Hazriq Izzuan Jaafar

PSO-tuned PID controller for a nonlinear gantry crane system

This paper presents development of an optimal PID controller for control of a nonlinear gantry crane system. An improved PSO algorithm based on a priority-based fitness approach is implemented for finding optimal PID parameters. The system dynamic model is derived using Lagrange equation. A combination of PID and PD controllers are utilized for position and oscillation control of the system. System responses including trolley displacement and payload oscillation are observed and analyzed. Simulation is conducted within Matlab environment to verify the performance of the controller. It is demonstrated that the controller is effective to move the trolley as fast as possible to the desired position with low payload oscillation technique.

Optimal Performance of a Nonlinear Gantry Crane System via Priority-based Fitness Scheme in Binary PSO Algorithm

This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position.

Optimal PID controller parameters for nonlinear gantry crane system via MOPSO technique

This paper presents development of an optimal PID controller for controlling the nonlinear gantry crane system. The PSO with linear weight summation approach is implemented for finding optimal PID parameters. The effectiveness of variation weight summation is observed in order to find the optimal performances of system. The system dynamic model is derived using Lagrange equation. A combination of five parameters (PID and PD) controllers are utilized for positioning and oscillation control of the system. System responses including trolley displacement and payload oscillation are observed and analyzed. Simulation is conducted within Matlab environment to verify the performance of the controller. It is demonstrated that implementation of linear weight summation approach in controlling system is useful in order to find the required performances according to the needs and circumstances.

Comparison of LQR and PID controller tuning using PSO for Coupled Tank System

Coupled Tank System is one of the widely used applications in industries. Like other process control, it require suitable controller to obtain the good system performances. Hence, this paper presents the study of Coupled Tank System using LQR and PID controller. Both controller parameters are tuned using Single-Objective Particle Swarm Optimization (PSO). The performance of the system is compared based on the transient response in term of of Rise Time (Tr), Settling Time (Ts), Steady State Error (ess) and Overshoot (OS). Simulation is conducted within MATLAB environment to verify the performances of the system. The result shows that both controller can be tuned using PSO, while LQR controller give slightly better results compared to PID controller.

Modeling and Simulation of Swarm Intelligence Algorithms for Parameters Tuning of PID Controller in Industrial Couple Tank System

Industrial tank system is widely used in consumer liquid processing and chemical processing industry. In liquid-based product manufacturing system, one of the main components consists of an industrial tank. This paper explores the applications of two swarm intelligence algorithms in optimizing the PID controller parameters. These swarm intelligence algorithms are Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). Each agent of the swarm intelligence will represent a possible solution of the problem where each dimension corresponds to the PID controllers parameters. Result obtained shows that there are potential in improving these algorithms to replace the conventional way of obtaining PID controllers parameters

Optimal tuning of PID+PD controller by PFS for Gantry Crane System

This paper presents a combination of the Priority Fitness Scheme (PFS) and optimization techniques to find the optimal performances of the Gantry Crane System. Control algorithm that based on the PFS will be designed and used as fitness function in PSO and BPSO. PFS are prioritized based on Steady State Error (SSE), Overshoot (OS) and Settling Time (Ts) according to the requirements. The system model is derived using the Lagrange equation and PID+PD controller is developed for positioning and oscillation control. System responses, including trolley displacement and payload oscillation are observed and analyzed. It is expected the optimal load transfers offer significant time saving with limited swing and smooth motions via PFPSO and PFBPSO.

A DNR and DG Sizing Simultaneously by Using EPSO

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an efficient hybridization of both Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) methods which is called the Evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration.

System identification of electro-hydraulic actuator system with pressure and load effects

Electro-hydraulic actuator (EHA) system has become one of the most prominent actuator in the past decades. Therefore, it is crucial to attain structural insight into the characteristics of the particular system. This paper presents system identification of EHA system using non-recursive or offline technique where the mathematical model is determined first by considering fixed supply pressure and load. Then, load variation is demonstrated to evaluate the parameter changes. Lastly, supply pressure is changed in order to show the significant effect in the model parameters. The finding shows that the load variation produces insignificant effect in the model parameters due to the limitations of the developed EHA system workbench. However, the supply pressure shows significant effect in the plant model parameters. As conclusion, the robustness and adaptability assessment in the controller design might be appropriately observed in future research works by using the supply variation for the existing EHA system workbench.

Cuckoo Search Approach for Cutting Stock Problem

Cutting Stock Problem has been used in many industries like paper, glass, wood and etc. Cutting Stock Problem has helped industries to reduce trim loss and at the same time meets the customer’s requirement. The purpose of this paper is to develop a new approach which is Cuckoo Search Algorithm in Cutting Stock Problem. Cutting Stock Problem with Linear Programming based method has been improved down the years to the point that it reaches limitation that it cannot achieve a reasonable time in searching for solution. Therefore, many researchers have to turn to metaheuristic algorithms as a solution to the problem which also makes these algorithms become famous. Cuckoo Search Algorithm is selected because it is a new algorithm and outperforms many algorithms. Hence, this paper intends to experiment the performance of Cuckoo Search in Cutting Stock Problem.

Analysis of movement for unmanned underwater vehicle using a low cost integrated sensor

This paper presents the development of low cost integrated Smart Sensor for Unmanned Underwater Vehicle (UUV) namely as underwater Remotely Operated Vehicle (ROV). In the underwater industries, the most crucial issues are the sensors that are needed for the underwater task. The sensors that are utilized in this area are quite expensive and sensitive. Every sensors used in the underwater vehicle are not in the form of integrated sensors and most of them based on case to case basis. However, nowadays, a lot of industries are involved in the development of the integrated sensor in order to reduce the production cost as well as to increase accuracies, efficiencies and productivities. Therefore, this research proposes an integrated sensor to be applied in the underwater operations. The integrated sensor is designed based on three goal performances which are; the accuracies; the sensitivities and the cost efficiencies. This integrated sensor is the combination of pressure sensor, inertial measurement unit (IMU), digital compass and temperature sensor that are placed in a waterproof casing. This integrated sensor is targeted to be used to control the movement of ROV to maintain its position called station keeping. The purpose of the station keeping is to ensure the ROV to remain stationary at the desired depth by utilizing the pressure sensor. The experimental studies have been carried out in order to see the responses of each sensor.