Showing papers in "International Journal of Control Automation and Systems in 2006"

Design of Fractional Order Controller Based on Particle Swarm Optimization

Abstract: An intelligent optimization method for designing Fractional Order PID (FOPID) controllers based on Particle Swarm Optimization (PSO) is presented in this paper. Fractional calculus can provide novel and higher performance extension for FOPID controllers. However, the difficulties of designing FOPlD controllers increase, because FOPID controllers append derivative order and integral order in comparison with traditional PID controllers. To design the parameters of FOPID controllers, the enhanced PSO algorithms is adopted, which guarantee the particle position inside the defined search spaces with momentum factor. The optimization performance target is the weighted combination of ITAE and control input. The numerical realization of FOPlD controllers uses the methods of Tustin operator and continued fraction expansion. Experimental results show the proposed design method can design effectively the parameters of FOPID controllers.

Performance Improvement of IPMC (Ionic Polymer Metal Composites) for a Flapping Actuator

Abstract: In this paper, a trade-off design and fabrication of IPMC (Ionic Polymer Metal Composites) as an actuator for a flapping device have been described. Experiments for the internal solvent loss of IPMCs have been conducted for various combinations of cation and solvent in order to find out the best combination of cation and solvent for minimal solvent loss and higher actuation force. From the experiments, it was found that IPMCs with heavy water as their solvent could operate longer. Relations between length/thickness and tip force of IPMCs were also quantitatively identified for the actuator design from the tip force measurement of 200, 400, 640, and 800µm thick IPMCs. All IPMCs thicker than 200µm were processed by casting Nafion™ solution. The shorter and thicker IPMCs tended to generate higher actuation force but lower actuation displacement. To improve surface conductivity and to minimize solvent evaporation due to electrically heated electrodes, gold was sputtered on both surfaces of the cast IPMCs by the Physical Vapor Deposition (PVD) process. For amplification of a short IPMC's small actuation displacement to a large flapping motion, a rack-and-pinion type hinge was used in the flapping device. An insect wing was attached to the IPMC flapping mechanism for its flapping test. In this test, the wing flapping device using the 800µm thick IPMC could create around 10°~85° flapping angles and 0.5~15Hz flapping frequencies by applying 3~4V.

Position Control of Shape Memory Alloy Actuators Using Self Tuning Fuzzy PID Controller

Abstract: Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

Lever Arm Compensation for GPS/INS/Odometer Integrated System

Abstract: For more accurate navigation, lever arm compensation is considered. The compensation method for GPS and an odometer is introduced and new compensation methods are proposed for an odometer to consider the effect of coordinate transformation errors and the scale factor error. The methods are applied to a GPS/INS/odometer integrated system and the simulation and experimental results show its effectiveness. Navigation is defined as all the related theories and technologies for obtaining position, velocity and attitude of a vehicle. With the use of navigation technology, one can know his/her position and can plan trajectory for their destination. Nowadays, especially, the need for the navigation of land vehicles has rapidly increased. In the near future, ubiquitous personal navigation will be spread and navigation will become a more essential technology. The Global Positioning System (GPS) is a satellite- based radio navigation system (1). It allows a user with a receiver to obtain accurate position information anywhere on the globe. It provides position information whose errors would not increase with respect to time. However, a signal from the GPS satellite cannot often arrive at a receiver in an urban area. In those cases, it cannot provide any position information.

A Biologically Inspired Intelligent PID Controller Tuning for AVR Systems

Abstract: This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.

Design of Optimal Fuzzy Logic based PI Controller using Multiple Tabu Search Algorithm for Load Frequency Control

Abstract: This paper focuses on a new optimization technique of a fuzzy logic based proportional integral (FLPI) load frequency controller by the multiple tabu search (MTS) algorithm. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and etTor method or experiences of designers. To overcome this problem, the MTS algorithm is proposed to simultaneously tune proportional integral gains, the membership functions and control rules of a FLPT load frequency controller in order to minimize the frequency deviations of the interconnected power system against load disturbances. The MTS algorithm introduces additional techniques for improvement of the search process such as initialization, adaptive search, multiple searches, crossover and restart process. Simulation results explicitly show that the performance of the proposed FLPI controller is superior to conventional PI and FLPI controllers in terms of overshoot and settling time. Furthermore, the robustness of the proposed FLPI controller under variation of system parameters and load change are higher than that of conventional PI and FLPI controllers.

Global Minimum-Jerk Trajectory Planning of Space Manipulator

Abstract: A novel approach based on genetic algorithms (GA) is developed to find a global minimum-jerk trajectory of a space robotic manipulator in joint space. The jerk, the third derivative of position of desired joint trajectory, adversely affects the efficiency of the control algorithms and stabilization of whole space robot system and therefore should be minimized. On the other hand, the importance of minimizing the jerk is to reduce the vibrations of manipulator. In this formulation, a global genetic-approach determines the trajectory by minimizing the maximum jerk in joint space. The planning procedure is performed with respect to all constraints, such as joint angle constraints, joint velocity constraints, joint angular acceleration and torque constraints, and so on. We use an genetic algorithm to search the optimal joint inter-knot parameters in order to realize the minimum jerk. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation result shows that GA-based minimum-jerk trajectory planning method has satisfactory performance and real significance in engineering.

Internal Fault Classification in Transformer Windings using Combination of Discrete Wavelet Transforms and Back-propagation Neural Networks

Abstract: This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modem differential relay for a transformer protection scheme.

Smooth Formation Navigation of Multiple Mobile Robots for Avoiding Moving Obstacles

Abstract: This paper addresses a formation navigation issue for a group of mobile robots passing through an environment with either static or moving obstacles meanwhile keeping a fixed formation shape. Based on Lyapunov function and graph theory, a NN formation control is proposed, which guarantees to maintain a formation if the formation pattern is , k C 1. k ≥ In the process of navigation, the leader can generate a proper trajectory to lead formation and avoid moving obstacles according to the obtained information. An evolutionary computational technique using particle swarm optimization (PSO) is proposed for motion planning so that the formation is kept as 1 C function. The simulation results demonstrate that this algorithm is effective and the experimental studies validate the formation ability of the multiple mobile robots system.

Direct and Indirect Robust Adaptive Fuzzy Controllers for a Class of Nonlinear Systems

Abstract: In this paper, we propose direct and indirect adaptive fuzzy sliding mode control approaches for a class of nonaffine nonlinear systems. In the direct case, we use the implicit function theory to prove the existence of an ideal implicit feedback linearization controller, and hence approximate it to attain the desired performances. In the indirect case, we exploit the linear structure of a Takagi-Sugeno fuzzy system with constant conclusion to establish an affine-in-control model, and therefore design an indirect adaptive fuzzy controller. In both cases, the adaptation laws of the adjustable parameters are deduced from the stability analysis, in the sense of Lyapunov, to get a more accurate approximation level. In addition to their robustness, the design of the proposed approaches does not require the upper bounds of both external disturbances and approximation errors. To show the efficiency of the proposed controllers, a simulation example is presented.

Optimal Vibration Control of Vehicle Engine-Body System using Haar Functions

Abstract: In this note a method of designing optimal vibration control based on Haar functions to control of bounce and pitch vibrations in engine-body vibration structure is presented. Utilizing properties of Haar functions, a computational method to find optimal vibration control for the engine-body system is developed. It is shown that the optimal state trajectories and optimal vibration control are calculated approximately by solving only algebraic equations instead of solving the Riccati differential equation. Simulation results are included to demonstrate the validity and applicability of the technique.

Dynamic Modeling and Adaptive Neural-Fuzzy Control for Nonholonomic Mobile Manipulators Moving on a Slope

Abstract: This paper addresses dynamic modeling and task-space trajectory following issues for nonholonomic mobile manipulators moving on a slope. An integrated dynamic modeling method is proposed considering nonholonomic constraints and interactive motions. An adaptive neural-fuzzy controller is presented for end-effector trajectory following, which does not rely on precise apriori knowledge of dynamic parameters and can suppress bounded external disturbances. Effectiveness of the proposed algorithm is verified through simulations.

Reduction of Fuzzy Rules and Membership Functions and Its Application to Fuzzy PI and PD Type Controllers

Abstract: Fuzzy controller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach, namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute en-or (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.

Feeder Pipe Inspection Robot with an Inch-Worm Mechanism Using Pneumatic Actuators

Abstract: The outlet feeder pipe thinning in a PHWR (Pressurized Heavy Water Reactor) is caused by a high pressure steam flow inside the pipe, which is a well known degradation mechanism called a FAC (Flow Assisted Corrosion). In order to monitor the degradation, the thickness of the outlet bends close to the exit of the pressure tube should be measured and analyzed at every official overhaul. This paper describes a mobile feeder pipe inspection robot that can minimize the irradiation dose to human workers by automating the measurement process. The robot can move by itself on the feeder pipe by using an inch worm mechanism, which is constructed by two gripper bodies that can fix the robot body on to the pipe, one extendable and contractible actuator, and a rotation actuator connected to the two gripper bodies to move forward and backward, and to rotate in a circumferential direction.

Improved Direct Torque Control for Sensorless Matrix Converter Drives with Constant Switching Frequency and Torque Ripple Reduction

Abstract: In this paper, an improved direct torque control CDTC) method for sensorless matrix converter drives is proposed which enables to minimize torque ripple, to obtain unity input power factor, and to achieve good sensorless speed-control performance in the low speed operation, while maintaining constant switching frequency and fast torque dynamics. It is possible to combine the advantages of matrix converters with the advantages of the DTC strategy using space vector modulation and a flux deadbeat controller. To overcome the phase current distortion by the non-linearity of a matrix converter drive, the simple non-linearity compensation method using PQR power theory are presented in the proposed scheme. Experimental results are shown to illustrate the feasibility of the proposed strategy.

An Image-Guided Robotic Surgery System for Spinal Fusion

Abstract: The goal of this work is to develop and test a robot-assisted surgery system for spinal fusion. The system is composed of a robot, a surgical planning system, and a navigation system. It plays the role of assisting surgeons for inserting a pedicle screw in the spinal fusion procedure. Compared to conventional methods for spinal fusion, the proposed surgical procedure ensures minimum invasion and better accuracy by using robot and image information. The robot plays the role of positioning and guiding needles, drills, and other surgical instruments or conducts automatic boring and screwing. Pre-operative CT images intra-operative fluoroscopic images are integrated to provide the surgeon with information for surgical planning. Some experiments employing the developed robotic surgery system are conducted. The experimental results confirm that the system is not only able to guide the surgical tools by accurately pointing and orienting the specified location, but also successfully compensate the movement of the patient due to respiration.

Attitude Dynamics Identification of Unmanned Aircraft Vehicle

Abstract: The role of Unmanned Aircraft Vehicles (UAVs) has been increasing significantly in both military and civilian operations. Many complex systems, such as UAVs, are difficult to model accurately because they exhibit nonlinearity and show variations with time. Therefore, the control system must address the issues of uncertainty, nonlinearity, and complexity. Hence, identification of the mathematical model is an important process in controller design. In this paper, attitude dynamics identification of UAV is investigated. Using the flight data, nonlinear state space model for attitude dynamics of UAV is derived and verified. Real time simulation results show that the model dynamics match experimental data.

H 2 -optimal Control with Regional Pole Assignment via State Feedback

Abstract: The design of H2-optimal control with regional pole assignment via state feedback in linear time-invariant systems is investigated. The aim is to find a state feedback controller such that the closed-loop system has the desired eigenvalues lying in some desired stable regions and attenuates the disturbance between the output vector and the disturbance vector. Based on a proposed result of parametric eigenstructure assignment via state feedback in linear systems, the considered H2-optimal control problem is changed into a minimization problem with certain constraints, and a simple and effective algorithm is proposed for this considered problem. A numerical example and its simulation results show the simplicity and effectiveness of this proposed algorithm.

Real-Time Object Tracking and Segmentation Using Adaptive Color Snake Model

Abstract: Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. In this paper, the development of new snake model called "adaptive color snake model (ACSM)" for segmentation and tracking is introduced. The simple operation makes the algorithm runs in real-time. For robust tracking, the condensation algorithm was adopted to control the parameters of ACSM. The effectiveness of the ACSM is verified by appropriate simulations and experiments.

A Safety Assessment Methodology for a Digital Reactor Protection System

Abstract: The main function of a reactor protection system is to maintain the reactor core integrity and the reactor coolant system pressure boundary. Generally, the reactor protection system adopts the 2-out-of-m redundant architecture to assure a reliable operation. This paper describes the safety assessment of a digital reactor protection system using the fault tree analysis technique. The fault tree technique can be expressed in terms of combinations of the basic event failures such as the random hardware failures, common cause failures, operator errors, and the fault tolerance mechanisms implemented in the reactor protection system. In this paper, a prediction method of the hardware failure rate is suggested for a digital reactor protection system, and applied to the reactor protection system being developed in Korea to identify design weak points from a safety point of view.

A Three-Degree-of-Freedom Anthropomorphic Oculomotor Simulator

Abstract: For a sophisticated humanoid that explores and learns its environment and interacts with humans, anthropomorphic physical behavior is much desired. The human vision system orients each eye with three-degree-of-freedom (3-DOF) in the directions of horizontal, vertical and torsional axes. Thus, in order to accurately replicate human vision system, it is imperative to have a simulator with 3-DOF end-effector. We present a 3-DOF anthropomorphic oculomotor system that reproduces realistic human eye movements for human-sized humanoid applications. The parallel link architecture of the oculomotor system is sized and designed to match the performance capabilities of the human vision. In this paper, a biologically-inspired mechanical design and the structural kinematics of the prototype are described in detail. The motility of the prototype in each axis of rotation was replicated through computer simulation, while performance tests comparable to human eye movements were recorded.

Position Control of Mobile Robot for Human-Following in Intelligent Space with Distributed Sensors

Abstract: Latest advances in hardware technology and state of the art of mobile robot and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. And mobile service robot requires the perception of its present position to coexist with humans and support humans effectively in populated environments. To realize these abilities, robot needs to keep track of relevant changes in the environment. This paper proposes a localization of mobile robot using the images by distributed intelligent networked devices (DINDs) in intelligent space (IS pace) is used in order to achieve these goals. This scheme combines data from the observed position using dead-reckoning sensors and the estimated position using images of moving object, such as those of a walking human, used to determine the moving location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates of a moving object and the estimated position of the robot are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used to estimate the location of moving robot. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in the determining of the location of the mobile robot. Its performance is verified by computer simulation and experiment.

Adaptive Predictive Control using Multiple Models, Switching and Tuning

Abstract: In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.

Adaptive Fuzzy Controller for the Nonlinear System with Unknown Sign of the Input Gain

Abstract: We propose and analyze a robust adaptive fuzzy controller for nonlinear systems without a priori knowledge of the sign of the input gain function. No assumptions are made about the type of nonlinearities of the system, except that such nonlinearities are smooth. The uncertain nonlinearities are captured by the fuzzy systems that have been proven to be universal approximators. The proposed control scheme completely overcomes the singularity problem that occurs in the indirect adaptive feedback linearizing control. Projection in the estimated parameters and switching in the control input are both not required. The stability of the closed- loop system is guaranteed in the Lyapunov viewpoint.

H ∞ Control of 2-D Discrete State Delay Systems

Abstract: This paper is concerned with the H∞ control problem of 2-D discrete state delay systems described by the Roesser model. The condition for the system to have a specified H ∞ performance is derived via the linear matrix inequality (LMI) approach. Furthermore, a design procedure for H ∞ state feedback controllers is given by solving a certain LMI. The design problem of optimal H ∞ controllers is formulated as a convex optimization problem, which can be solved by existing convex optimization techniques. Simulation results are presented to illustrate the effectiveness of the proposed results.

Path Planning of Automated Optical Inspection Machines for PCB Assembly Systems

Abstract: We propose a path planning method to improve the productivity of AOI (automated optical inspection) machines in PCB (printed circuit board) assembly lines. The path-planning problem is the optimization problem of finding inspection clusters and the visiting sequence of cameras to minimize the overall working time. A unified method is newly proposed to determine the inspection clusters and visiting sequence simultaneously. We apply a hybrid genetic algorithm to solve the highly complicated optimization problem. Comparative simulation results are presented to verify the usefulness of the proposed method.

Multirate Digital Control for Fuzzy Systems : LMI-Based Design and Stability Analysis

Abstract: This paper studies an intelligent digital control for nonlinear systems with multirate sampling. It is worth noting that the multirate control design is addressed for a given nonlinear system represented by Takagi-Sugeno (T-S) fuzzy models. The main features of the proposed method are that i) it is provided that the sufficient conditions for stabilization of the discrete-time T-S fuzzy system in the sense of Lyapunov stability criterion, which is can be formulated in the linear matrix inequalities (LMIs); and ii) the stability properties of the trivial solution of the digital control system can be deduced from that of the solution of its discretized versions. An example is provided for showing the feasibility of the proposed method.

A Calibration Method for Six-Accelerometer INS

Abstract: The gyroscope free strap-down INS is composed only of accelerometers. Any gyroscope free INS navigation error is deeply affected by the accuracy of the sensor bias, scale factor, orientation and location error. However these parameters can be found by calibration. There is an important research issue about a multi-position calibration method in this paper. It provides a novel method to find the error parameters for the six-accelerometer INS. A superior simulation is shown that the multi-position calibration can find the specifications of a six- accelerometer INS in laboratory. From these parameters the six-accelerometer INS could apply in realistic navigation.

Adaptive Feedback Linearization Control Based on Airgap Flux Model for Induction Motors

Abstract: This paper presents an adaptive feedback linearization control scheme for induction motors with simultaneous variation of rotor and stator resistances. Two typical modeling techniques, rotor flux model and stator flux model, have been developed and successfully applied to the controller design and adaptive observer design, respectively. By using stator fluxes as states, over-parametrization in adaptive control can be prevented and control strategy can be developed without the need of nonlinear transformation. It also decrease the relative degree for the flux modulus by one, thereby, yielding a simple control algorithm. However, when this method is used for flux observer, it cannot guarantee the convergence offlux. Similarly, the rotor flux model may be appropriate for observers, but it is not so for adaptive controllers. In addition, if these two existing methods are merged into overall adaptive control system, it brings about structural complexies. In this paper, we did not use these two medeling methods, and opted for the airgap flux model which takes on only the positive aspects of the existing rotor flux model and stator flux model and prevents structural complexity from occuring. Through theoretical analysis by using Lyapunov's direct method, simulations, and actual experiments, it is shown that stator and rotor resistances converge to their actual values, flux is well estimated, and torque and flux are controlled independently with the measurements of rotor speed, stator currents, and stator voltages. These results were achieved under the persistent excitation condition, which is shown to hold in the simulation.

Time-optimal Trajectory Planning for a Robot System under Torque and Impulse Constraints

Abstract: In this paper, moving a fragile object from an initial point to a specific location in the minimum time without damage is studied. In order to achieve this goal, initially, the maximum acceleration and velocity ranges are specified. These ranges can be dynamically generate on the planned path by the manipulator. The path can be altered by considering the geometrical constraints. Later, considering the impulsive force constraint on the object, the range of maximum acceleration and velocity are obtained to preserve object safety while the manipulator is carrying it along the curved path. Finally, a time-optimal trajectory is planned within the maximum allowable range of acceleration and velocity. This time-optimal trajectory planning can be applied to real applications and is suitable for both continuous and discrete paths.