Bahram Kimiaghalam

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.

An application of model predictive control for a shipboard crane

Designing an effective controller for shipboard cranes by means of analytical methods is too complex, because of their highly nonlinear equations of motion. In this work we formulate the swinging suppression problem for a new structure of shipboard crane designed by Maryland research group. Then a feedforward control law to greatly decrease the load sway will be introduced. This feedforward control counteracts the effect of ship rolling on load sway based on measurements of ship rolling angle at each instant. The measurement errors and also sways caused by other disturbance sources are not taken into account by this feedforward control. Next, we describe the model predictive control (MPC) based nonlinear controller that is designed for this problem. The proposed controller uses an optimizer to find an open loop solution at each sampling interval for a given horizon, based on a model of the plant and an appropriately defined objective function. MPC acts as a feedback control that will compensate for shortcomings of the feedforward control. Details of the controller, the design process and the simulation results are presented.

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.

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.

A generalized framework for autonomous formation reconfiguration of multiple spacecraft

Formation flying scenarios involve the coordinated control of multiple spacecraft to achieve specific scientific measurements for a mission. Formation reconfiguration is the process that allows science data acquisition by aligning each spacecraft in a desired configuration. Formation reconfiguration is thus a necessary step to enable the science goals of formation flying missions, and involves combining knowledge derived from many diverse fields in order to adequately formulate the problems that exist and develop robust solutions to address them. For that reason, control of autonomous flying formations is a very broad problem space, and there is considerable research in many of the different sub areas that are encompassed by this field. In this paper, we suggest a framework to define a generalized structure for combining divergent solutions to formation flying control, with a focus on developing a system capable of autonomous reasoning for formation flying reconfiguration. We also explore selected areas of research that are relevant to addressing the reconfiguration problem. In this paper, we discuss a generalized framework that can incorporate all necessary aspects of reconfiguration, from constraint satisfaction to cooperative autonomous control CAC, into an autonomous control system. This framework allows the control process that is currently handled on the ground to be handled by the fleet in space. The integration of multiple technologies into a generalized framework can assist the fleet by allowing independent spacecrafts to determine how to maneuver in situations where due to time and/or communication constraints they can not depend on ground control for assistance.

Genetic algorithm based gain scheduling

We designed a feedforward control law that greatly decreases the load sway of a shipboard crane due to ship rolling. This feedforward control uses measurements of ship rolling angle at each instant. At different operating points the optimal feedforward gain changes while is numerically computable. Here, we propose to use a genetic algorithm (GA) based approach to optimize the mapping of feedforward gain in four dimensional space. The process is based on the numerical calculation of the optimal feedforward gain for any rolling angle (/spl rho/), length of the rope (L), and luffing angle (/spl delta//sub 0/). The optimal gain is calculated for a group of points in the working space and then fit a function of order n to these points in a four dimensional space. Our choice for this problem includes real value GA with a combination of different crossover methods. The cost function is the sum of squared errors at selected points and we aim to minimize it. Since moving the load to another location also changes the optimal gain, the new improved gain scheduling further reduces the swinging within the whole working space. GA is a directed search method and is capable of searching for variables of functions with any desired structure. The major advantages of using GA for function mappings is that the function does not have to be linear or in any specific form.

LQG controller for asymmetrical half-bridge converter with range winding

Asymmetric half-bridge converters are widely used as the front-end DC-DC converter in distributed power systems. Added range windings are proposed to increase the efficiency of these converters. Activating and deactivating these windings produce transient effect in the output voltage. In this study, an optimal controller based on the linear quadratic Gaussian (LQG) method is designed for the efficient regulation of the output voltage of an asymmetric half-bridge controller. The intent of implementing LQG control is to minimize the output voltage transient produced by range switch operation due to input voltage variations. Simulation results are presented to compare the performance of the new LQG based controller with that of a conventional controller.

Means-End Relations and a Measure of Efficacy

Propositional dynamic logic (PDL) provides a natural setting for semantics of means-end relations involving non-determinism, but such models do not include probabilistic features common to much practical reasoning involving means and ends. We alter the semantics for PDL by adding probabilities to the transition systems and interpreting dynamic formulas 〈α〉 ϕ as fuzzy predicates about the reliability of α as a means to ϕ. This gives our semantics a measure of efficacy for means-end relations.