Intelligent controllers for vechicle suspension system using magnetorheological damper

Semi-active suspension control with magnetorheological (MR) damper is one of the most fascinating systems being studied in improving the vehicle ride comfort. This study aims to investigate the development of intelligent controllers for vehicle suspension system using MR damper, namely, the proportional-integral-derivative (PID) and fuzzy logic (FL) controllers optimized using particle swarm optimization (PSO), firefly algorithm (FA) and advanced firefly algorithm (AFA). Since the conventional optimization method always has a problem in identifying the optimum values and it is time consuming, the evolutionary algorithm is the best approach in replacing the conventional method as it is very efficient and consistent in exploring the values for every single space. The PSO and FA are among of the evolutionary algorithms which have been studied in this research. Nevertheless, the weakness of FA such as getting trapped into several local minima is an attractive area that has been focused more as a possible improvement during the evolutionary process. Thus, a new algorithm based on the improvement of the original FA was introduced to improve the solution quality of the FA. This algorithm is called advanced firefly algorithm. A parametric modelling technique known as Spencer model was proposed and employed to compute the dynamic behaviour of the MR damper system. The Spencer model was experimentally validated and conducted to capture the behaviour of the Lord RD-1005-3 MR damper with the same excitation input. A simulation of a semi-active suspension system was developed within MATLAB Simulink environment. The effectiveness of all control schemes were investigated in two major issues, namely the ability of the controller to reject the unwanted motion of the vehicle and to overcome the damping constraints. The result indicates that, the PID-AFA control scheme is more superior as compared to the PID-PSO, PID-FA, FL-PSO, FL-FA, FL-AFA and passive system with up to 27.1% and 19.1% reduction for sprung mass acceleration and sprung mass displacement, respectively. Finally, the performance of the proposed intelligent control schemes which are implemented experimentally on the developed quarter vehicle suspension test rig shows a good agreement with the results of the simulation study. The proposed control scheme of PID-AFA has reduced the sprung mass acceleration and sprung mass displacement over the FL-AFA and passive system up to 28.21% and 16.9%, respectively

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

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

Semi-active suspension system using MR damper with PSO skyhook and sensitivity analysis of PID controller

This paper introduces the use of Particle Swarm Optimization (PSO) algorithm to tune Skyhook controller & Sensitivity Analysis method to tune PID controller for semi-active suspension system in furtherance of increasing and enhancing the ride comfort and vehicle stability. The performance of skyhook and PID controller are optimized by PSO and Sensitivity Analysis respectively. The mean square error (MSE) of the system is set as an objective function for optimization process of the proposed controller. The performances of proposed controllers are compared with the passive system in terms of sprung displacement & sprung acceleration. The bump & hole and random road profile is set as a disturbance of the system. The simulated results reflect that the proposed controllers offer a significant improvement in ride comfort and vehicle stability

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

Performance evaluation of modified-hybrid radio tomographic imaging for human localization in outdoor environment

The accuracy is the main factor in developing the localization system. However, due to the environmental noise and interference, the accuracy of localization has been affected. Since Device-free Localization has a multipath problem, thus Radio Frequency Tomography (RTI) has been introduced. This approach is used to localize the human position. This approach offers great potential in monitoring activities especially in perimeter surveillance application. Conventionally, RTI uses Linear Back Projection algorithm (LBP) to reconstruct the tomographic image. However, this algorithm suffers with the ill-posed problem caused by back-projection and the smearing effect due to the overlapping image. This leads to a low-quality tomographic image projection. To improve the quality tomographic image, several regularization approaches has been introduced by other researchers. However, because the target occupies only a small amount of space compared to the entire area monitored, the resulting image is blurred with noise. Therefore, this paper proposed a Modified Hybrid Radio Tomographic Imaging (HRTI-M). Through this proposed method, the area smeared on the RTI image has been reduced. Moreover, the average error of the reconstruction area has been able to be reduced from more than 3% to less than 1%

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

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

Vibration suppression of the horizontal flexible plate using proportional– integral–derivative controller tuned by particle swarm optimization

This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation