Linear Systems Control and System Identification

Learning Outcomes

• Washington Accord (V4) Summary of Graduate Attributes attained in this course:
   WA2 – Problem Analysis
   WA3 – Design/Development of Solutions
   WA4 – Investigation
   WA5 – Tool Usage
   WA6 – The Engineer and the World
   WA8 – Individual and Collaborative Teamwork
   WA9 – Communication
   WA11 – Lifelong Learning
  
  
• Course topics with Learning Outcomes (and Washington Accord (WA) and UC Graduate Attributes) identified.
  
  1. Modern Control and Linear Systems: Introduction to state space systems and mathematics; State space description of dynamic systems: CTS and DTS; State space analysis, Stabilizable: Observability and Controllability, Stability; State space control design; Modern optimal control methods and stability analysis
           1.1. Understand system theory topics: including state space modelling of SISO and MIMO systems, natural and forced responses, state transition matrix, stability, controllability and observability (WA2, WA4)
           1.2. Ability to derive, interpret and solve problems using modern state space control methods for continuous time systems (CTS) and discrete time systems (DTS), and simulate these feedback control systems (WA2, WA5)
           1.3. Understand system stability, controllability, observability, and stabilizability in modern control, and able to use these tools in systems analysis and design (WA2, WA3, WA4, WA5) (EIE4)
           1.4. Ability to apply and solve problems for the design of modern state space and optimal control problems, including application to complex, multi-input, multi-output (MIMO) systems (WA2, WA3) (EIE3)
  2. System Identification (ID): Introduction to System ID and identifiability; Integral-based Methods; Gradient descent and Regression based methods
           2.1. Understand modern system identification methods using time domain and state space models (WA2)
           2.2. Ability to derive and solve system identification problems using modern state space system models and experimental data, including integral-based methods and least-squares based methods (WA2, WA4, WA5) (EIE4)
           2.3. Understand system ID methods and their implementation and application via project-based learning (WA2, WA3, WA10) (EIE2)
  3. Overarching course objectives, as per ENME303 and including:
           3.1. Able to apply modern modelling and control or system identification methods and analysis to a wider spectrum of real-life engineering problems via laboratories, problems, case studies and projects (WA6, WA9, WA10) (EIE1, EIE2, EIE5)
           3.2. Lay a theoretical and mathematical foundation for the analysis of advanced control systems (WA5, WA6, WA12) (EIE1, EIE3, EIE4, EIE5)

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Employable, innovative and enterprising Students will develop key skills and attributes sought by employers that can be used in a range of applications.

Prerequisites

Restrictions

For further information see Mechanical Engineering .