Nonlinear control

Nonlinear control

Nonlinear control theory is the area of control theory which deals with systems that are nonlinear, time-variant, or both. Control theory is an interdisciplinary branch of engineering and mathematics that is concerned with the behavior of dynamical systems with inputs, and how to modify the output by changes in the input using feedback, feedforward, or signal filtering. The system to be controlled is called the "plant". One way to make the output of a system follow a desired reference signal is to compare the output of the plant to the desired output, and provide feedback to the plant to modify the output to bring it closer to the desired output. Control theory is divided into two branches. Linear control theory applies to systems made of devices which obey the superposition principle. They are governed by linear differential equations. A major subclass is systems which in addition have parameters which do not change with time, called linear time invariant (LTI) systems. These systems can be solved by powerful frequency domain mathematical techniques of great generality, such as the Laplace transform, Fourier transform, Z transform, Bode plot, root locus, and Nyquist stability criterion. Nonlinear control theory covers a wider class of systems that do not obey the superposition principle. It applies to more real-world systems, because all real control systems are nonlinear. These systems are often governed by nonlinear differential equations. The mathematical techniques which have been developed to handle them are more rigorous and much less general, often applying only to narrow categories of systems. These include limit cycle theory, Poincaré maps, Lyapunov stability theory, and describing functions. If only solutions near a stable point are of interest, nonlinear systems can often be linearized by approximating them by a linear system obtained by expanding the nonlinear solution in a series, and then linear techniques can be used. Nonlinear systems are often analyzed using numerical methods on computers, for example by simulating their operation using a simulation language. Even if the plant is linear, a nonlinear controller can often have attractive features such as simpler implementation, faster speed, more accuracy, or reduced control energy, which justify the more difficult design procedure. An example of a nonlinear control system is a thermostat-controlled heating system. A building heating system such as a furnace has a nonlinear response to changes in temperature; it is either "on" or "off", it does not have the fine control in response to temperature differences that a proportional (linear) device would have. Therefore, the furnace is off until the temperature falls below the "turn on" setpoint of the thermostat, when it turns on. Due to the heat added by the furnace, the temperature increases until it reaches the "turn off" setpoint of the thermostat, which turns the furnace off, and the cycle repeats. This cycling of the temperature about the desired temperature is called a limit cycle, and is characteristic of nonlinear control systems. (Wikipedia).

Nonlinear control
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Fuzzy control of inverted pendulum

Fuzzy control of inverted pendulum, State-feedback controller is designed based on T-S fuzzy model with the consideration of system stability and performance.

From playlist Demonstrations

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What Is Gain Scheduling? | Control Systems in Practice

Often, the best control system is the simplest. When the system you’re trying to control is highly nonlinear, this can lead to very complex controllers. This video continues our discussion on control systems in practice by talking about a simple form of nonlinear control: gain scheduling.

From playlist Control Systems in Practice

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Nonlinear Control Systems

For the latest information, please visit: http://www.wolfram.com Speaker: Suba Thomas In Mathematica 10, a full suite of functions for analyzing and designing nonlinear control systems was introduced. This talk showcases the workflow for designing controllers for nonlinear systems using

From playlist Wolfram Technology Conference 2014

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What Is PID Control? | Understanding PID Control, Part 1

Chances are you’ve interacted with something that uses a form of this control law, even if you weren’t aware of it. That’s why it is worth learning a bit more about what this control law is, and how it helps. PID is just one form of feedback controller. It is the simplest type of contro

From playlist Understanding PID Control

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Everything You Need to Know About Control Theory

Control theory is a mathematical framework that gives us the tools to develop autonomous systems. Walk through all the different aspects of control theory that you need to know. Some of the concepts that are covered include: - The difference between open-loop and closed-loop control - How

From playlist Control Systems in Practice

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Fuzzy control of inverted pendulum,

Fuzzy control of inverted pendulum, State-feedback controller is designed based on T-S fuzzy model with the consideration of system stability and performance. Details can be found in https://nms.kcl.ac.uk/hk.lam/HKLam/index.php/demonstrations

From playlist Demonstrations

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C52 Introduction to nonlinear DEs

A first look at nonlinear differential equations. In this first video examples are shown of equations that still have explicit solutions.

From playlist Differential Equations

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Translating Inputs, Outputs, and Initial Conditions Between Linear and Nonlinear Dynamic Systems

In this video we discuss the nuances and differences between linear and nonlinear models. In particular, we show how to use equivalent inputs, outputs, and initial conditions for both systems. Topics and timestamps: 0:00 – Introduction 10:40 – Inputs 14:21 – Outputs 16:01 – Initial condi

From playlist Control Theory

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System Identification: Full-State Models with Control

This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and the sparse identification of nonlinear dynamics (SINDy). https://www.eigensteve.com/

From playlist Data-Driven Control with Machine Learning

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Adaptive, Gain-Scheduled and Nonlinear Model Predictive Control | Understanding MPC, Part 4

This video explains the type of MPC controller you can use based on your plant model, constraints, and cost function. - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN- - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M The available options include the linear ti

From playlist Understanding Model Predictive Control

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Koopman Observable Subspaces & Finite Linear Representations of Nonlinear Dynamics for Control

This video illustrates the use of the Koopman operator to simulate and control a nonlinear dynamical system using a linear dynamical system on an observable subspace. From the Paper: Koopman observable subspaces and finite linear representations of nonlinear dynamical systems for contro

From playlist Research Abstracts from Brunton Lab

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Koopman Operator Optimal Control

This video illustrates the use of the Koopman operator to simulate and control a nonlinear dynamical system using a linear dynamical system on an observable subspace. From the Paper: Koopman observable subspaces and finite linear representations of nonlinear dynamical systems for contro

From playlist Research Abstracts from Brunton Lab

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System Identification: Sparse Nonlinear Models with Control

This lecture explores an extension of the sparse identification of nonlinear dynamics (SINDy) algorithm to include inputs and control. The resulting SINDY with control (SINDYc) can be used with model predictive control for nonlinear systems. Sparse identification of nonlinear dynamics

From playlist Data-Driven Control with Machine Learning

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Nonlinear Model Predictive Control Design | Understanding MPC, Part 8

Learn how to design a nonlinear MPC controller for an automated driving application with Model Predictive Control Toolbox™ and Embotech FORCESPRO solvers. - Lane following using nonlinear model predictive control: https://bit.ly/3m3g19u The demonstration shows how to use the nonlinear M

From playlist Understanding Model Predictive Control

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Sparse Identification of Nonlinear Dynamics (SINDy)

This video illustrates a new algorithm for the sparse identification of nonlinear dynamics (SINDy). In this work, we combine machine learning, sparse regression, and dynamical systems to identify nonlinear differential equations purely from measurement data. From the Paper: Discovering

From playlist Research Abstracts from Brunton Lab

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Koopman Spectral Analysis (Control)

In this video, we explore extensions of Koopman theory for control systems. Much of the excitement and promise of Koopman operator theory is centered around the ability to represent nonlinear systems in a linear framework, opening up the potential use of linear estimation and control tech

From playlist Koopman Analysis

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Sparse Identification of Nonlinear Dynamics for Model Predictive Control

This lecture shows how to use sparse identification of nonlinear dynamics with control (SINDYc) with model predictive control to control nonlinear systems purely from data. Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. E. Kaiser, J. N. K

From playlist Data-Driven Control with Machine Learning

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Intro to Linear Systems: 2 Equations, 2 Unknowns - Dr Chris Tisdell Live Stream

Free ebook http://tinyurl.com/EngMathYT Basic introduction to linear systems. We discuss the case with 2 equations and 2 unknowns. A linear system is a mathematical model of a system based on the use of a linear operator. Linear systems typically exhibit features and properties that ar

From playlist Intro to Linear Systems

Related pages

Feedback | Simulation language | Control-Lyapunov function | Differential equation | Lyapunov redesign | Lyapunov stability | Time-variant system | Dynamical system | Backstepping | Popov criterion | Singular perturbation | Linearization | Nyquist stability criterion | Feedback linearization | Lyapunov function | Filter (signal processing) | Small-gain theorem | Bifurcation theory | Numerical method | Linear equation | Frequency domain | Gain scheduling | Laplace transform | Aleksandr Lyapunov | Nonlinear system | Plant (control theory) | Control theory | Circle criterion | Bode plot | Mathematics | Kalman's conjecture | Chaos theory | Limit cycle | Involution (mathematics) | Frobenius theorem (differential topology) | Root locus | Poincaré map | Small control property | Describing function | Feed forward (control) | Sliding mode control | Aizerman's conjecture | Fourier transform