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Could it Work?

Automatic Control&

Systems Engineering

Dr Jun Liu

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Could it work?

• Feedback & Control in Every Discipline• Basic Elements of A Control System• Control Engineering as a Discipline• A Case Study• Estimation in Engineering• Things You Should Know• Some Resources

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What is Feedback?

• Feedback refers to the situation where two (or more) dynamical systems are connected together such that they interact with each other.

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The Power of Feedback

• Build accurate systems from imprecise components.

• Make systems resilient to external influences and internal component variations.

• Regulate, stabilise, and shape behavior.• Drawbacks:– Risk of instability– Injection of measurement noise– Complexity and costs

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Feedback Everywhere

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What is Control?

• Control is the use of algorithms and feedback in engineered systems (Astrom and Murray, 2008).

• Examples:– Feedback loops in electronic amplifiers– Setpoint controllers in chemical and materials

processing– “fly-by-wire” systems on aircraft– Router protocols that control traffic flow on the

Internet

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A Typical Control System

The feedback loop of sensing, computation, and actuation is the central concept in control.

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Control Engineering as a Discipline

• Control engineering relies on and shares tools physics (dynamics and modelling), computer science (information and software), and operation research (optimisation and game theory).

• But it also differs from these subjects in both insights and approach.

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Control Engineering as a Discipline

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Control Tools

• Modelling– From first principles (physics)– From input/output data (system identification)– Model reduction techniques

• Analysis– Stability analysis (linear and nonlinear systems)– Performance measures (input/output systems)– Robustness analysis– Computational tools for simulation and verification

• Synthesis– From simple feedback algorithms to complex logic– Automated synthesis of control algorithms directly from specification

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The PID Controller

Based on a survey of over eleven thousand controllers in the refining, chemicals and pulp and paper industries, 97% of regulatory controllers utilize PID feedback.

(L. Desborough and R. Miller, 2002)

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Autonomous VehiclesFuture goals: taking the human driver out of the loop could improve co-operative driving. Automatic speed control, car-to-car spacing may give improved through-put at junctions when joining, leaving a traffic stream.

© Jeff Dunn, Edudemic, 14 September 2012

Slides courtesy: Dr Anthony J H Simons

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Autonomous Vehicles

• Example: Autonomous Vehicles– Can we supersede human drivers?

• General considerations:– Safety: what volumes of traffic can you support

and travelling at what kinds of speed?– Efficiency: how can you minimize disruptions to

the steady flow of traffic, globally?– Control: should the vehicles be controlled locally

or centrally, or a combination of both?Slides courtesy: Dr Anthony J H Simons

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Autonomous Vehicles

• What control architecture?– Real-time: continuous feedback– Multi-sensor fusion: getting the bigger picture– Actuator control: what response ranges?

• What kind of sensing technology?– Computer vision, with 3D object resolution?– Other kinds of radar, sonar, HF wave?– How fast can you detect and avoid oncoming cars,

pedestrians, cyclists?Slides courtesy: Dr Anthony J H Simons

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Autonomous Vehicles

• What software technology?– How fast can you do edge-detection,

wire-frame modelling, 3D obstacle recognition?– How easily can you integrate vision with radar?– What processing and data bus requirements?

• Planning and autonomous control:– How do you model realistic acceleration of cars as

they join/leave traffic streams?– What kinds of emergency safety measures?– How to plan journeys to follow maps?

Slides courtesy: Dr Anthony J H Simons

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Autonomous Vehicles

Alice of Team Caltech2005 & 2007 DARPA Grand Challenge The Control System Architecture

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Estimation in Engineering

• Also known as “Rules of thumb” or “back of the envelope calculations”

• Can save time avoiding detailed analysis where a simple calculation reveals the answer

• The approach can help where any decision based on known principles needs to be made

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Making Assumptions

• Usually we will need to make some assumptions• In a big number, small differences are

insignificant; we only need rough accuracy• Useful to know some standard data (e.g. Typical

weight of a person, materials data, etc)• If you are making a big assumption, you could

repeat your calculation with different values to see the effect – i.e. does it change anything? Is the relationship linear or power?

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Using Estimates in Your Projects

• You need to support your proposals this week with some feasibility calculations

• These should prove that your ideas could work, not that they will work

• Think about using the approach you have seen today to help do this:– What is the control system? What are the sensing

mechanism, actuation mechanism, and control law? Could it work? How will it be implemented? Costs? etc

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Expectations of ACSE Students

• Think at a system level. • Think across disciplines. • Work collaboratively with engineers from other disciplines.• Be able to identify control objectives.• Be able to identify what comprise the main components of

the control system to be designed.• Be able to balance between costs and benefits:

– What controllers to use?– Design new systems or modifying existing ones?

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Some Resources• Åström & Murray, Feedback Systems: An

Introduction for Scientists and Engineers (available online)

• Dorf & Bishop, Modern Control Systems (IC or St George’s, 629.8312 (D))

• Golnaraghi & Kuo, Automatic Control Systems (St George's 629.831 (G))

• The Future of Control, K. J. Åström (http://workshop-impact-control-2009.lsr.ei.tum.de/workshop/files/Plenary_2.pdf)

• The Impact of Control Technology: Overview, Success Stories, and Research Challenges, report edited by T. Samad and A. Annaswamy, 2011 (http://www.ieeecss.org/main/IoCT-report)

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More Resources• PID controllers

– Wikipedia tutorial, http://en.wikipedia.org/wiki/PID_controller– PIDLab Java simulators, http://www.pidlab.com/en/home– MT Flanagan's PropIntDeriv class, part of a Java library at UCL,

http://www.ee.ucl.ac.uk/~mflanaga/java/index.html• Physics and Chemistry

– CRC Handbook of Chemistry and Physics, St George's 530 (C)

– Web resources, eg: http://srikant.org/core/phy11sep.html

• Materials Science– Ashby & Jones, Engineering

Materials 1, St George’s 620.1 (A)