Evolutionary algorithms for active vibration control of flexible manipulator

Flexible manipulator systems offer numerous advantages over their rigid counterparts including light weight, faster system response, among others. However, unwanted vibration will occur when flexible manipulator is subjected to disturbances. If the advantages of flexible manipulator are not to be sacrificed, an accurate model and efficient control system must be developed. This thesis presents the development of a Proportional-Integral-Derivative (PID) controller tuning method using evolutionary algorithms (EA) for a single-link flexible manipulator system. Initially, a single link flexible manipulator rig, constrained to move in horizontal direction, was designed and fabricated. The input and output experimental data of the hub angle and endpoint acceleration of the flexible manipulator were acquired. The dynamics of the system was later modeled using a system identification (SI) method utilizing EA with linear auto regressive with exogenous (ARX) model structure. Two novel EAs, Genetic Algorithm with Parameter Exchanger (GAPE) and Particle Swarm Optimization with Explorer (PSOE) have been developed in this study by modifying the original Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. These novel algorithms were introduced for the identification of the flexible manipulator system. Their effectiveness was then evaluated in comparison to the original GA and PSO. Results indicated that the identification of the flexible manipulator system using PSOE is better compared to other methods. Next, PID controllers were tuned using EA for the input tracking and the endpoint vibration suppression of the flexible manipulator structure. For rigid motion control of hub angle, an auto-tuned PID controller was implemented. While for vibration suppression of the endpoint, several PID controllers were tuned using GA, GAPE, PSO and PSOE. The results have shown that the conventional auto-tuned PID was effective enough for the input tracking of the rigid motion. However, for end-point vibration suppression, the result showed the superiority of PID-PSOE in comparison to PID-GA, PID-GAPE and PID-PSO. The performance of the best simulated controller was validated experimentally later. Through experimental validation, it was found that the PID-PSOE was capable to suppress the vibration of the single-link flexible manipulator with highest attenuation of 31.3 dB at the first mode of the vibration. The outcomes of this research revealed the effectiveness of the PID controller tuned using PSOE for the endpoint vibration suppression of the flexible manipulator amongst other evolutionary methods

Fuzzy-skyhook controller with cuckoo search algorithm for a semi-active suspension system with magnetorheological damper

This study aims to investigate the performance of a semi-active suspension system of the quarter car using fuzzy-skyhook (fs) controller tuned by cuckoo search algorithm (CSA). Since the parameters of the controller are crucial to be determined, the CSA method is deemed a good approach when combined with the fuzzy-skyhook controller since the proposed controller is expected to improve the searching accuracy of the parameters. The magnetorheological (MR) damper model was developed using the Spencer model approach based on the force-velocity and force-displacement characteristics. Then, a full simulation of the suspension system excited with a sinusoidal road profile input was conducted using MATLAB/Simulink. A comparative study was carried out between the semi-active systems and benchmarked against a passive suspension. The effectiveness of the fuzzy-skyhook controller with CSA (fs-CSA) was analyzed and compared to the fuzzy-skyhook and skyhook controllers. The result indicates that fs-CSA gives the highest percentage of improvement for the body acceleration and displacement for up to 48.6% and 21.3%, respectively

Intelligent Optimization of Force Tracking Parameters for MR Damper Modelling using Firefly Algorithm

Magnetorheological (MR) damper system is commonly used to replace the conventional damper in the suspension system due to its low power consumption, fast time response and simple structure. Since inner loop controller is very important in defining the amount of current supplied to the MR damper system, many existing controllers are found not well-structured in terms of calculating the optimum value of the controller parameter. Poor control design using the conventional method will cause the output current obtained for the MR damper to be unpredictable. To overcome this problem, an intelligent optimization method known as firefly algorithm (FA) was used by this study to optimize the force tracking controller (FTC) parameters as to achieve the exact damping force of MR damper system. The MR damper was first developed using Spencer model and the required voltage input was then provided by the FTC. The controller parameters were tuned using intelligent FA method in order to find the optimum values which would identify the accuracy of the force tracking that followed the MR damping force. The simulation shows that the FTC with FA technique is able to track the desired force better than the heuristic method up to 1.71 % error considering a given desired input force

Optimization Of Pid Controller For Double-Link Flexible Robotic Manipulator Using Metaheuristic Algorithms

This paper investigates the optimization approach of PID controller for double-link flexible robotic manipulator using metaheuristic algorithm. This research focus on population-based metaheuristic that is particle swarm optimization (PSO) and artificial bees algorithm (ABC) to tune the PID control parameters of the system. In the tuning process, the number of iteration was set and the number of particles was varied. The tuning process was interrupted once the convergence value of Mean Square Error (MSE) was achieved. For PSO, it was found that when the number of iteration increased, or the number of particles were set to higher values, there were no significant improvement of MSE. Results showed that 25 iterations were required for MSE to converge for hub angle and 20 iterations were required for MSE to converge for endpoint acceleration. Meanwhile, it was discovered that ABC portrayed the same pattern with PSO whereby when the number of iteration increased or the number of colony sizes were set to higher values, there were no significant improvement of MSE. From the results, 15 iterations were required for MSE to converge for hub angle and 25 iterations were required for MSE to converge for end-point acceleration. The performance of the algorithm was validated by evaluating the performance of the controllers in comparison with the conventional controller that is Ziegler Nichols (ZN) in term of input tracking capability and vibration suppression for both links. The system managed to reach desired angle for both hub angle 1 and 2. Besides, vibration reduction shows great improvement for both link 1 and 2. This signifies that, the PSO and ABC algorithm are very effective in optimizing the PID parameters

Modelling of Flexible Manipulator System Using Flower Pollination Algorithm

The study of the flexible manipulator system (FMS) has attracted many researchers due to its superiority of light weight and faster system response. Flexible manipulator system is an improvement from its rigid structure, however it can be easily vibrated when it subjected to disturbance. If the advantages of FMS are not to be sacrificed, an accurate model and efficient control system must be developed. Thus, this study presents an approach of evolutionary swarm algorithm via flower pollination algorithm (FPA) to model the dynamic system of flexible manipulator structure. An experimental rig of flexible manipulator system was developed for input-output acquisition. Then, this input-output data was fed to system identification method to obtain a dynamic model of flexible manipulator system utilizing evolutionary algorithm with linear auto regressive with exogenous (ARX) model structure. The result obtained through flower pollination algorithm was then compared with conventional method known as least square (LS) algorithm in terms of mean square error (MSE), correlation test and pole-zero diagram. The best MSE achieved by LS modeling for endpoint acceleration and hub angle positioning are 0.0075 and 0.0028, respectively. While, the best MSE produced by flower pollination algorithm for endpoint acceleration and hub angle positioning are 0.0063 and 0.0020, respectively. It is reveals that the performance of intelligence algorithm is superior than conventional algorithm

A Single Objective Flower Pollination Algorithm for Modeling the Horizontal Flexible Plate System

Flexible plate structure is a chosen technology used for many applications since past decades ago. However, this structure has a disadvantage that needs to be avoided which is easy to vibrate. Thus, this project presents the modelling of horizontal flexible plate system using bio-inspired flower pollination algorithm. The objective is to obtain an accurate model of the real system in the simulated environment. The collected of real vibration data through experimental study was then utilized to develop the dynamic system model based on linear autoregressive with exogenous (ARX) model structure and optimized by flower pollination algorithm (FPA). The algorithm is a novel bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. The gained model in this simulation is approved utilizing the most minimal mean squared error, correlation tests, and pole zero graph stability due to check the robustness of the model. The performance of the developed model was then compared with the conventional algorithm known as recursive least square (RLS). The best model achieved in this study will be used as a platform of controller development using active vibration control technique

Intelligent PID Controller Tuned by Bacterial Foraging Optimization Algorithm for Vibration Suppression of Horizontal Flexible Structure

Flexible structure offers various advantages such as being lightweight, efficient, quick system response and low energy consumption. However, this structure produces too much vibration which leads to system failure. To overcome this drawback, this project developed an intelligent vibration controller based on Bacterial Foraging Optimization (BFO) and incorporated into a Proportional-Integral-Derivative (PID) controller. BFO is a metaheuristic approach categorized as an evolutionary algorithm recently developed, and a nature-inspired optimization algorithm. BFO has been successfully applied to solve some engineering problems due to its simplicity and ease of implementation. Therefore, by introducing BFO to the PID controller, the desired parameters to control vibration experienced by a horizontal flexible plate can be easily found. The performance of the intelligent PID controller was compared to the conventional tuning method known as Ziegler-Nichols (ZN). It was noticed that the PID controller tuned by BFO successfully outperformed the PID controller tuned by ZN by achieving a high attenuation at the first mode of vibration of 44.65 dB as compared to the latter which was only attenuated at the first mode of vibration at 12.8 dB. The developed PID controller was also able to maintain a good performance level even when this system was introduced to multiple sinusoidal disturbance

Fuzzy-Pid Based Controler For Active Vibration Control Of Nonlinear Dynamic Systems

The light weight characteristic offered by flexible structures can be easily influenced to the excessive vibration and it also brings several problems including instability, fatigue, bending and low performance. Therefore, it is compulsory to suppress the undesired vibration of flexible structures due to sustain its performance. This paper presents the development of hybrid controller known as fuzzy-PID based controller for vibration suppression of the horizontal flexible plate structure. Initially, the experimental rig was designed and integrated with the instrumentation system for vibration data collection purpose. The vibration data obtained experimentally was used to model the dynamic system based on auto-regressive with exogenous input structure using evolutionary swarm algorithm. The model obtained in simulation environment was then used for the development of PID-Fuzzy based controller. The performance of proposed controller was validated by exerting two types of disturbances to the system for robustness verification. It was indicated that PID-fuzzy controller was achieved higher attenuation value at the first mode of vibration by achieving 32.14 dB attenuation in the system. The attenuation value has been reduced from 103.5 dB to 71.36 dB, equivalent to 31.05 % attenuation, after the introduction of vibration control. The mean squared error achieved by the controller is 0.0237, compared with 0.6655 before the activation of controlle

Hub Angle Control for A Single Link Flexible Manipulator Based on Cuckoo Search Algorithm

Flexible manipulators are one of the promising devices that can be applied in many fields especially in automation and manufacturing fields as they are designed to reduce energy consumption and increase the speed of operation. However, agitation process experienced in the complex structure of the system which causes unwanted vibration will affect the precision of operation. Thus, an efficient control system is required to make them functional. Therefore, the development of an accurate model of flexible manipulator was presented prior to establishing active vibration control to suppress the vibration and increase efficiency of the system. This paper presents the development of a Proportional-IntegralDerivative controller based on cuckoo search algorithm for a single link flexible manipulator system. Initially, the system was modelled using input and output experimental data of the hub angle. System identification was implemented via swarm intelligence algorithm known as cuckoo search algorithms based on auto regressive with exogenous model structure. Then, the performance of proposed algorithms was validated based on three robustness methods known as mean squared error, pole zero diagram stability and correlation tests. The simulation results showed superior performance of cuckoo search algorithm by achieving lowest mean squared error, good correlation tests and high root locus stability. Then, the cuckoo search model was implemented in the proposed control scheme with the aim of accurate positioning at the end point of flexible manipulator

Self-tuning active vibration controller using particle swarm optimization for flexible manipulator system

This paper presents the development of an optimal self-tuning PID controller for vibration suppression of flexible manipulator structures using particle swarm optimization (PSO). A simulation environment characterizing the dynamic behavior of the flexible manipulator system was first developed using finite difference (FD) approach to acquire the input-output data of the system. The global search technique of PSO is used to estimate the model transfer function through parametric identification in comparison to the conventional recursive least square (RLS) method. Next, the control structure comprises conventional PID controller and an intelligent PID incorporated PSO controller for position and vibration control respectively acted on flexible manipulator model system. Behavior of system response including hub angle and end-point displacement are recorded and assessed. The validation of the algorithm is presented in time and frequency domains. It was demonstrated that proposed controller is effective to move the flexible link to the desired position with suppression of vibration at end-point of a flexible manipulator structure