Intelligent Control for Automation of Yam Storage System Using Fuzzy Logic Controller

This paper presents the development of intelligent control technique for yam storage system based on fuzzy logic controller (FLC). The expert control of yam storage system is formulated in the form of fuzzy rules. The inputs to the controller are the outside and inside temperature, wind speed and presences of rain. The output is the window opening angle. Simulations were performed for different typical levels of input parameters and also for extreme fictitious conditions. The results shown that, the controller is capable of responding to the changes in temperature conditions by adjusting the window opening angle to keep the internal temperature within acceptable range. The controller also satisfies security requirements due to sudden changes in wind velocity and presence of rain

A matlab-based low-cost autopilot for autonomous helicopter development

The challenges associated with the software and hardware integration activities in development of flight autopilot system for autonomous helicopter have called for a change of tactics. The resulting effect is for example, a long time delay in autopilot system design, testing and deployment coupled with the fact that several other autonomous helicopter development tasks depend largely on availability of the autopilot system. Though, the use of off-the-shelf autopilot for a flight control system may ease these challenges, they are generally characterized with limited functionalities, and restrict the user’s design authority. As alternative approach, this paper presents the development of a MATLAB-based autopilot system for autonomous helicopter development. This approach provides an integrated design environment for rapid-prototyping of a low-cost autopilot system. The results of real-time application of the autopilot for flight data logging are presented. The performance shows the effectiveness of the developed autopilot system in small scale autonomous helicopter design and implementation. This is hope to reduce the design cycle time involves in the deployment of small scale autonomous helicopter in various civil low-cost, small payload applications

Artificial intelligent based friction modelling and compensation in motion control system

The interest in the study of friction in control engineering has been driven by the need for 10 precise motion control in most of industrial applications such as machine tools, robot 11 systems, semiconductor manufacturing systems and Mechatronics systems. Friction has 12 been experimentally shown to be a major factor in performance degradation in various 13 control tasks. Among the prominent effects of friction in motion control are: steady state 14 error to a reference command, slow response, periodic process of sticking and sliding (stick-15 slip) motion, as well as periodic oscillations about a reference point known as hunting when 16 an integral control is employed in the control scheme. Table 1 shows the effects and type of 17 friction as highlighted by Armstrong et. al.(1994). It is observed that, each of task is 18 dominated by at least one friction effect ranging from stiction, or/and kinetic to negative 19 friction (Stribeck). Hence, the need for accurate compensation of friction has become 20 important in high precision motion control. Several techniques to alleviate the effects of 21 friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; 22 Tjahjowidodo, 2004; Canudas, et. al., 1986). 23 One of the successful methods is the well-known model-based friction compensation 24 (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, 25 the effect of the friction is cancelled by applying additional control signal which generates a 26 torque/force. The generated torque/force has the same value (or approximately the same) 27 with the friction torque/force but in opposite direction

Matlab-Based Algorithm for Real Time Analysis of Multiexponential Transient Signals

Multiexponential transient signals are particularly important due to their occurrences in many natural phenomena and human applications. For instance, it is important in the study of nuclear magnetic resonance (NMR) in medical diagnosis (Cohn-Sfetcu et al., 1975)), relaxation kinetics of cooperative conformational changes in biopolymers (Provencher, 1976), solving system identification problems in control and communication engineering (Prost and Guotte, 1982), fluorescence decay of proteins (Karrakchou et al., 1992), fluorescence decay analysis (Lakowicz, 1999). Several research work have been reported on the analysis of multicomponent transient signals following the pioneer work of Prony in 1795 (Prony, 1975) and Gardner et al. in 1959 (Gardner, 1979). Detailed review of several techniques for multicomponent transient signals’ analysis was recently reported in (Jibia, 2010)

System identification of parameterized state-space model of a small scale UAV helicopter

The success of model-based ight control law design for autonomous helicopter is largely dependent on the availability of reliable model of the system. Considering the complexity of the helicopter dynamics, and inherent di�culty involves with physical measurement of the system parameters, the grey modeling approach which involves the development of parameterized model from �rst principles and estimation of these parameters using system identi�cation (sysID) technique has been proposed in the literatures. Prediction Error Modeling (PEM) algorithm has been identi�ed as an e�ective system identi�cation technique. However, application of this method to complex system like helicopter is not a trivial exercise due to inherent coupling in the system states and the challenges associated with parameter initialization in PEM algorithm. In this work, an e�ective procedure in application of PEM algorithm available in MATLAB toolbox is presented for small scale helicopter using real-time ight data. The approach was able to yield satisfactory model suitable for model-based ight control design

Control of an inverted pendulum using MODE-based optimized LQR controller

This paper presents an evolutionary optimization based LQR controller design for an inverted pendulum system. The objective is to address the challenges of appropriate design parameters selection in LQR controller while providing optimal performance compromise between the system control objectives with respect to pendulum angle and position response. Hence, a Multiobjective differential evolution algorithm is proposed to design an LQR controller with optimal compromise between the conflicting control objectives. The performance of the MODEbased LQR is benchmarked with an existing controller from the system manufacturer (QANSER). The performance shows the effectiveness of the proposed design algorithm, and in addition provides an efficient solution to conventional trial and error design approach

Robust state feedback control design via PSO-based constrained optimisation

Computational intelligence has been successfully applied to many engineering applications including control engineering problems. In this paper, a robust state feedback control design using particle swarm optimizer based constrained optimization is proposed. The feedback controller is optimized based on state space model of the plant with structured uncertainty such that the closed-loop system would have maximum stability radius. A wedge region is assigned as a constraint to locate the desired closed-loop poles, which correspond to time-domain control system performance. The proposed controller design is applied to anti-swing control for a gantry crane system. The experimental result is shown, and comparison with that of conventional linear quadratic regulator based controller and H∞ loop shaping controller are made. The proposed method achieves a satisfactory robust performance based on the structured singular value analysis and the experimental results