Abstract: This paper summaries basic principles and concepts of
intelligent controls, implemented in humanoid robotics as well as
recent algorithms being devised for advanced control of humanoid
robots. Secondly, this paper presents a new approach neuro-fuzzy
system. We have included some simulating results from our
computational intelligence technique that will be applied to our
humanoid robot. Subsequently, we determine a relationship between
joint trajectories and located forces on robot-s foot through a
proposed neuro-fuzzy technique.
Abstract: In this paper, a worm-like micro robot designed for inpipe
application with intelligent active force control (AFC) capability
is modelled and simulated. The motion of the micro robot is based on
an impact drive mechanism (IDM) that is actuated using piezoelectric
device. The trajectory tracking performance of the modelled micro
robot is initially experimented via a conventional proportionalintegral-
derivative (PID) controller in which the dynamic response of
the robot system subjected to different input excitations is
investigated. Subsequently, a robust intelligent method known as
active force control with fuzzy logic (AFCFL) is later incorporated
into the PID scheme to enhance the system performance by
compensating the unwanted disturbances due to the interaction of the
robot with its environment. Results show that the proposed AFCFL
scheme is far superior than the PID control counterpart in terms of
the system-s tracking capability in the wake of the disturbances.
Abstract: This paper describes a newly designed decentralized
nonlinear control strategy to control a robot manipulator. Based on the
concept of the nonlinear state feedback theory and decentralized
concept is developed to improve the drawbacks in previous works
concerned with complicate intelligent control and low cost effective
sensor. The control methodology is derived in the sense of Lyapunov
theorem so that the stability of the control system is guaranteed. The
decentralized algorithm does not require other joint angle and velocity
information. Individual Joint controller is implemented using a digital
processor with nearly actuator to make it possible to achieve good
dynamics and modular. Computer simulation result has been
conducted to validate the effectiveness of the proposed control scheme
under the occurrence of possible uncertainties and different reference
trajectories. The merit of the proposed control system is indicated in
comparison with a classical control system.
Abstract: This paper focuses on the integration of hybrid renewable energy resources available in remote isolated islands of Sundarban-24 Parganas-South of Eastern part of India to National Grid of conventional power supply to give a Smart-Grid scenario. Before grid-integration, feasibility of optimization of hybrid renewable energy system is monitored through an Intelligent Controller proposed to be installed at Moushuni Island of Sundarban. The objective is to ensure the reliability and efficiency of the system to optimize the utilization of the hybrid renewable energy sources and also a proposition of how theses isolated Hybrid Renewable Energy Systems at remote islands can be grid-connected is analyzed towards vision of green smart-grid.
Abstract: This paper deals with the application of artificial
neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy
Inference System(ANFIS) approach to Load Frequency Control
(LFC) of multi unequal area hydro-thermal interconnected power
system. The proposed ANFIS controller combines the advantages of
fuzzy controller as well as quick response and adaptability nature of
ANN. Area-1 and area-2 consists of thermal reheat power plant
whereas area-3 and area-4 consists of hydro power plant with electric
governor. Performance evaluation is carried out by using intelligent
controller like ANFIS, ANN and Fuzzy controllers and conventional
PI and PID control approaches. To enhance the performance of
intelligent and conventional controller sliding surface is included.
The performances of the controllers are simulated using
MATLAB/SIMULINK package. A comparison of ANFIS, ANN,
Fuzzy, PI and PID based approaches shows the superiority of
proposed ANFIS over ANN & fuzzy, PI and PID controller for 1%
step load variation.
Abstract: The current trend of increasing quality and demands
of the final product is affected by time analysis of the entire
manufacturing process. The primary requirement of manufacturing is
to produce as many products as soon as possible, at the lowest
possible cost, but of course with the highest quality. Such
requirements may be satisfied only if all the elements entering and
affecting the production cycle are in a fully functional condition.
These elements consist of sensory equipment and intelligent control
elements that are essential for building intelligent manufacturing
systems. The intelligent manufacturing paradigm includes a new
approach to production system structure design. Intelligent behaviors
are based on the monitoring of important parameters of system and
its environment. The flexible reaction to changes. The realization and
utilization of this design paradigm as an "intelligent manufacturing
system" enables the flexible system reaction to production
requirement as soon as environmental changes too. Results of these
flexible reactions are a smaller layout space, be decreasing of
production and investment costs and be increasing of productivity.
Intelligent manufacturing system itself should be a system that can
flexibly respond to changes in entering and exiting the process in
interaction with the surroundings.
Abstract: This paper presents an intelligent speed control
system based on fuzzy logic for a voltage source PWM inverter-fed
indirect vector controlled induction motor drive. Traditional indirect
vector control system of induction motor introduces conventional PI
regulator in outer speed loop; it is proved that the low precision of the
speed regulator debases the performance of the whole system. To
overcome this problem, replacement of PI controller by an intelligent
controller based on fuzzy set theory is proposed. The performance of
the intelligent controller has been investigated through digital
simulation using MATLAB-SIMULINK package for different
operating conditions such as sudden change in reference speed and
load torque. The simulation results demonstrate that the performance
of the proposed controller is better than that of the conventional PI
controller.
Abstract: RoboCup Rescue simulation as a large-scale Multi
agent system (MAS) is one of the challenging environments for
keeping coordination between agents to achieve the objectives
despite sensing and communication limitations. The dynamicity of
the environment and intensive dependency between actions of
different kinds of agents make the problem more complex. This point
encouraged us to use learning-based methods to adapt our decision
making to different situations. Our approach is utilizing
reinforcement leaning. Using learning in rescue simulation is one of
the current ways which has been the subject of several researches in
recent years. In this paper we present an innovative learning method
implemented for Police Force (PF) Agent. This method can cope
with the main difficulties that exist in other learning approaches.
Different methods used in the literature have been examined. Their
drawbacks and possible improvements have led us to the method
proposed in this paper which is fast and accurate. The Brain
Emotional Learning Based Intelligent Controller (BELBIC) is our
solution for learning in this environment. BELBIC is a
physiologically motivated approach based on a computational model
of amygdale and limbic system. The paper presents the results
obtained by the proposed approach, showing the power of BELBIC
as a decision making tool in complex and dynamic situation.
Abstract: Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.
Abstract: an intelligent BRT system is necessary when
communities looking for new ways to use high capacity rapid transit
at a reduced cost.This paper will describe the intelligent control
system that works with Datacenter. With the help of GPS system, the
data center can monitor the situation of each bus and bus station.
Through RFID technology, bus station and traffic light can transfer
data with bus and by Wimax communication technology all of parts
can talk together; data center learns all information about the location
of bus, the arrival of bus in each station and the number of passengers
in station and bus.Finally, the paper presents the case study of those
theories in Tehran BRT.
Abstract: This paper proposes an effective algorithm approach to hybrid control systems combining fuzzy logic and conventional control techniques of controlling the speed of induction motor assumed to operate in high-performance drives environment. The introducing of fuzzy logic in the control systems helps to achieve good dynamical response, disturbance rejection and low sensibility to parameter variations and external influences. Some fundamentals of the fuzzy logic control are preliminary illustrated. The developed control algorithm is robust, efficient and simple. It also assures precise trajectory tracking with the prescribed dynamics. Experimental results have shown excellent tracking performance of the proposed control system, and have convincingly demonstrated the validity and the usefulness of the hybrid fuzzy controller in high-performance drives with parameter and load uncertainties. Satisfactory performance was observed for most reference tracks.
Abstract: The need to implement intelligent highways is much
more emphasized with the growth of vehicle production line as well as vehicle intelligence. The control of intelligent vehicles in order to
reduce human error and boost ease congestion is not accomplished solely by the aid of human resources. The present article is an attempt
to introduce an intelligent control system based on a single central computer. In this project, central computer, without utilizing Global
Positioning System (GPS), is capable of tracking all vehicles, crisis management and control, traffic guidance and recording traffic
crimes along the highway. By the help of RFID technology, vehicles
are connected to computerized systems, intelligent light poles and
other available hardware along the way. By the aid of Wimax
communicative technology, all components of the system are
virtually connected together through local and global networks
devised in them and the energy of the network is provided by the
solar cells installed on the intelligent light poles.
Abstract: The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.
Abstract: Programmable logic controllers are the main controllers in the today's industries; they are used for several applications in industrial control systems and there are lots of examples exist from the PLC applications in industries especially in big companies and plants such as refineries, power plants, petrochemical companies, steel companies, and food and production companies. In the PLCs there are some functions in the function library in software that can be used in PLC programs as basic program elements. The aim of this project are introducing and implementing a new function block of a neural network to the function library of PLC. This block can be applied for some control applications or nonlinear functions calculations after it has been trained for these applications. The implemented neural network is a Perceptron neural network with three layers, three input nodes and one output node. The block can be used in manual or automatic mode. In this paper the structure of the implemented function block, the parameters and the training method of the network are presented by considering the especial method of PLC programming and its complexities. Finally the application of the new block is compared with a classic simulated block and the results are presented.
Abstract: Development of intelligent assembly cell conception includes new solution kind of how to create structures of automated and flexible assembly system. The current trend of the final product quality increasing is affected by time analysis of the entire manufacturing process. The primary requirement of manufacturing is to produce as many products as soon as possible, at the lowest possible cost, but of course with the highest quality. Such requirements may be satisfied only if all the elements entering and affecting the production cycle are in a fully functional condition. These elements consist of sensory equipment and intelligent control elements that are essential for building intelligent manufacturing systems. Intelligent behavior of the system as the control system will repose on monitoring of important parameters of the system in the real time. Intelligent manufacturing system itself should be a system that can flexibly respond to changes in entering and exiting the process in interaction with the surroundings.
Abstract: The Combination of path planning and path following is the main purpose of this paper. This paper describes the developed practical approach to motion control of the MRL small size robots. An intelligent controller is applied to control omni-directional robots motion in simulation and real environment respectively. The Brain Emotional Learning Based Intelligent Controller (BELBIC), based on LQR control is adopted for the omni-directional robots. The contribution of BELBIC in improving the control system performance is shown as application of the emotional learning in a real world problem. Optimizing of the control effort can be achieved in this method too. Next the implicit communication method is used to determine the high level strategies and coordination of the robots. Some simple rules besides using the environment as a memory to improve the coordination between agents make the robots' decision making system. With this simple algorithm our team manifests a desirable cooperation.
Abstract: This paper is described one of the intelligent control method in Autonomous systems, which is called fuzzy control to correct the three wheel omnidirectional robot movement while it make mistake to catch the target. Fuzzy logic is especially advantageous for problems that can not be easily represented by mathematical modeling because data is either unavailable, incomplete or the process is too complex. Such systems can be easily up grated by adding new rules to improve performance or add new features. In many cases , fuzzy control can be used to improve existing traditional controller systems by adding an extra layer of intelligence to the current control method. The fuzzy controller designed here is more accurate and flexible than the traditional controllers. The project is done at MRL middle size soccer robot team.
Abstract: Bioprocesses are appreciated as difficult to control because their dynamic behavior is highly nonlinear and time varying, in particular, when they are operating in fed batch mode. The research objective of this study was to develop an appropriate control method for a complex bioprocess and to implement it on a laboratory plant. Hence, an intelligent control structure has been designed in order to produce biomass and to maximize the specific growth rate.
Abstract: Since the driving speed and control accuracy of
commercial optical disk are increasing significantly, it needs an
efficient controller to monitor the track seeking and following
operations of the servo system for achieving the desired data
extracting response. The nonlinear behaviors of the actuator and servo
system of the optical disk drive will influence the laser spot
positioning. Here, the model-free fuzzy control scheme is employed to
design the track seeking servo controller for a d.c. motor driving
optical disk drive system. In addition, the sliding model control
strategy is introduced into the fuzzy control structure to construct a
1-D adaptive fuzzy rule intelligent controller for simplifying the
implementation problem and improving the control performance. The
experimental results show that the steady state error of the track
seeking by using this fuzzy controller can maintain within the track
width (1.6 μm ). It can be used in the track seeking and track
following servo control operations.
Abstract: Road Traffic Accidents are a major cause of disability and death throughout the world. The control of intelligent vehicles in order to reduce human error and boost ease congestion is not accomplished solely by the aid of human resources. The present article is an attempt to introduce an intelligent control system based on RFID technology. By the help of RFID technology, vehicles are connected to computerized systems, intelligent light poles and other available hardware along the way. In this project, intelligent control system is capable of tracking all vehicles, crisis management and control, traffic guidance and recording Driving offences along the highway.