Aerospace Control Design

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• C1/M1 As part of the exam and coursework, students must apply mathematics, statistics and engineering principles to solve complex control design problems. Example tools include complex analysis, frequency response and linearisation. Much of the knowledge is at the forefront of control theory and the design of the controller is informed by a critical awareness of new developments in the field as well as the wider context of aerospace engineering, particularly aerodynamics and flight mechanics. C2/M2 As part of the exam and coursework, students must formulate and analyse complex control problems to determine if their controlled system can meet specified dynamic performance requirements. They must use mathematical and engineering principles to design sufficiently robust control strategies that consider missing information, e.g. an unknown transfer function, or uncertainty in the plant model. Limitations of techniques such as linearisation, lack of controllability/observability and frequency analysis are quantified in the exam and coursework. C3/M3 As part of the coursework, students must select appropriate computational (python, matlab, Simulink, etc.) and analytical techniques (root locus methods, frequency analysis, pole placement) to model complex control problems. Students discuss the limitation of the tools in their ability to model real-world conditions such as time delays, sensor noise and external disturbances. C6/M6 As part of the coursework, students apply a systems approach to model the dynamical behaviour of systems. The resulting block diagram or state-space representations are the basis for solving complex control design problems. C8/M8 As part of the coursework, students must identify and analyse ethical concerns related to control systems of aerospace applications. Students will analyse related case studies (737max, Airbus flight control law protections) to make reasoned ethical choices informed by professional codes of conduct. C9/M9 As part of the exam and coursework, students must use risk management strategies such as stability margins, noise suppression and performance measures to identify, evaluate and mitigate risks that arise due to uncertainty in the plant model, sensor failure/contamination and external disturbances.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• determine criteria for desired system performance and interpret trade-offs in different design configurations.
• apply standard design techniques to achieve satisfactory closed-loop performance.
• derive a model, making justifiable assumptions, from a description of a physical system.
• analyse time and frequency domain response characteristics from plots, determine stability and predict responses for modified plots.

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• apply these skills in specific domains, e.g. flight mechanics, ship dynamics and automotive systems.
• appreciate some of the technical issues associated with control system design and the relationship with other areas of engineering and allied disciplines.
• to analyse dynamic systems using standard mathematical techniques

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• how negative feedback affects dynamic response and its characterization by primary analysis and performance measures
• fundamental mathematical tools used in system analysis and design.
• low order linear mathematical models of physical systems and their manipulation

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Study and learn independently, solve problems systematically

Introduction (1 lecture)

Linear systems theory (2 lectures):

• review of time domain analysis of linear systems dynamics,
• stability, performance measures and design process
• state space and process models
• example control systems.

System Representation in the s-domain (4 lectures)

• the Laplace transform and system transfer function,
• free/forced behaviour and the characteristic equation,
• system poles and zeros, relative and absolute stability, root loci,
• steady-state error and the final value theorem.
• multivariable systems

Frequency response of linear systems (4 lectures)

• sinusoidal excitation and Fourier Series,
• forecasting gain and phase, the frequency response function,
• graphical representation of frequency response, Bode plots.

Closed-loop control systems (6 lectures)

• open/closed loop transfer function definitions,
• performance measures in control system design,
• control system design examples,
• PID control system definitions and characteristics.

Control system stability analysis (7 lectures)

• stability in the s-domain, the Root locus method,
• stability in the frequency domain, Nyquist criterion,
• performance measures in the frequency domain,
• gain and phase margins, closed loop frequency response.

Design of feedback control systems (6 lectures)

• system compensation objectives and characteristics,
• lead-lag compensation, root locus and frequency response methods
• disturbance rejection

Design examples (3 lectures)

• longitudinal, roll and yaw control
• phugoid suppression
• multivariable gas turbine engine control

Revision and problem solving (3 lectures)

Teaching methods will include 36 lectures, tutorial and problem solving classes.

Learning activities include directed reading, case studies, problem solving and computer simulations.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.