Children: Control theory
Robust control is a branch of control theory that deals with the design and analysis of controllers for dynamic systems that are subject to uncertainties and variations. The primary goal of robust control is to ensure that the system behaves reliably under a range of conditions, despite potential disturbances, parameter variations, or model inaccuracies.
A sampled data system is a type of system that processes continuous signals by taking discrete samples at specific intervals. This process involves converting a continuous-time signal into a discrete-time signal, which can then be analyzed and processed using digital methods. Key characteristics of sampled data systems include: 1. **Sampling**: This is the process of measuring the value of a continuous signal at regular intervals. The points at which the signal is measured are called samples.
Scenario optimization is a mathematical and computational approach used to make decisions under uncertainty by evaluating multiple possible future scenarios. This method is particularly relevant in fields such as finance, supply chain management, operations research, and energy systems, where outcomes can significantly vary based on uncertain factors. Here are the key elements of scenario optimization: 1. **Scenarios**: These are distinct representations of future states based on different assumptions regarding uncertain parameters.
The Schmidt-Kalman filter is an extension of the Kalman filter designed to handle situations where the system dynamics or measurement processes involve nonlinearities, particularly when the state space can be divided into linear and nonlinear components. It is typically used in scenarios where standard linear Kalman filtering is not sufficient due to the presence of nonlinear transformations. The Schmidt filter itself is often associated with the context of tracking and navigation, particularly in aerospace applications.
Self-organized criticality (SOC) is a concept in physics and complex systems theory that describes how certain systems naturally evolve into a critical state where minor changes can lead to significant, nonlinear events, such as avalanches, earthquakes, or market crashes. In a self-organized critical system, components interact in a way that the system accumulates energy or information over time, leading it to a critical threshold where a small trigger can cause a large-scale response.
Self-tuning refers to a system's ability to automatically adjust its parameters and settings to optimize performance without requiring manual intervention. This concept can be applied in various contexts, including: 1. **Machine Learning**: In this context, self-tuning algorithms may automatically adjust hyperparameters to improve model performance based on feedback or validation results. 2. **Databases**: Some database management systems utilize self-tuning mechanisms to optimize query performance and resource utilization by adjusting configurations or indexes dynamically.
"Sense and respond" is a concept often used in various fields, including business, technology, and systems theory, emphasizing the ability to detect changes in the environment and respond quickly and effectively to those changes. It contrasts with traditional models that might rely on predefined responses or rigid processes. ### Key Aspects of Sense and Respond: 1. **Real-time Awareness**: Organizations or systems must be able to monitor their environment continuously, collecting data to understand conditions and trends as they evolve.
In control systems, sensitivity refers to the measure of how the output of a system responds to changes in parameters or inputs. A system's sensitivity indicates how sensitive the system is to variations in its components, such as gains in the controller, system dynamics, disturbances, or external inputs. Sensitivity can be quantitatively expressed and is usually denoted as the sensitivity function.
The separation principle is a concept that can be applied in various fields, including control theory, economics, and decision-making processes. Here are some prominent interpretations of the separation principle based on different contexts: 1. **Control Theory**: In control theory, the separation principle refers to the idea that the control design process can be separated from the state estimation process.
The separation principle in stochastic control is a fundamental concept that applies to the design of optimal control strategies in systems influenced by randomness. It states that under certain conditions, the control problem can be decoupled into two distinct problems: one involving the estimation of the state of the system and the other involving the determination of the optimal control policy.
In the context of radio control (RC) systems, a "servo" is a type of electromechanical device that provides precise control of angular position, velocity, and acceleration. Servos are commonly used in RC models, including airplanes, helicopters, cars, boats, and drones, to control the movement of various components such as control surfaces (like ailerons, rudders, and elevators), steering mechanisms, and other movable parts.
Servo bandwidth refers to the range of frequencies over which a servo system can effectively respond to control inputs and maintain desired performance. In control systems, particularly in servos—which are systems used to provide precise control of angular or linear position, velocity, and acceleration—bandwidth is a critical parameter that affects the system’s responsiveness, stability, and accuracy.
A servomechanism, often referred to simply as a "servo," is an automatic device that uses feedback to control a mechanism's position, velocity, or acceleration. It consists of a motor (typically a DC motor, AC motor, or stepper motor) along with a feedback sensor (such as a potentiometer, encoder, or tachometer) and a controller.
A set-valued function is a type of mathematical function where, instead of associating each input with a single output, it associates each input with a set of possible outputs. Formally, a set-valued function can be defined as follows: Let \( X \) be a set (the domain) and \( Y \) be another set (the codomain).
A shift-invariant system, also known as a time-invariant system, is a type of system in which the output does not depend on the specific time at which an input is applied. In other words, if the input signal is shifted in time, the output signal will also shift in the same manner without changing its form.
Singular control refers to a specific type of control problem in the field of optimal control theory. It typically arises in situations where the control variables are subject to constraints or limits, and the system's dynamics can exhibit singularities. In mathematical terms, a control problem is considered "singular" when the usual assumptions about the behavior of the control signals break down, often leading to the need for special techniques to analyze and solve the problem.
The Smith Predictor is a control algorithm used primarily for processes with time delays. It is particularly effective in improving the performance of feedback control systems where delays can cause stability issues and degraded response characteristics. The main concept behind the Smith Predictor is to compensate for the time delay in the process by incorporating a model of the process dynamics into the control loop. ### Key Components: 1. **Process Model**: The Smith Predictor uses a mathematical model of the process to predict future output based on current and past inputs.
Space Vector Modulation (SVM) is a sophisticated technique used in pulse width modulation (PWM) for controlling power converters, specifically in the context of three-phase voltage source inverters. SVM is employed to represent the output voltage of an inverter as a vector in a two-dimensional space, which allows for more efficient and optimized control of the switching states of the inverter.
A state-transition equation is a mathematical representation used in various fields, such as control theory, systems engineering, and economics, to describe how a system transitions from one state to another over time. The equation typically relates the current state of the system to its next state and incorporates dynamic aspects of the system, such as time, input variables, or external influences.
The term "steady state" is used in various fields such as physics, engineering, biology, economics, and more, and it generally refers to a condition in which variables within a system remain constant over time despite ongoing processes or changes in other conditions.