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

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

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.

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.

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.

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

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.

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

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.

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

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 system that must manage control actions in the presence of delays between input and output. Example: Satellite stabilization systems that account for communication time lags.

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 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.