PD-type Fuzzy Logic Controller Research Articles

Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system

Two-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system. This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input–output data. Ensuring stable and responsive control, design of optimized PD-type and PID-type fuzzy logic controllers using Particle Swarm Optimization (PSO) were established and were tested under a simulation environment. The performance was evaluated across various metrics, including Integral Squared Error (ISE), Integral Absolute Error (IAE), Mean Squared (MSE), and Integral Time Absolute Error (ITAE). The result demonstrates that the PSO Optimized PID-type fuzzy logic controller with scaling factor from MSE index performance come out as best overall, significantly outperforms PD-type fuzzy logic controller, reducing its settling time by 12.5% to 35 s, minimizing overshoot to 0.81°, and achieving a negligible steady-state error of 0.046%. These results highlight the significant of integrating fuzzy logic control and PSO to the neural network model in enhancing the stability and performance of two-wheeled wheelchair systems, offering user safety and comfort.

Error driven Fuzzy Logic Controller (FLC) for Spherical Mobile Robot: Simulation & Experimental Performance Analysis

Spherical mobile robot is a ball-shaped mobile robot that capable to move from one place to another to perform the desired tasks. The challenge for spherical mobile robot falls within control algorithm aspects, where it is nonlinear and highly under-actuated increases the difficulties to control its motion. In this paper, the potentiality of intelligent controller is explored to control the motion spherical mobile robot. An error driven Fuzzy Logic controller (FLC) which consist of modular of two Proportional-Derivative (PD) type FLC and a Proportional-Integral (PI) type FLC were designed and tested to control the position of the spherical mobile robot, angle of the rotation of the pendulum and the angular velocity of the spherical mobile robot, respectively. The performance of the controller was then optimized by tuning the input and output gain of the controller with Particle Swarm optimization (PSO) method. The performance of the robot without the controller and with optimized controller were compared and analysed. As a result, the performance of the system in the form of rise time and settling time of the robot to control its position managed to be reduced by 86% and 89% respectively with optimized PD-type. For PD-type FLC to control the rotation of the pendulum, it was capable to eliminate the overshoot and reduce it settling time by 35.5%. While the optimized PI-type FLC has managed to eliminate the steady state error and achieved it desired velocity setpoint in 1.2s. Lastly, all the designed controllers were successfully tested on the prototype of the spherical mobile robot to study the performance of the spherical mobile robot. However, the overall performance of the implemented designed controller has shown a minimum effectiveness. The controller managed to achieve the setpoint value but with low stability which result from the less accuracy of the hardware sensors.

A Comparative Study on Applying Bio-Inspired Optimization Techniques for Designing an Effective Fuzzy Logic Controller

A fuzzy logic – based controller has been considered to be a feasible and effective control strategy for a numerous number of complex control problems. To design such an effective fuzzy logic controller, it is necessary to determine proper scaling factors which significantly affect the control quality of the system. This study concentrates on applying well-known bio-inspired optimization methods, i.e. PSO, GA and DE, to deal with this determination. A typical PD-type fuzzy logic architecture is chosen to be a traditionally intelligent controller. Then, the bio-inspired optimization methods will be applied for such a fuzzy logic controller to optimally determine its three scaling factors. The simulation results provided for the load – frequency control issue of a two-area interconnected hydropower system verifies the applicability of the proposed control strategy. Comparative simulations are also to decide which is the best choice of the bio-inspired optimization methods in the determination of significant scaling factors regarding the PD-type fuzzy logic controller.

An Optimized Fuzzy-Padé Controller Applied to Attitude Stabilization of a Quadrotor

In this research, an Optimized Fuzzy-Pade Controller (OFPC) for attitude stabilization of a quadrotor is proposed by using Pade approximants and fuzzy singleton rules. To determine unknown coefficients of the Pade approximant, a PD-type Fuzzy Logic Controller (FLC) is first designed. As the number of the fuzzy singleton rules extracted from the FLC is less than the number of the unknown coefficients, some of them cannot be determined using the rules. The coefficients not specified are determined applying particle swarm optimization algorithm to increase convergence speed and decrease energy consumption. The simulation results for the OFPC demonstrate faster convergence speed, lower power consumption, larger convergence region, better robustness, and faster computational time in comparison to the FLC. Unlike the FLC, the OFPC is fast enough to be implemented on the microcontroller of the quadrotor. The experimental results indicate that the proposed controller quickly stabilizes the quadrotor, even with externally applied disturbances. The proposed approach possesses certain advantages over FLCs, and it can be employed wherever FLCs are not applicable due to the high computational burden.

Modelling and Simulation Analysis of Rolling Motion of Spherical Robot

This paper presents the findings of modelling, control and analysis of the spherical rolling robot based on pendulum driven within the simulation environment. The spherical robot is modelled using Lagrange function based on the equation of rolling motion. PD-type Fuzzy logic controller (FLC) was designed to control the position of the spherical robot where 25 rules were constructed to control the rolling motion of spherical robot. It was then integrated with the model developed in Simulink-Matlab environment. The output scaling factor (output gain) of the FLC was heuristically tuned to improve the system performance. The simulation results show that the FLC managed to eliminate the overshoot response and demonstrated better performance with 29.67% increasing in settling time to reach 0.01% of steady state error.

A novel integration of pd-type fuzzy logic controllers and smes devices to maintain the network frequency of a large-scale power system

A novel integration of pd-type fuzzy logic controllers and smes devices to maintain the network frequency of a large-scale power system

Modelling and Performance Comparison of PD and Takagi-Sugeno Type Fuzzy Logic Controllers for Inverted Pendulum System

In recent years, fuzzy logic based modeling and control methods have been increasingly used in both industrial applications and academic field. Inverted pendulum system consists of conventional materials with nonlinear dynamic structure to test types of controllers. This paper presents a simulation study of Takagi-Sugeno type Fuzzy Logic Controllers for an inverted pendulum (cart-pole) system. A Takagi-Sugeno method was applied to the inverted-pendulum system, which can balance the pendulum over a bigger range of pole angle and cart position. Fuzzy logic controller proposed in this study is compared with the conventional PD controller in order to demonstrate the successes of the proposed controller. This study aims to determine the best control strategy that provides better performance with reference to pendulum's angle and cart's position. A Takagi-Sugeno type Fuzzy Logic Controller yields better results in terms of rise time, settling time, overshoot and steady state error compared to PD controller. Simulation results are shown to prove the effectiveness and robustness of the suggested fuzzy controller.

Suppression of structural vibrations using PDPI + PI type fuzzy logic controlled active dynamic absorber

A new PDPI + PI type fuzzy logic controller for vibration suppression of buildings has been proposed in this study. First, a two-story building model that consists of steel plates and columns has been constructed and free vibration tests have been performed to obtain model parameters for simulations. Then a dynamic absorber with optimal mass value has been designed. Afterwards, to combine the advantages of PD and PI type fuzzy logic controllers, a new PDPI + PI type fuzzy logic controller has been designed. The numerical results have shown that using proposed controller the designed active dynamic absorber reduced building vibrations effectively.

Fuzzy force learning controller of flexible wiper system

Wiper blade of automobile is among those types of flexible system that is required to be operated in quite high velocity to be efficient in high load conditions. This causes some annoying noise and deteriorated vision for occupants. The modeling and control of vibration and low-frequency noise of an automobile wiper blade using soft computing techniques are focused in this study. The flexible vibration and noise model of wiper system are estimated using artificial intelligence system identification approach. A PD-type fuzzy logic controller and a PI-type fuzzy logic controller are combined in cascade with active force control (AFC)-based iterative learning (IL). A multi-objective genetic algorithm is also used to determine the scaling factors of the inputs and outputs of the PID-FLC as well as AFC-based IL gains. The results from the proposed controller namely fuzzy force learning (FFL) are compared with those of a conventional lead---lag-type controller and the wiper bang---bang input. Designing controllers based on classical methods could become tedious, especially for systems with high-order model. In contrast, FFL controller design requires only tuning of some scaling factors in the control loop and hence is much simpler and efficient than classical design methods.

Implementation of Fuzzy Logic Controller for Wheelchair Motion Control Based on EOG Data

The study of this paper is to implementation the fuzzy logic control designed for wheelchair motion based on the eye movement signals using electrooculograhphy (EOG) technique. This technique is to acquire the eye movement data from a person, for example, tetraplegia. The tetraplegia is paralysis caused by illness or injury to a human that result in the partial or total loss of use of all their limbs and torso. The eye movement data which was obtained can be used as a main communication tool between human and machine. The PD-type fuzzy controller was successfully designed and tested on the wheelchair model, for control the linear motion (focused for forward motion). The wheelchair model was developed using MSC.Visual Nastran 4D. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set by the EOG signal.

Implementation of Wheelchair Motion Control Based on Electrooculography Using Simulation and Experimental Performance Testing

The aim of this study is to perform the experimental verification on the fuzzy-based control designed for wheelchair motion. This motion control based on the eye movement signals using electrooculograhphy (EOG) technique. The EOG is a technique to acquire the eye movement data from a person, i.e tetraplegia, which the data obtained, can be used as a main communication tool. This study is about the implementation of the designed controller using PD-type fuzzy controller and tested on the hardware of the wheelchair system using the eye movement signal obtained through EOG technique as the motion input references. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set (forward and backward direction) by the EOG signal.

A Robust Fuzzy Tracking Control Scheme for Robotic Manipulators with Experimental Verification

The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.

Sway Reduction on Gantry Crane System using Delayed Feedback Signal and PD-type Fuzzy Logic Controller: A Comparative Assessment

Sway Reduction on Gantry Crane System using Delayed Feedback Signal and PD-type Fuzzy Logic Controller: A Comparative Assessment

The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

SUMMARY This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur’e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

New hybrid fuzzy controller for direct torque control induction motor drives

A new hybrid fuzzy controller for direct torque control (DTC) induction motor drives is presented in this paper. The newly developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control at transient state, and a simple switching mechanism between steady and transient states, to achieve satisfied performance under steady and transient conditions. The features of the presented new hybrid fuzzy controller are highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control (FLC), proportional-derivative (PD) type FLC, and combination of PD-type FLC and I control, for DTC-based induction motor drives. The pros and cons of these controllers are demonstrated by intensive experimental results. It is shown that the presented induction motor drive is with fast tracking capability, less steady state error, and robust to load disturbance while not resorting to complicated control method or adaptive tuning mechanism. Experimental results derived from a test system are presented confirming the above-mentioned claims.

Effects of sampling rate and scaling coefficients on the performance of a PD-type fuzzy logic controller for a two-link planar robotic arm

A computed-torque controller that uses a PD-type fuzzy logic controller as compensator is studied in this paper. Its robustness and sensitivity against modeling errors, sampling rate and scaling coefficients have been investigated and compared with conventional PD-compensated computed-torque controller. It has been shown that even though the fuzzy logic controller is more robust than its conventional competitor, its performance is affected by change of sampling rate and scaling coefficients.

Theoretical analysis of a fuzzy-logic controller with unequally spaced triangular membership functions

The mathematical analysis concerning the explicit input/output relation of the fuzzy logic controller (FLC) is addressed in this article. Under the assumption of simple rule mapping and the use of arbitrarily located triangular fuzzy partitions as the membership functions, the inferred output of the FLC can be decomposed into two terms: the global multilevel relay and the local nonlinear compensator. The ultimate control behavior of this FLC and the equivalence to nonlinear proportional-integral (PI) controller, as the number of linguistic terms are approaching to infinity, are also investigated. The local stability criteria for PI-type FLCs are derived and the global stability properties for PD-type FLCs are established. This analysis is a generalization to previous related research works where the FLC is characterized by equally spaced triangular fuzzy partitions on the universal sets.

A Self-Tuning Fuzzy PD Controller

In this article we present a self-tuning PD-type fuzzy logic controller (FLC). The output scaling factor (SF) of the proposed controller is modified on-line by an updating factor (α) according to the process trend. The value of a is determined from a rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning FLC is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighbouring MFs). The effectiveness of the scheme is established through simulation experiments on various types of second order processes such as (i) marginally stable, (ii) non-linear and (iii) non-minimum phase-pole (unstable) systems with different values of dead time. Performance of the proposed self-tuning FLC is compared with that of its conventional counterpart with respect to both step set-point change and load disturbance using several performance indices. In each case the proposed controller shows remarkably improved performance over the conventional one.