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This module covers advanced control concepts that move away from simple linear approaches and idealised environments towards ensuring better quality of control in realistic robotic systems. The module will be grounded in mathematics and theory but aims to employ this theory in practical use cases.

Aims:

The aims of the module are to:

• Develop students’ enabling knowledge in advanced concepts in control, in control, including nonlinear system modeling and control, optimal control, robust control, and safety-critical control, and understanding their applications and implementations.
• Support students in creating practical solutions in robotics an AI against functional and non-functional user requirements, and in testing and assessing these solutions in simulated and real-world environments and articulating the limitations of those assessments.
• Provide students with the tools for critical analysis of reasoning about the appropriateness and quality of practical solutions produced in the context of the problems defined.

Intended learning outcomes:

On successful completion of this module, a student will be able to:

1. Demonstrate a broad knowledge and understanding of the fundamentals of optimal and safety-critical control, focusing on mitigating the adverse effects of unknown environments, with the goal of applying this knowledge within the realm of control to address complex issues in the fields of robotics and AI.ÌýÌý

1. Select an appropriate control approach from a range of possibilities based on intended engineering objectives in the problem domain.Ìý

1. Design, decompose, plan and implement the control components of simple robotic systems.ÌýÌý

1. Integrate these design components into a coherent whole, testing this to ensure operability.ÌýÌý

1. Demonstrate and assess the performance of the system, improving it where appropriate.Ìý

Indicative content:

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

• Linear quadratic control.
• Disturbance observer.
• Feedback linearisation.
• Lyapunov stability theory.
• Model predictive control.
• Robust control.
• Sliding mode control.
• Control barrier function.

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.