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A control system that operates without feedback. Example: A simple robotic arm programmed to perform repetitive tasks without sensors to adjust its motion.

A system that constantly monitors and adjusts its operation using feedback. Example: A robotic arm that corrects its position based on sensor readings.

This system relies on feedback to determine if the output has achieved the desired goal. Example: Autonomous vehicles continuously adjusting speed and steering based on environmental feedback.

A system that anticipates disturbances and compensates for them before they affect the system. Example: A robot that adjusts its movement in anticipation of a known obstacle.

A control loop feedback mechanism widely used in industrial control systems. Example: A drone stabilizing its flight using Proportional, Integral, and Derivative control factors.

A control system that can adjust its parameters in response to changes in the environment or system dynamics. Example: A service robot that updates its path planning in a dynamic environment.

A control system designed to function properly even in the presence of uncertainties. Example: Deep space probes that continue to operate despite varying space conditions.

This system uses artificial intelligence techniques to manage complex control problems. Example: A self-driving car making real-time decisions based on machine learning algorithms.

Combines both continuous and discrete control strategies. Example: A manufacturing robot that uses a combination of fluid motion control and discrete logic for complex assembly tasks.

A basic control system that switches the output fully on or off. Example: A robotic thermostat that turns a heating or cooling device on or off to maintain temperature.

This system is designed to operate in such a way as to optimize a specific performance criterion. Example: A robotic manipulator that minimizes energy consumption while completing tasks.

A system that deals with controlling processes that are subject to random variations. Example: A robotic inventory system that restocks based on probabilistic demand predictions.

This control system deals with nonlinearities which can be due to complex phenomena like saturation or friction. Example: A bipedal robot maintaining balance involves complex nonlinear dynamics.

Uses neural networks to approximate system dynamics and control actions. Example: A robotic arm that learns to grasp different objects using reinforcement learning.

Employs fuzzy logic to handle imprecision and reason about complex problems in a human-like manner. Example: A vacuum cleaning robot that adjusts its cleaning intensity based on dirt levels perceived imprecisely.

A control strategy that uses an explicit process model to predict future system behavior. Example: Industrial robots that predict the outcome of their actions to adjust their movements for precision tasks.

Utilizes digital signals to manage and regulate physical systems. Example: A drone's flight controller which interprets signals from sensors and user input to control the motors.

Operates based on a preset sequence of operations or events. Example: A robotic assembly line that performs a set sequence of actions to assemble a product.

A control strategy where the controller parameters are varied according to the operating point of the system. Example: An aircraft autopilot system that adjusts gains based on flight conditions.