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Concept

State space (computer science)

Related lectures (32)

State-Space Representation: Controllability and Observability

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

State Space Control: Discrete Systems

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

State Representation and System Dynamics

Covers the representation of state-space models and system dynamics.

State-Space Representation: Basics & Transformations

Covers the basics of state-space representation and explores transformations to different forms.

State-Space Dynamic Response

Covers the computation of transfer functions and system poles and zeros.

Optimal Control and State Estimation

Covers the design of a regulator and state estimator for optimal control.

StateSpace Reference Tracking

Covers reference tracking in state-space systems and implementing control laws.

Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Applications of Laplace Transform: State Space Modeling

Explores the application of Laplace transform in state space modeling of linear systems and the control of dynamic systems.

Observability and Controllability

Explores observability and controllability in linear systems, emphasizing the significance of input decoupling for observability.

Multivariable Control: System Theory and Linear Systems

Introduces the basics of multivariable control, focusing on system theory and linear systems.

Multivariable Control: System Theory and Applications

Covers system theory, classic feedback control, and applications in green building and natural gas refrigeration plants.

Linear Quadratic Regulator Example

Covers an example of computing a linear controller to minimize closed-loop performance.

Introduction to Multivariable Control

Covers the basics of multivariable control, including system modeling, temperature control, and optimal strategies, emphasizing the importance of considering all inputs and outputs simultaneously.

StateSpace Controllability

Explores controllability in state-space models, state transformations, and their impact on state gain.

Dynamics of Discrete Systems

Covers the dynamics of discrete systems and the concept of orbits in Riemann varieties.

Introduction to Feedback Control Systems: Concepts and Applications

Covers the principles of feedback control systems and their applications in various fields.

Coupling of Markov Chains: Ergodic Theorem

Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.

StateSpace ControlDesign

Explores full state feedback control design, focusing on pole placement and linear state-feedback controller design for systems like a pendulum.

Symbolic Representation of State Spaces

Delves into symbolic representation of state spaces using decision diagrams for high-level Petri nets, showcasing efficient encoding techniques and benchmark results.