Controlling the non-parametric modeling of Double Link Flexible Robotic Manipulator using Hybrid PID tuned by P-Type ILA

Utilization of robotic manipulator with multi-link structure encompasses a great influence in most of the present industries. However, controlling the motion of multi-link manipulator has become a troublesome errand particularly once the flexible structure is employed. As of now, the framework utilizes the complicated arithmetic to resolve desired hub angle with the coupling result and vibration within the framework. Hence, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the enhancement on hub angle position and vibration concealment. The research utilised neural network because the model estimation supported NARX model structure. In the controllers’ development, this research focuses on self-tuning controller. P-Type iterative learning algorithm (ILA) control theme was enforced to adapt the controller parameters to fulfill the required performances once there is changes to the system. The hybrid of proportional-integral-derivate (PID) controller was developed for hub motion and end-point vibration suppression of every link respectively. The controllers were tested in MATLAB/Simulink simulation setting. The performance of the controller was compared with the fixed hybrid PID-PID controller in term of input tracking and vibration concealment. The results indicated that the proposed controller was effective to maneuver the double-link flexible robotic manipulator to the specified position with reduction of the vibration at the tip of the DLFRM structure

Intelligent PID Controller of Flexible Link Manipulator with Payload

This paper presents the experimental study of intelligent PID controller with the present of payload. The controllers were constructed to optimally track the desired hub angle and vibration suppression of DLFRM. The hub angle and end-point vibration models were identified based on NNARX structure. The results of all developed controllers were analyzed in terms of trajectory tracking and vibration suppression of DLFRM subjected to disturbance. The simulation studies showed that the intelligent PID controllers have provided good performance. Further investigation via experimental studies was carried out. The results revealed that the intelligent PID control structure able to show similar performance up to 20 g of payload hold by the system. Once the payload increased more than 20 g, the performance of the controller degrades. Thus, it can be concluded that, the controllers can be applied in real application, provided the tuning process were carried out with the existence of the maximum payload which will be subjected in the system. The 20 g payload value can act as uncertainty for the controller performance

Utilizing P-Type ILA in tuning Hybrid PID Controller for Double Link Flexible Robotic Manipulator

The usage of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop the controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. The research utilized DLFRM modeling based on NARX model structure estimated by neural network. In the controllers' development, this research focuses on adaptive controller. PType iterative learning algorithm (ILA) control scheme is implemented to adapt the controller parameters to meet the desired performances when there are changes to the system. The hybrid PID-PID controller is developed for hub motion and end point vibration suppression of each link respectively. The controllers are tested in MATLAB/Simulink simulation environment. The performance of the controller is compared with the fixed hybrid PID-PID controller in term of input tracking and vibration suppression. The results indicate that the proposed controller is effective to move the double-link flexible robotic manipulator to the desired position with suppression of the vibration at the end of the double-link flexible robotic manipulator structure

Intelligent cuckoo search algorithm of PID and skyhook controller for semi-active suspension system using magneto-rheological damper

This article introduces the application of the Cuckoo Search (CS) Algorithm to tune Proportional-Integral-Derivative (PID) and Skyhook controller for the semi-active (SA) suspension system further to improve the vehicle's ride comfort and stability. Meanwhile, the PID-CSA and Skyhook-CSA intelligent approaches have been compared to the passive suspension system. The performance of the PID controller and Skyhook controller are optimised by Cuckoo Search (CS) Algorithm, respectively. The system's mean square error (MSE) is defined as the objective function for optimising the proposed controllers. The performance of the proposed PID-CSA and Skyhook-CSA controllers are evaluated with the passive suspension system in the form of body acceleration, body displacement, and tire acceleration. The sinusoidal road profile is set as the disturbance of this system. The percentage improvement for body acceleration and body displacement achieved about 25% for the PID-CSA controller and 1-4% for Skyhook-CSA. These simulated results reflect that the proposed controllers outperformed other considered methods to obtain the most effective vehicle stability and ride comfort

Vibration control of semi-active suspension system using modified skyhook with advanced firefly algorithm

The semi-active suspension (SAS) system is a partial suspension device used in the vehicle system to improve the ride comfort and road handling. Due to the high non-linearity of the road profile disturbances plus uncertainties derived from vehicle dynamics, a conventional Skyhook controller is not deemed enough for the vehicle system to improve the performance. A major problem of the implementation of the controller is to optimize a proper parameter as this is an important element in demanding a good controller response. An advanced Firefly Algorithm (AFA) integrated with the modified skyhook (MSky) is proposed to enhance the robustness of the system and thus able to improve the vehicle ride comfort. In this paper, the controller scheme to be known as MSky-AFA was validated via MATLAB simulation environment. A different optimizer based on the original firefly algorithm (FA) is also studied in order to compute the parameter of the MSky controller. This control scheme to be known as MSky-FA was evaluated and compared to the proposed MSky-AFA as well as the passive suspension control. The results clearly exhibit more superior and better response of the MSky-AFA in reducing the body acceleration and displacement amplitude in comparison to the MSky-FA and passive counterparts for a sinusoidal road profile condition

Genetic algorithms based adaptive active vibration control of a flexible structure

This paper investigates the development of an active vibration control (AVC) mechanism for a flexible plate structure using a genetic modelling strategy where the utilisation of genetic algorithms (GAs) for dynamic modelling of the system is considered. The global search technique of GAs is used to obtain a dynamic model of a flexible plate structure based on one-step-ahead (OSA) prediction and verified within the AVC system. The GA based AVC algorithm thus developed is implemented within a flexible plate simulation environment and its performance in the reduction of deflection at the centre of the plate is assessed. The validation of the algorithm is presented in both the time and frequency domains. An assessment of the results thus obtained is given in comparison to the AVC system using conventional recursive least squares (RLS) method. Investigations reveal that the developed GA based AVC system performs better in the suppression of vibration of a flexible plate structure compared to an RLS based AVC system

Non-parametric neuro-model of a flexible beam structure

In this paper, the development of dynamic model of flexible cantilever (fixed-free) beam in transverse motion using FD approach is presented. Validation using theoretical value is carried out in order to ensure the reliability of the developed FD algorithm. Next, system identification using non-parametric Neural Network (NN) methods: Multilayer Perceptron (MLP) and ELMAN networks are developed. To suppress the unwanted vibration, simulated case studies of AVC using P and PI control schemes are investigated. Results demonstrate that PI control methods outperformed P controller in cancelling the vibration

Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper

This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system's mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc-Wen model of a MR damper is used to determine the required damping force based on force-displacement and force-velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL-PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis

Fuzzy-pid control of transverse vibrating pipe due to vortex induced vibration

This paper presents the fuzzy-PID control of flexibly mounted rigid pipe which undergoes vortex induced vibration phenomena. The cylinder is allowed to move in one direction which is perpendicular to the flow direction. The input and output data are obtained from experimental works where the input is the current flow speed and the output is the acceleration of the pipe. Auto-Regressive External Input (ARX) model is used as the transfer function structure whereas Recursive Least Square (RLS) technique is adopted in order to determine the parameter of the ARX model. Fuzzy-PID controller are developed and applied to the vibrating system that represented by the ARX model. Two types of external disturbances are applied in order to determine the robustness of the controller. It was shown that the developed controller successfully attenuate the vibration of the system, regardless the type of external disturbance exerted to the system