Chapter 3 Analyzing Motion of Systems of Particles
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Position Management System Online Subject Area
USDA, NATIONAL FINANCE CENTER INSIGHT ENTERPRISE REPORTING Business Intelligence Delivered Insight Quick Reference | Position Management System Online Subject Area What is Position Management System Online (PMSO)? • This Subject Area provides snapshots in time of organization position listings including active (filled and vacant), inactive, and deleted positions. • Position data includes a Master Record, containing basic position data such as grade, pay plan, or occupational series code. • The Master Record is linked to one or more Individual Positions containing organizational structure code, duty station code, and accounting station code data. History • The most recent daily snapshot is available during a given pay period until BEAR runs. • Bi-Weekly snapshots date back to Pay Period 1 of 2014. Data Refresh* Position Management System Online Common Reports Daily • Provides daily results of individual position information, HR Area Report Name Load which changes on a daily basis. Bi-Weekly Organization • Position Daily for current pay and Position Organization with period/ Bi-Weekly • Provides the latest record regardless of previous changes Management PII (PMSO) for historical pay that occur to the data during a given pay period. periods *View the Insight Data Refresh Report to determine the most recent date of refresh Reminder: In all PMSO reports, users should make sure to include: • An Organization filter • PMSO Key elements from the Master Record folder • SSNO element from the Incumbent Employee folder • A time filter from the Snapshot Time folder 1 USDA, NATIONAL FINANCE CENTER INSIGHT ENTERPRISE REPORTING Business Intelligence Delivered Daily Calendar Filters Bi-Weekly Calendar Filters There are three time options when running a bi-weekly There are two ways to pull the most recent daily data in a PMSO report: PMSO report: 1. -
Solving Equations; Patterns, Functions, and Algebra; 8.15A
Mathematics Enhanced Scope and Sequence – Grade 8 Solving Equations Reporting Category Patterns, Functions, and Algebra Topic Solving equations in one variable Primary SOL 8.15a The student will solve multistep linear equations in one variable with the variable on one and two sides of the equation. Materials • Sets of algebra tiles • Equation-Solving Balance Mat (attached) • Equation-Solving Ordering Cards (attached) • Be the Teacher: Solving Equations activity sheet (attached) • Student whiteboards and markers Vocabulary equation, variable, coefficient, constant (earlier grades) Student/Teacher Actions (what students and teachers should be doing to facilitate learning) 1. Give each student a set of algebra tiles and a copy of the Equation-Solving Balance Mat. Lead students through the steps for using the tiles to model the solutions to the following equations. As you are working through the solution of each equation with the students, point out that you are undoing each operation and keeping the equation balanced by doing the same thing to both sides. Explain why you do this. When students are comfortable with modeling equation solutions with algebra tiles, transition to writing out the solution steps algebraically while still using the tiles. Eventually, progress to only writing out the steps algebraically without using the tiles. • x + 3 = 6 • x − 2 = 5 • 3x = 9 • 2x + 1 = 9 • −x + 4 = 7 • −2x − 1 = 7 • 3(x + 1) = 9 • 2x = x − 5 Continue to allow students to use the tiles whenever they wish as they work to solve equations. 2. Give each student a whiteboard and marker. Provide students with a problem to solve, and as they write each step, have them hold up their whiteboards so you can ensure that they are completing each step correctly and understanding the process. -
Chapters, in This Chapter We Present Methods Thatare Not Yet Employed by Industrial Robots, Except in an Extremely Simplifiedway
C H A P T E R 11 Force control of manipulators 11.1 INTRODUCTION 11.2 APPLICATION OF INDUSTRIAL ROBOTS TO ASSEMBLY TASKS 11.3 A FRAMEWORK FOR CONTROL IN PARTIALLY CONSTRAINED TASKS 11.4 THE HYBRID POSITION/FORCE CONTROL PROBLEM 11.5 FORCE CONTROL OFA MASS—SPRING SYSTEM 11.6 THE HYBRID POSITION/FORCE CONTROL SCHEME 11.7 CURRENT INDUSTRIAL-ROBOT CONTROL SCHEMES 11.1 INTRODUCTION Positioncontrol is appropriate when a manipulator is followinga trajectory through space, but when any contact is made between the end-effector and the manipulator's environment, mere position control might not suffice. Considera manipulator washing a window with a sponge. The compliance of thesponge might make it possible to regulate the force applied to the window by controlling the position of the end-effector relative to the glass. If the sponge isvery compliant or the position of the glass is known very accurately, this technique could work quite well. If, however, the stiffness of the end-effector, tool, or environment is high, it becomes increasingly difficult to perform operations in which the manipulator presses against a surface. Instead of washing with a sponge, imagine that the manipulator is scraping paint off a glass surface, usinga rigid scraping tool. If there is any uncertainty in the position of the glass surfaceor any error in the position of the manipulator, this task would become impossible. Either the glass would be broken, or the manipulator would wave the scraping toolover the glass with no contact taking place. In both the washing and scraping tasks, it would bemore reasonable not to specify the position of the plane of the glass, but rather to specifya force that is to be maintained normal to the surface. -
Chapter 5 ANGULAR MOMENTUM and ROTATIONS
Chapter 5 ANGULAR MOMENTUM AND ROTATIONS In classical mechanics the total angular momentum L~ of an isolated system about any …xed point is conserved. The existence of a conserved vector L~ associated with such a system is itself a consequence of the fact that the associated Hamiltonian (or Lagrangian) is invariant under rotations, i.e., if the coordinates and momenta of the entire system are rotated “rigidly” about some point, the energy of the system is unchanged and, more importantly, is the same function of the dynamical variables as it was before the rotation. Such a circumstance would not apply, e.g., to a system lying in an externally imposed gravitational …eld pointing in some speci…c direction. Thus, the invariance of an isolated system under rotations ultimately arises from the fact that, in the absence of external …elds of this sort, space is isotropic; it behaves the same way in all directions. Not surprisingly, therefore, in quantum mechanics the individual Cartesian com- ponents Li of the total angular momentum operator L~ of an isolated system are also constants of the motion. The di¤erent components of L~ are not, however, compatible quantum observables. Indeed, as we will see the operators representing the components of angular momentum along di¤erent directions do not generally commute with one an- other. Thus, the vector operator L~ is not, strictly speaking, an observable, since it does not have a complete basis of eigenstates (which would have to be simultaneous eigenstates of all of its non-commuting components). This lack of commutivity often seems, at …rst encounter, as somewhat of a nuisance but, in fact, it intimately re‡ects the underlying structure of the three dimensional space in which we are immersed, and has its source in the fact that rotations in three dimensions about di¤erent axes do not commute with one another. -
Position, Velocity, and Acceleration
Position,Position, Velocity,Velocity, andand AccelerationAcceleration Mr.Mr. MiehlMiehl www.tesd.net/miehlwww.tesd.net/miehl [email protected]@tesd.net Position,Position, VelocityVelocity && AccelerationAcceleration Velocity is the rate of change of position with respect to time. ΔD Velocity = ΔT Acceleration is the rate of change of velocity with respect to time. ΔV Acceleration = ΔT Position,Position, VelocityVelocity && AccelerationAcceleration Warning: Professional driver, do not attempt! When you’re driving your car… Position,Position, VelocityVelocity && AccelerationAcceleration squeeeeek! …and you jam on the brakes… Position,Position, VelocityVelocity && AccelerationAcceleration …and you feel the car slowing down… Position,Position, VelocityVelocity && AccelerationAcceleration …what you are really feeling… Position,Position, VelocityVelocity && AccelerationAcceleration …is actually acceleration. Position,Position, VelocityVelocity && AccelerationAcceleration I felt that acceleration. Position,Position, VelocityVelocity && AccelerationAcceleration How do you find a function that describes a physical event? Steps for Modeling Physical Data 1) Perform an experiment. 2) Collect and graph data. 3) Decide what type of curve fits the data. 4) Use statistics to determine the equation of the curve. Position,Position, VelocityVelocity && AccelerationAcceleration A crab is crawling along the edge of your desk. Its location (in feet) at time t (in seconds) is given by P (t ) = t 2 + t. a) Where is the crab after 2 seconds? b) How fast is it moving at that instant (2 seconds)? Position,Position, VelocityVelocity && AccelerationAcceleration A crab is crawling along the edge of your desk. Its location (in feet) at time t (in seconds) is given by P (t ) = t 2 + t. a) Where is the crab after 2 seconds? 2 P()22=+ () ( 2) P()26= feet Position,Position, VelocityVelocity && AccelerationAcceleration A crab is crawling along the edge of your desk. -
Chapter 3 Motion in Two and Three Dimensions
Chapter 3 Motion in Two and Three Dimensions 3.1 The Important Stuff 3.1.1 Position In three dimensions, the location of a particle is specified by its location vector, r: r = xi + yj + zk (3.1) If during a time interval ∆t the position vector of the particle changes from r1 to r2, the displacement ∆r for that time interval is ∆r = r1 − r2 (3.2) = (x2 − x1)i +(y2 − y1)j +(z2 − z1)k (3.3) 3.1.2 Velocity If a particle moves through a displacement ∆r in a time interval ∆t then its average velocity for that interval is ∆r ∆x ∆y ∆z v = = i + j + k (3.4) ∆t ∆t ∆t ∆t As before, a more interesting quantity is the instantaneous velocity v, which is the limit of the average velocity when we shrink the time interval ∆t to zero. It is the time derivative of the position vector r: dr v = (3.5) dt d = (xi + yj + zk) (3.6) dt dx dy dz = i + j + k (3.7) dt dt dt can be written: v = vxi + vyj + vzk (3.8) 51 52 CHAPTER 3. MOTION IN TWO AND THREE DIMENSIONS where dx dy dz v = v = v = (3.9) x dt y dt z dt The instantaneous velocity v of a particle is always tangent to the path of the particle. 3.1.3 Acceleration If a particle’s velocity changes by ∆v in a time period ∆t, the average acceleration a for that period is ∆v ∆v ∆v ∆v a = = x i + y j + z k (3.10) ∆t ∆t ∆t ∆t but a much more interesting quantity is the result of shrinking the period ∆t to zero, which gives us the instantaneous acceleration, a. -
Multidisciplinary Design Project Engineering Dictionary Version 0.0.2
Multidisciplinary Design Project Engineering Dictionary Version 0.0.2 February 15, 2006 . DRAFT Cambridge-MIT Institute Multidisciplinary Design Project This Dictionary/Glossary of Engineering terms has been compiled to compliment the work developed as part of the Multi-disciplinary Design Project (MDP), which is a programme to develop teaching material and kits to aid the running of mechtronics projects in Universities and Schools. The project is being carried out with support from the Cambridge-MIT Institute undergraduate teaching programe. For more information about the project please visit the MDP website at http://www-mdp.eng.cam.ac.uk or contact Dr. Peter Long Prof. Alex Slocum Cambridge University Engineering Department Massachusetts Institute of Technology Trumpington Street, 77 Massachusetts Ave. Cambridge. Cambridge MA 02139-4307 CB2 1PZ. USA e-mail: [email protected] e-mail: [email protected] tel: +44 (0) 1223 332779 tel: +1 617 253 0012 For information about the CMI initiative please see Cambridge-MIT Institute website :- http://www.cambridge-mit.org CMI CMI, University of Cambridge Massachusetts Institute of Technology 10 Miller’s Yard, 77 Massachusetts Ave. Mill Lane, Cambridge MA 02139-4307 Cambridge. CB2 1RQ. USA tel: +44 (0) 1223 327207 tel. +1 617 253 7732 fax: +44 (0) 1223 765891 fax. +1 617 258 8539 . DRAFT 2 CMI-MDP Programme 1 Introduction This dictionary/glossary has not been developed as a definative work but as a useful reference book for engi- neering students to search when looking for the meaning of a word/phrase. It has been compiled from a number of existing glossaries together with a number of local additions. -
Diffman: an Object-Oriented MATLAB Toolbox for Solving Differential Equations on Manifolds
Applied Numerical Mathematics 39 (2001) 323–347 www.elsevier.com/locate/apnum DiffMan: An object-oriented MATLAB toolbox for solving differential equations on manifolds Kenth Engø a,∗,1, Arne Marthinsen b,2, Hans Z. Munthe-Kaas a,3 a Department of Informatics, University of Bergen, N-5020 Bergen, Norway b Department of Mathematical Sciences, NTNU, N-7491 Trondheim, Norway Abstract We describe an object-oriented MATLAB toolbox for solving differential equations on manifolds. The software reflects recent development within the area of geometric integration. Through the use of elements from differential geometry, in particular Lie groups and homogeneous spaces, coordinate free formulations of numerical integrators are developed. The strict mathematical definitions and results are well suited for implementation in an object- oriented language, and, due to its simplicity, the authors have chosen MATLAB as the working environment. The basic ideas of DiffMan are presented, along with particular examples that illustrate the working of and the theory behind the software package. 2001 IMACS. Published by Elsevier Science B.V. All rights reserved. Keywords: Geometric integration; Numerical integration of ordinary differential equations on manifolds; Numerical analysis; Lie groups; Lie algebras; Homogeneous spaces; Object-oriented programming; MATLAB; Free Lie algebras 1. Introduction DiffMan is an object-oriented MATLAB [24] toolbox designed to solve differential equations evolving on manifolds. The current version of the toolbox addresses primarily the solution of ordinary differential equations. The solution techniques implemented fall into the category of geometric integrators— a very active area of research during the last few years. The essence of geometric integration is to construct numerical methods that respect underlying constraints, for instance, the configuration space * Corresponding author. -
Hybrid Position/Force Control of Manipulators1
Hybrid Position/Force Control of 1 M. H. Raibert Manipulators 2 A new conceptually simple approach to controlling compliant motions of a robot J.J. Craig manipulator is presented. The "hybrid" technique described combines force and torque information with positional data to satisfy simultaneous position and force Jet Propulsion Laboratory, trajectory constraints specified in a convenient task related coordinate system. California Institute of Technology Analysis, simulation, and experiments are used to evaluate the controller's ability to Pasadena, Calif. 91103 execute trajectories using feedback from a force sensing wrist and from position sensors found in the manipulator joints. The results show that the method achieves stable, accurate control of force and position trajectories for a variety of test conditions. Introduction Precise control of manipulators in the face of uncertainties fluence control. Since small variations in relative position and variations in their environments is a prerequisite to generate large contact forces when parts of moderate stiffness feasible application of robot manipulators to complex interact, knowledge and control of these forces can lead to a handling and assembly problems in industry and space. An tremendous increase in efective positional accuracy. important step toward achieving such control can be taken by A number of methods for obtaining force information providing manipulator hands with sensors that provide in exist: motor currents may be measured or programmed, [6, formation about the progress -
1.2 the Strain-Displacement Relations
Section 1.2 1.2 The Strain-Displacement Relations The strain was introduced in Book I: §4. The concepts examined there are now extended to the case of strains which vary continuously throughout a material. 1.2.1 The Strain-Displacement Relations Normal Strain Consider a line element of length x emanating from position (x, y) and lying in the x - direction, denoted by AB in Fig. 1.2.1. After deformation the line element occupies AB , having undergone a translation, extension and rotation. y ux (x x, y) ux (x, y) B * B A A B x x x x Figure 1.2.1: deformation of a line element The particle that was originally at x has undergone a displacement u x (x, y) and the other end of the line element has undergone a displacement u x (x x, y) . By the definition of (small) normal strain, AB* AB u (x x, y) u (x, y) x x (1.2.1) xx AB x In the limit x 0 one has u x (1.2.2) xx x This partial derivative is a displacement gradient, a measure of how rapid the displacement changes through the material, and is the strain at (x, y) . Physically, it represents the (approximate) unit change in length of a line element, as indicated in Fig. 1.2.2. Solid Mechanics Part II 9 Kelly Section 1.2 B A B* A B x u x x x x Figure 1.2.2: unit change in length of a line element Similarly, by considering a line element initially lying in the y direction, the strain in the y direction can be expressed as u y (1.2.3) yy y Shear Strain The particles A and B in Fig. -
Teaching Strategies for Improving Algebra Knowledge in Middle and High School Students
EDUCATOR’S PRACTICE GUIDE A set of recommendations to address challenges in classrooms and schools WHAT WORKS CLEARINGHOUSE™ Teaching Strategies for Improving Algebra Knowledge in Middle and High School Students NCEE 2015-4010 U.S. DEPARTMENT OF EDUCATION About this practice guide The Institute of Education Sciences (IES) publishes practice guides in education to provide edu- cators with the best available evidence and expertise on current challenges in education. The What Works Clearinghouse (WWC) develops practice guides in conjunction with an expert panel, combining the panel’s expertise with the findings of existing rigorous research to produce spe- cific recommendations for addressing these challenges. The WWC and the panel rate the strength of the research evidence supporting each of their recommendations. See Appendix A for a full description of practice guides. The goal of this practice guide is to offer educators specific, evidence-based recommendations that address the challenges of teaching algebra to students in grades 6 through 12. This guide synthesizes the best available research and shares practices that are supported by evidence. It is intended to be practical and easy for teachers to use. The guide includes many examples in each recommendation to demonstrate the concepts discussed. Practice guides published by IES are available on the What Works Clearinghouse website at http://whatworks.ed.gov. How to use this guide This guide provides educators with instructional recommendations that can be implemented in conjunction with existing standards or curricula and does not recommend a particular curriculum. Teachers can use the guide when planning instruction to prepare students for future mathemat- ics and post-secondary success. -
Step-By-Step Solution Possibilities in Different Computer Algebra Systems
Step-by-Step Solution Possibilities in Different Computer Algebra Systems Eno Tõnisson University of Tartu Estonia E-mail: [email protected] Introduction The aim of my research is to compare different computer algebra systems, and specifically to find out how the students could solve problems step-by-step using different computer algebra systems. The present paper provides the preliminary comparison of some aspects related to step-by-step solution in DERIVE, Maple, Mathematica, and MuPAD. The paper begins with examples of one-step solutions of equations. This is followed by a cursory survey of useful commands, entering commands, programming etc. I hope that a more detailed and complete review will be composed quite soon. Suggestions for complementing the comparison are welcome. It is necessary to know which concrete versions are under consideration. In alphabetical order: DERIVE for Windows. Version 4.11 (1996) Maple V Release 5. Student Version 5.00 (1998) Mathematica for Students. Version 3.0 (1996) MuPAD Light. Version 1.4.1 (1998) Only pure systems (without additional packages, etc.) are under consideration. I believe that there may be more (especially interface-sensitive) possibilities in MuPAD Pro than in MuPAD Light. My paper is not the first comparison, of course. I found several previous ones in the Internet. For example, 1. Michael Wester. A review of CAS mathematical capabilities. 1995 There are 131 short problems covering a broad range of symbolic mathematics. http://math.unm.edu/~wester/cas/Paper.ps (One of the profoundest comparisons is probably M. Wester's book Practical Guide to Computer Algebra Systems.) 1 2.