Amar Faiz Zainal Abidin

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.

A Particle Swarm Optimization Approach to Robotic Drill Route Optimization

Most of the operational time of a PCB Robotic Drill is spent on moving the drill bit between the holes. This operational time can be kept at a minimal level by optimizing the route taken by the robot. An optimized route translates to a minimal cost of operating the robot. This paper proposes a new model that implements Particle Swarm Optimization (PSO) in order to find optimized routing path when using the PCB Robotic Drill. The main task of the PCB Robotic Drill is to drill holes at Printed Circuit Board (PCB). This PCB Robotic Drill will route the drill site by moving the drill bit along Cartesian axes from it’s initial position. Then, the drill bit will return back to the initial position. The drill route consists of a number of potential locations where the holes are going to be drilled. As the number of holes required increases so thus does the complexity to find the optimized route. The proposed model can be used to solve this complex problem with minimal computational time. The result of a case study indicates that the proposed model is capable to find the shortest path for the robot to complete its task. Thus concluded the proposed model can be implemented in any drill route problems.

A Particle Swarm Optimization Approach for Routing in VLSI

The performance of very large scale integration (VLSI) circuits is depends on the interconnected routing in the circuits. In VLSI routing, wire sizing, buffer sizing, and buffer insertion are techniques to improve power dissipation, area usage, noise, crosstalk, and time delay. Without considering buffer insertion, the shortest path in routing is assumed having the minimum delay and better performance. However, the interconnect delay can be further improved if buffers are inserted at proper locations along the routing path. Hence, this paper proposes a heuristic technique to simultaneously find the optimal routing path and buffer location for minimal interconnect delay in VLSI based on particle swarm optimization (PSO). PSO is a robust stochastic optimization technique based on the movement and information sharing of swarms. In this study, location of doglegs is employed to model the particles that represent the routing solutions in VLSI. The proposed approach has a good potential in VLSI routing and can be further extended in futureTo seek for a hyperchaotic attractor with complex topological attractor structure, a new four-dimensional continuous autonomous hyperchaotic system is proposed. Within a wider region of the variation of the control parameter, this system can generate novel hperchaotic and chaotic attractors along with quasi-periodic and periodic orbits. By employing Lyapunov exponent spectrum, bifurcation diagram, Poincaré mapping and phase portrait, etc., the existence of hyperchaotic behaviors of new system is verified and the dynamical routes from period, quasi-period, chaos and hyperchaos are observed. Furthermore, a practical circuit is designed to realize the system, which the experimental results indicate that new four-dimensional hyperchaotic system is a realizable chaotic system with potential values of engineering applications.

Firefly algorithm for path optimization in PCB holes drilling process

In PCB holes drilling process, the time taken for a task completion heavily rely on the distance travels by the drill bit of the CNC machine. In order to minimize the distance traveled by the drill bit, Firefly Algorithm can be used. The proposed model applies Firefly Algorithm to search for the optimized path in PCB holes drilling process. Each agents position represented a possible path that can be taken by the drill bit. The fitness of the agent is inversely proportional to the distance of the path where the shorter the distance, the better the fitness of an agent. Then, the agents compare their fitness with each other. Agent will try to improve its fitness by moving closer towards other agent with better fitness. The process repeated until maximum iteration achieved. Performance of the proposed model is compared with other literatures using a standard case study.

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.

A Firefly Algorithm approach for routing in VLSI

Many studies had been conducted in improving the performance of large scale integration circuits that heavily depends on the interconnected routing in the circuits. Strategic choice of wire placement and buffer placement for very large scale integration (VLSI) routing can improve time delay of VLSI circuit. This paper explores the use of Firefly Algorithm in VLSI routing. The location of doglegs is employed to model the firefly that represents the routing solution. The proposed approach is then compared with previous literature for benchmarking. The result indicates that it has a good potential in VLSI routing and can be further extended in future.

An improved binary particle swarm optimization algorithm for DNA encoding enhancement

The accuracy of DNA computing highly depends on the DNA strands used in solving complex computations. As such, many approaches are proposed to design DNA oligonucleotides that are stable and unique. In this paper, an improved binary particle swarm optimization (IBPSO) algorithm is proposed and implemented. Four objective functions which are H-measure, similarity, hairpin and continuity are employed to define the uniqueness of designed sequences. The DNA words are constrained within a predefined range of GC-content and melting temperature. The performances and the ability of the algorithm to enhance the characteristics of generated DNA code words are analyzed. The results obtained show that this algorithm executes better sequences and did perform better compared to other optimization techniques. Moreover, it converges faster than the previously suggested binary particle swarm optimization algorithm.

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

Modelling and trajectory following of an armoured vehicle

In this study, a trajectory following strategy consists of two feedback loops is employed on an armoured vehicle to follow a pre-defined trajectory using PID controllers. A 7 Degree-of-Freedoms (DOF) mathematical model is used to model the armoured vehicle which considers full vehicle interactions including tire model, powertrain model, slips and vertical load distribution model. Next, parametric study for the PID parameters was carried out. To tune the PID parameters, two methods are used namely try-and-error based on the parametric study and also using Particle Swarm Optimisation (PSO) algorithm. Lastly, controller responses were evaluated and compared in terms of its performance in following the trajectory. It was proven that PSO algorithm manage to tune and optimise the PID controller parameters for the trajectory following control.

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.