Lectures related to State Space Representation: Models

Covers the linearization method through two examples in process control.

Covers the process control of a mixing tank and optimizing its performance.

Explores cascade control for furnace temperature and gas pressure regulation, showcasing its advantages over single-loop systems.

Covers the basics of digital control and its implementation using a microprocessor.

Covers linearity, causality, and equilibrium points in dynamic systems studied in the course.

Covers methods to determine stability of closed-loop transfer functions in Process Control.

Explains temperature regulation in tank systems through control loop elements and process control.

Explores the shift from continuous to discrete control systems, focusing on the challenges and benefits of digital implementation.

Introduces the concept of transfer function in dynamic systems analysis.

Explores cascade control, showcasing examples and characteristics, and recommends its use in specific scenarios.

Covers Laplace transform rules, examples, signal transformation, and process control.

Discusses the effect of setpoint change on temperature in a control system and the concept of real and double poles.

Explores derivative action in PID controllers, including measurement noise amplification and historical context.

Covers the structure theorem for state-space representations and companion forms.

Covers the elements of a control loop and process control.

Explores state-space representation, controllability, observability, and regulator calculation using the Ackermann method.

Explains the lead-lag controller, process control, feedforward action, and disturbance rejection in feedback systems.

Covers the step response and gain of first-order integrating systems.

Covers the inverse Laplace transform with examples and methods for solving complex roots.

Covers the types of control systems and loop transfer functions in process control.