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Most robotic systems move through manipulating a set of actuators such as joints, wheels and propellors. To ensure accurate and smooth motion of the robot, the motion of these actuators must be precisely controlled.

This module is concerned with how a robot controls its joints and actuators to achieve desired trajectories. It will provide students exposure to basic theory, current leading-edge research, and the strengths and weaknesses of different approaches. It will mostly focus on linearized systems. This evaluation will include technical, ethical and societal impacts.

The module will cover concepts, such as state space models, estimation algorithms such as Kalman filters, and classical control algorithms such as linear quadratic control. Safety will also be considered.

Aims:

The aims of the module are to:Ìý

• Develop students’ knowledge of the types and kinds of estimation and control algorithms -theoretically how they work, how they are realised, and when is it most appropriate for the different types and kinds of control to be used.
• Support students in creating practical solutions in robotics and AI against functional and non-functional requirements, testing and assessing those in simulated and real-world environments and articulating the limitations of those assessments.
• Provide students with tools for critical analysis of reasoning about the appropriateness and quality of practical solutions produced in the context of the problems defined.
• Develop learners' ability to work in teams and communicate effectively about control system design and implementation.

Intended learning outcomes:

1. On successful completion of this module, a student will be able to:
2. Use a range of methods for single and multivariable control, stability and robustness and to be able to apply these in the context of control to solve complex problems in the field of robotics and artificial intelligence.
3. Apply the fundamentals of estimation and to be able to apply this to solve complex problems in the field of robotics and artificial intelligence.
4. Select an appropriate control approach from a range of possibilities based on intended engineering objectives in the problem domain.
5. Design, decompose, plan and implement the control components of simple robotic systems.
6. Demonstrate and assess the performance of robotic systems, improving them where appropriate.

Indicative content:

The following are indicative of the topics the module will typically cover:

• State space models.
• Models - what is a predictive model, discrete vs continuous time.
• Simple control: Bang/Bang, PID control.
• Stability.
• Kalman filters.
• Multivariable control.
• Linear Quadratic Control.
• Robust control.
• Ethics and safety.

Requisite conditions:

To be eligible to select this module as optional or elective, a student must be registered on a programme and year of study for which it is formally available.