A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.
Computer network routing is performed based on routing protocol decisions. Open Shortest Path First OSPF is the most known routing protocol. It suffers from congestion problem since it generally uses single (least cost) path to deliver information. Some times OSPF delivers information using more than one path in the case of more than one path have the same cost value. This condition is rarely achieved in normal cases. In this work OSPF is developed to distribute information load across multiple paths and makes load distribution as general case for the routing protocol. The modification supposes no protocol replacement and uses the existing protocol facilities. This makes faster information delivery, load balancing, less congestion, and with little modification on the built in OSPF functions. Disjoint paths are calculated then the costs of the best set of them are adapted using approporate ratio.
This paper concerns with exploitation of renewable energy sources for meeting energy requirements of remote locations. It presents an investigation which is based on a practical project that was executed in collaboration between academia and industry. It involves design and installation of a prototype integrated renewable energy system which consists of two 15 kW wind turbines, electrolyser, fuel cell system (FCS) and the associated control equipment. It was installed at the furthest island of Shetland, North of Scotland, U.K. The philosophy used in designing this system is summarised as follows: During times of high wind, the electricity generated by wind turbines is normally greater than that required by site electrical load. The excessive amount of generated electricity is stored into Hydrogen by utilising an electrolyser which is then used to generate the deficient electric power by the FCS at times of low wind.
It's not easy to implement the mixed / optimal controller for high order system, since in the conventional mixed / optimal feedback the order of the controller is much than that of the plant. This difficulty had been solved by using the structured specified PID controller. The merit of PID controllers comes from its simple structure, and can meets the industry processes. Also it have some kind of robustness. Even that it's hard to PID to cope the complex control problems such as the uncertainty and the disturbance effects. The present ideas suggests combining some of model control theories with the PID controller to achieve the complicated control problems. One of these ideas is presented in this paper by tuning the PID parameters to achieve the mixed / optimal performance by using Intelligent Genetic Algorithm (IGA). A simple modification is added to IGA in this paper to speed up the optimization search process. Two MIMO example are used during investigation in this paper. Each one of them has different control problem.
This paper examines the use of non-integer switching frequency ratios in digitally controlled DC-DC converters. In particular the execution of multiple control algorithms using a Digital Signal Processor (DSP) for this application is analyzed. The variation in delay from when the Analog to Digital Converter (ADC) samples the output voltage to when the duty cycle is updated is identified as a critical factor to be considered when implementing the digital control system. Fixing the delay to its maximum value is found to produce reasonable performance using a conventional DSP. A modification of the DSP’s interrupt control logic is proposed here that minimizes the delay and thereby yields improved performance compared with that given by a standard interrupt controller. Applying this technique to a multi-rail power supply system provides the designer with the flexibility to choose arbitrary switching frequencies for individual converters, thereby allowing optimization of the efficiency and performance of the individual converters.
Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complex- valued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.
Address Resolution Protocol (ARP) is used to resolve a host’s MAC address, given its IP address. ARP is stateless, as there is no authentication when exchanging a MAC address between the hosts. Hacking tactics using ARP spoofing are constantly being abused differently; many previous studies have prevented such attacks. However, prevention requires modification of the underlying network protocol or additional expensive equipment, so applying these methods to the existing network can be challenging. In this paper, we examine the limitations of previous research in preventing ARP spoofing. In addition, we propose a defence mechanism that does not require network protocol changes or expensive equipment. Before sending or receiving a packet to or from any device on the network, our method checks the MAC and IP addresses to ensure they are correct. It protects users from ARP spoofing. The findings demonstrate that the proposed method is secure, efficient, and very efficient against various threat scenarios. It also makes authentication safe and easy and ensures data and users’ privacy, integrity, and anonymity through strong encryption techniques.