Robust Control Design Based on Differential Evolution for Two-Mass System

This paper presents a robust control design based on constrained optimization using Differential Evolution (DE). The feedback controller is designed based on state space model of the plant considering structured uncertainty such that the closed-loop system would have maximum stability radius. A wedge region is assigned as a constraint for desired closed loop poles location. The proposed control technique is applied to a two-mass system that is known as benchmark problem for robust control design. The simulation results seem to be interesting in which the robustness performance is achieved in the presence of parameter variations of the plant

FORECASTING SAVING DEPOSIT IN MALAYSIAN ISLAMIC BANKING: COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK AND ARIMA

The aim of this paper is to test the ability of artificial neural network (ANN) as an alternative method in time series forecasting and compared to autoregres­sive integrated moving average (ARIMA) in studying saving deposit in Malay­sian Islamic banks. Artificial neural network is getting popular as an alterna­tive method in time series forecasting for its capability to capture vola­tility pattern of non-linear time series data. In addition, the use of an estab­lished tool of analysis such as ARIMA is of importance here for comparative purposes. These two methods are applied to monthly data of the Malaysian Islamic bank­ing deposits from January 1994 to November 2005. The result provides evidence that ANN using “early stopping” approach can be used as an alterna­tive forecasting engine with univariate time series model. It can predict non-lin­ear time series using the pattern of the data directly without any statisti­cal analysis

Fuzzy-tuned PID anti-swing control of automatic gantry crane

Anti-swing control is a well-known term in gantry crane control. It is designed to move the payload of gantry crane as fast as possible while the payload swing angle should be kept as small as possible at the final position. A number of studies have proposed anti-swing control using the well-known proportional, integral, derivative (PID) control method. However, PID controllers cannot always effectively control systems with changing parameters. Some studies have also proposed intelligent-based control including fuzzy control. However, the designers often have to face the problem of tuning many parameters during the design to obtain optimum performance. Thus, a lot of effort has to be taken in the design stage. In this paper Fuzzy-tuned PID controller design for anti-swing gantry crane control is presented. The objective is to design a practical anti-swing control which is simple in the design and also robust. The proposed Fuzzy-tuned PID utilizes fuzzy system as PID gain tuners to achieve robust performance to parameters variations in the gantry crane. A complex dynamic analysis of the system is not needed. PID controller is firstly optimized in MATLAB using a rough model dynamic of the system which is identified by conducting a simple open-loop experiment. Then, the PID gains are used to guide the range of the fuzzy outputs of the Fuzzy-tuned PID controllers. The experimental results show that the proposed anti-swing controller has satisfactory performance. In addition, the proposed method is straightforward in the design

Tuning of PID Controller Using Particle Swarm Optimization (PSO)

The aim of this research is to design a PID Controller using PSO algorithm. The model of a DC motor is used as a plant in this paper. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as genetic algorithm (GA) and particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. However, due to the computational efficiency, only PSO will be used in this paper. The comparison between PSO-based PID (PSO-PID) performance and the ZN-PID is presented. The results show the advantage of the PID tuning using PSO-based optimization approach

A mini review on nanorobots in medical field: Applications and challenges

Technology advancements have expanded our ability to affect the world around us on a progressively smaller scale. Nanotechnology provides substantial advantages over traditional approaches for diagnosis and therapy. Nanorobot development is an intriguing and promising area of nanotechnological research. Nanorobotics is the study of robotics at the nanometer scale, which encompasses nanoscale robots as well as huge robots capable of manipulating things with nanometer resolution in the nanoscale range. Nano-robotic manipulation, with its capacity to position and orient nanometer-scale objects, is a viable technique to build nano-systems, including nanorobots. nanorobotics has provided a ray of hope in various disciplines, particularly in medicine. They are used in medical field for treating cancer, checking blood contents, diagnoses, and accurate drug delivery. This mini literature review focuses on the applications and challenges of nanorobots in the medical field

Robust control design based on differential evolution for two-mass system

This paper presents a robust control design based on constrained optimization using Differential Evolution (DE). The feedback controller is designed based on state space model of the plant considering structured uncertainty such that the closed-loop system would have maximum stability radius. A wedge region is assigned as a constraint for desired closed loop poles location. The proposed control technique is applied to a two-mass system that is known as benchmark problem for robust control design. The simulation results seem to be interesting in which the robustness performance is achieved in the presence of parameter variations of the plant