Courses tagged with "Vectors" (52)

The Experimental Project Lab in the Department of Aeronautics and Astronautics is a two-semester course sequence: 16.621 Experimental Projects I and 16.622 Experimental Projects II (this course). Students in 16.622 gain practical insight and improved understanding of engineering experimentation through design and execution of "project" experiments. Building upon work in course 16.621, students construct and test equipment, make systematic experimental measurements of phenomena, analyze data, and compare theoretical predictions with results. Deliverables comprise a written final project report and formal oral presentations. Instructions on oral presentations and multi-section reporting are given. Experimental Projects I and II provide a valuable link between theory (16.621) and practice (16.622).

This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to:

• Design controllers using state-space methods and analyze using classical tools.
• Understand impact of implementation issues (nonlinearity, delay).
• Indicate the robustness of your control design.
• Linearize a nonlinear system, and analyze stability.

General introduction to systems engineering using both the classical V-model and the new Meta approach. Topics include stakeholder analysis, requirements definition, system architecture and concept generation, trade-space exploration and concept selection, design definition and optimization, system integration and interface management, system safety, verification and validation, and commissioning and operations. Discusses the trade-offs between performance, lifecycle cost and system operability. Readings based on systems engineering standards and papers. Students apply the concepts of systems engineering to a cyber-electro-mechanical system, which is subsequently entered into a design competition.

Students will prepare a PDR (Preliminary Design Review)-level design intended for the Cansat Competition.This year's class will be taught in the form of a Small-Private-Online-Course (SPOC) and offered simultaneously to students at MIT under number 16.842 and Ecole Polytechnique Fédérale de Lausanne (EPFL) as ENG-421.

Human Supervisory Control of Automated Systems discusses elements of the interactions between humans and machines.  These elements include: assignment of roles and authority; tradeoffs between human control and human monitoring; and human intervention in automatic processes.  Further topics comprise: performance, optimization and social implications of the system; enhanced human interfaces; decision aiding; and automated alterting systems.  Topics refer to applications in aerospace, industrial and transportation systems.

This class addresses some of the important issues involved with the planning, development, and implementation of lean enterprises. People, technology, process, and management dimensions of an effective lean manufacturing company are considered in a unified framework. Particular emphasis is placed on the integration of these dimensions across the entire enterprise, including product development, production, and the extended supply chain. Analysis tools as well as future trends and directions are explored. A team project is a key component of this subject.

In 16.540 we address fluid dynamic phenomena of interest in internal flow situations. The emphasis tends to be on problems that arise in air breathing propulsion, but the application of the concepts covered is more general, and the course is wider in scope, than turbomachines (in spite of the title). Stated more directly, the focus is on the fluid mechanic principles that determine the behavior of a broad class of industrial devices. The material can therefore be characterized, only partly tongue in cheek, as "industrial strength fluid mechanics done in a rigorous manner".

The fundamental concepts, and approaches of aerospace engineering, are highlighted through lectures on aeronautics, astronautics, and design. Active learning aerospace modules make use of information technology. Student teams are immersed in a hands-on, lighter-than-air (LTA) vehicle design project, where they design, build, and fly radio-controlled LTA vehicles. The connections between theory and practice are realized in the design exercises. Required design reviews precede the LTA race competition. The performance, weight, and principal characteristics of the LTA vehicles are estimated and illustrated using physics, mathematics, and chemistry known to freshmen, the emphasis being on the application of this knowledge to aerospace engineering and design rather than on exposure to new science and mathematics.

This course introduces the fundamental Lean Six Sigma principles that underlay modern continuous improvement approaches for industry, government and other organizations. Lean emerged from the Japanese automotive industry, particularly Toyota, and is focused on the creation of value through the relentless elimination of waste. Six Sigma is a quality system developed at Motorola which focuses on elimination of variation from all processes. The basic principles have been applied to a wide range of organizations and sectors to improve quality, productivity, customer satisfaction, employee satisfaction, time-to-market and financial performance.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

A presentation of the fundamentals of modern numerical techniques for a wide range of linear and nonlinear elliptic, parabolic and hyperbolic partial differential equations and integral equations central to a wide variety of applications in science, engineering, and other fields. Topics include: Mathematical Formulations; Finite Difference and Finite Volume Discretizations; Finite Element Discretizations; Boundary Element Discretizations; Direct and Iterative Solution Methods.

This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5212 (Numerical Methods for Partial Differential Equations).

This course introduces the design of feedback control systems as applied to a variety of air and spacecraft systems. Topics include the properties and advantages of feedback systems, time-domain and frequency-domain performance measures, stability and degree of stability, the Root locus method, Nyquist criterion, frequency-domain design, and state space methods.

This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.

Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

This course studies basic optimization and the principles of optimal control. It considers deterministic and stochastic problems for both discrete and continuous systems. The course covers solution methods including numerical search algorithms, model predictive control, dynamic programming, variational calculus, and approaches based on Pontryagin's maximum principle, and it includes many examples and applications of the theory.

This course was created for the "product development" track of MIT's System Design and Management Program (SDM) in conjunction with the Center for Innovation in Product Development.  After taking this course, a student should be able to:

• Formulate measures of performance of a system or quality characteristics. These quality characteristics are to be made robust to noise affecting the system.
• Sythesize and select design concepts for robustness.
• Identify noise factors whose variation may affect the quality characteristics.
• Estimate the robustness of any given design (experimentally and analytically).
• Formulate and implement methods to reduce the effects of noise (parameter design, active control, adjustment).
• Select rational tolerances for a design.
• Explain the role of robust design techniques within the wider context of the product development process.
• Lead product development activities that include robust design techniques.