrial and service robotic applications by enlarging the vehicle′s workspace and increasing its flexibility. Designing a two-wheeled robot with five degrees of freedom creates a high challenge for the control, therefore the modelling and design of such robot should be precise with a uniform distribution of mass over the robot and the actuators. By employing the Lagrangian modelling approach, the TWRM′s mathematical model is derived and simulated in Matlab/Simulink®. For stabilizing the system′s highly nonlinear model, two control approaches were developed and implemented: proportional-integral-derivative (PID) and fuzzy logic control (FLC) strategies. Considering multiple scenarios with different initial conditions, the proposed control strategies′ performance has been assessed

Using real interpolation method for adaptive identification of nonlinear inverted pendulum system

In this paper, we investigate the inverted pendulum system by using real interpolation method (RIM) algorithm. In the first stage, the mathematical model of the inverted pendulum system and the RIM algorithm are presented. After that, the identification of the inverted pendulum system by using the RIM algorithm is proposed. Finally, the comparison of the linear analytical model, RIM model, and nonlinear model is carried out. From the results, it is found that the inverted pendulum system by using RIM algorithm has simplicity, low computer source requirement, high accuracy and adaptiveness in the advantages

Modelling And Fuzzy Logic Control Of An Underactuated Tower Crane System

Tower crane is one of the flexible maneuvering systems that has been applied pervasively as a powerful big-scale construction machine. The under-actuated tower crane system has nonlinearity behavior with a coupling between translational and slew motions which increases the crane control challenge. In practical applications, most of the tower cranes are operated by a human operator which lead to unsatisfactory control tasks. Motivated to overcome the issues, this paper proposes a fuzzy logic controller based on single input rule modules dynamically connected fuzzy inference system for slew/translational positioning and swing suppressions of a 3 degree-of-freedom tower crane system. The proposed method can reduce the number of rules significantly, resulting in a simpler controller design. The proposed method achieves higher suppressions of at least 56% and 81% in the overall in-plane and out-plane swing responses, respectively as compared to PSO based PID+PD control

CONTROL OF A PENDULUM USING HEDGE ALGEBRAS CONTAINING ACTUATOR SATURATION

In this study, the control problem of a pendulum using hedge-algebras-based fuzzy controller (HAC) containing actuator saturation is presented. In HAC, linguistic values of linguistic terms are obtained through semantically quantifying mappings (SQMs) based on several fuzzy parameters of each linguistic variable without using any fuzzy set and inherent order relationships between linguistic values are always ensured. Hence, the design of a HAC leads to determining parameters of SQMs. Numerical results of HAC are compared with those of an analogical conventional fuzzy controller (FC) in order to show advantages of the proposed method.In this study, the control problem of a pendulum using hedge-algebras-based fuzzy controller (HAC) containing actuator saturation is presented. In HAC, linguistic values of linguistic terms are obtained through semantically quantifying mappings (SQMs) based on several fuzzy parameters of each linguistic variable without using any fuzzy set and inherent order relationships between linguistic values are always ensured. Hence, the design of a HAC leads to determining parameters of SQMs. Numerical results of HAC are compared with those of an analogical conventional fuzzy controller (FC) in order to show advantages of the proposed method

ADAPTIVE WAVELETS SLIDING MODE CONTROL FOR A CLASS OF SECOND ORDER UNDERACTUATED MECHANICAL SYSTEMS

The control of underactuated mechanical systems (UMS) remains an attracting field where researchers can develop their control algorithms. To this date, various linear and nonlinear control techniques using classical and intelligent methods have been published in literature. In this work, an adaptive controller using sliding mode control (SMC) and wavelets network (WN) is proposed for a class of second-order UMS with two degrees of freedom (DOF).This adaptive control strategy takes advantage of both sliding mode control and wavelet properties. In the main result, we consider the case of un-modeled dynamics of the above-mentioned UMS, and we introduce a wavelets network to design an adaptive controller based on the SMC. The update algorithms are directly extracted by using the gradient descent method and conditions are then settled to achieve the required convergence performance.The efficacy of the proposed adaptive approach is demonstrated through an application to the pendubot