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Continuous improvement

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A closed-loop system is a control system that measures its output and uses feedback to adjust its input toward a desired value.

Closed-loop control measures a system’s output, compares it with a desired value, and adjusts its input to reduce the difference. A thermostat, for example, tracks room temperature and switches heating or cooling to approach its setpoint. section 8.7

Figure 8.17 PID-based closed-loop control system—the PID controller takes the error signal and produces a control signal to reduce the error to zero.

The diagram shows how a PID controller turns the difference between desired and measured states into a control signal. section 8.7

A PID controller combines proportional, integral, and derivative terms to respond to error, its accumulated magnitude and duration, and its rate of change. Genetic algorithms can tune the controller’s Kp, Ki, and Kd parameters against a selected response objective. section 8.7 section 8.7

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines feedback, explains PID terms, and demonstrates genetic-algorithm tuning of controller parameters.
Examples
Uses thermostat-based room-temperature control and a PID tuning example.
What it assumes
Introduces thermostat feedback and error before explaining PID terms.
What you can do after
Apply `pymoo` and `control` to optimize PID parameters and inspect a step response.
How it connects
Connects feedback control with PID controllers, genetic algorithms, and step response.

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This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.