Marwan Bikdash

Feedback and feedforward control law for a ship crane with Maryland rigging system

A state-space model of the shipboard crane based on implicit description of the crane with no simplifying assumptions is developed. By choosing appropriate states, full control authorities for changing the length of the rope is achieved. Three control actions are considered: changing the luffing angle and changing the length of the rope from two points on the boom. A chaotic rolling moment, with a dominant frequency of the same order as the resonance frequency of the shipboard crane, is applied to the ship as an external disturbance. The effect of this disturbance is studied. Feedforward and feedback controllers are then designed and tested for this shipboard crane to suppress the load pendulation caused by ship rolling. The friction in the pulley is assumed negligible. The simulation results based on these controllers show more than 98% decrease in the pendulation magnitude due to control.

Pendulation suppression of a shipboard crane using fuzzy controller

We derive the nonlinear equations of motion for a shipboard crane equipped with the Maryland Rigging. We then develop a state-space model of the crane from an implicit description without simplifying assumptions. A chaotic rolling moment with a dominant frequency of the same order as the resonance frequency of the shipboard crane is applied as an external disturbance. The effect of the disturbance is studied. A fuzzy controller is then designed and tested for this shipboard crane. In this fuzzy controller the change in the length of the rope is the control action, while the friction in the pulley is assumed negligible. The results for this controller show a big decrease in the pendulation magnitude as compared to the cases with no control.

Genetic algorithms solution for unconstrained optimal crane control

Crane control is a difficult problem for conventional control methods because of the highly nonlinear equations that must be satisfied. Usually the necessary conditions for solving an optimal control problem require finding the initial co-state vector. In this paper real-coded genetic algorithms are used to find the desired initial value of the costates of the system with no constraints. In our genetic representation, each chromosome represents a set of co-states and each gene (co-state) has an associated cost based on its ability to move the system to desired state after a given amount of time. The objective is to evolve a minimum cost co-state. Our results for this unconstrained crane problem are quite encouraging.

Modeling and optimal control design of shipboard crane

A linearized dynamical model of shipboard crane with the Maryland Rigging is derived by using Lagranges equations. Based on the linearized model, numerical resonant frequencies of Maryland Rigging are obtained and verified for complete nonlinear model. One disturbance, the ship roll motion angle, and three control authorities, changing the length of pulley cable, pulley-brake mechanism control option and load control torque are included in the linearized model for analysis. Controllability and observability are confirmed for each control variable. An active control law to cancel the effect of ship roll motion on load pendulation is achieved by changing the cable length of the pulley and proved to be valid for small ship roll motion. Also, a controller based on linear quadratic regulator (LQR) is designed to reduce pendulation.

Design of generalized Sugeno controllers by approximating hybrid fuzzy-PID controllers

This paper proposes a simple technique to design a generalized Sugeno-type controller (GSC). A hybrid fuzzy-PID (HFPID) controller is approximated using recursive least squares by a Sugeno-type controller. The method used to generate the approximating data is shown to affect the approximation. A comparison of the performance between a Sugeno-type controller and a hybrid fuzzy-PID controller when they are both applied to a two-degree-of-freedom robot manipulator arm is shown. Furthermore, the number of terms needed to produce accurate performance of the generalized Sugeno-type controller is also discussed.

Generation of Equivalent-Circuit Models From Simulation Data of a Thermal System

In this paper, we develop a methodology to obtain medium-order electrical equivalent circuits (ECs) of the thermal behavior of electronic systems. The method combines several elements: 1) the use of detailed finite-element (FE) simulations of steady-state thermal behavior; 2) graph partitioning of FE meshes to decompose the geometry at intermediate levels of detail; and 3) physically guided estimation of the parameters of the EC. To obtain richer training datasets, we also develop a method to include fictitious heat sources inside the FE model. This approach yields modular medium-order models for extensive and complicated geometries, such as a power-electronic chip. Moreover, representing the thermal behavior with an EC enables coupled simulations of electrothermal behavior, which are important in power electronics. We test our algorithms on a multimaterial pole of a dc motor and electronic chip. Excellent agreement in modeling both steady-state and transient behaviors was obtained.

Critical infrastructure interdependency modeling: Using graph models to assess the vulnerability of smart power grid and SCADA networks

We discuss a framework for quantitative vulnerability assessment of critical infrastructure systems. We focus on the smart electric power delivery systems, i.e., electricity transmission and distribution smart grids, along with SCADA and EMS systems. We introduce concepts and results from graph and social network theories, and apply them to the study of the WSCC-Smart Power Grid Network-SCADA-EMS (WSSE) System. We also calculate values of the topological characteristics of the networks and compare their error and attack tolerances, i.e., their performance when vertices are removed, randomly or in a malicious way. Also, we study the possible topology generation models such as the random graph, small-world, and scale-free models. The WSEE system was found to follow the scale-free graph model of social network theory.

Feedforward Control Law For A Shipboard Crane With Maryland Rigging System

A state-space model of the Maryland Rigging shipboard crane is derived from Newtons law under the assumptions of boom stiffness, fully controllable boom motion, no cable elasticity, no damping, and full control authority for changing the length of the rope. A chaotic rolling moment, with a dominant frequency of the same order as the resonance frequency of the shipboard crane, is applied to the ship as an external disturbance. The effect of this disturbance is studied. Since designing a controller by means of analytical methods for this system is too complex, we use a novel approach to this problem that focuses on the equilibrium point. By deriving the equations for calculating the position of the equilibrium point of the load in space, we change the problem to minimizing the change in the position of this point. A feedforward type controller is then designed as to keep the load closest to the “equilibrium point” for the actual roll angle. The controller seeks to suppress the load sway caused by the ships rolling motion by changing the luffing angle while the friction in the pulley is assumed to be negligible. Changing the luffing angle seems to be the most effective control action in shipboard cranes. The feedforward gain is then optimized by numerical methods. The simulation results for this controller show a huge decrease in the sway magnitude as compared to the cases with no control. The roll angle, luffing angle of the boom, and the length of the rope are changed individually and then the related optimum feedforward gains are numerically obtained. Using these data, the mapping of the optimum gain based on these variables is derived. Scheduling the gain based on this mapping greatly improves the performance of the feedforward controller. This procedure can be repeated for similar applications.

Automated electric utility pole detection from aerial images

This paper presents an algorithm for the recognition of similar electrical poles from an aerial image by detecting the pole shadow. One pole is used as a template (already identified by a human operator) for the algorithm. The algorithm includes feature extraction, candidate position determination, and elimination of redundant candidates. First, features of a pole shadow are extracted using standard filters and image processing techniques. Then the extracted features are used to design convolution filters tailored to emphasize possible locations for the shadows. Subsequently, an image candidate is submitted to Radon Transformation to verify adherence to expected shadow characteristics. Simulations show that most poles are made much more noticeable by the algorithm.

Genetic algorithms and fuzzy-based vibration control of plate using PZT actuators

Presents a control system testbed which is used for active vibration control. Software simulation and real-time experiments are realized at the same time using different control methodologies through the MATLAB and dSPACE environments. Localized control with hybrid-fuzzy PD controller, genetic algorithms-designed PD controller and rate-feedback controller are shown to attenuate vibration at resonance frequencies. The results confirm that these three kinds of control methods are reliable in the active damping of the vibration.