Control Systems and Sensors Overview

Lecture 1

• Sensors provide data to control systems for decision-making.
• Sensors detect physical parameters using electrical signals (voltage or current).
• Control systems use mathematical models to represent physical systems.
• Single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) control systems exist.
• Actuators adjust the environment, using mechanical force from energy transformations.
• Open-loop systems' control actions are independent of the desired output. Feedback is absent.
• Closed-loop systems' control actions depend on the desired output. Feedback is present.
• Open-loop systems are easier to design and more economical, but typically less accurate.
• Closed-loop systems are more accurate but more complex and costlier to design.

Lecture 2

• Digital systems are flexible and easy to mass-produce.
• Analog systems are less complex and more reliable for specific applications.
• Digital systems are becoming cheaper, smaller, consuming less power and have greater processing power.
• Microprocessors (CPUs) support instruction fetching, data decoding and command executing.
• Microcontrollers combine processors, memory, digital/analog I/Os, and peripherals in a single unit.
• Programming languages for microcontrollers include C/C++, Assembly.

Lecture 3

• Analog-to-digital converters (ADCs) convert analog signals to digital signals for digital devices.
• Digital devices handle binary values (0,1), whereas analog devices handle continuous signal values.
• Common input devices include keyboards, mice, styluses, touchscreens, and sensors.
• Common output devices include screens, displays, printers, speakers, and LEDs.
• Torque is calculated as Force multiplied by Distance (Torque = F x D)
• Pulse-width modulation (PWM) adjusts average power by varying on-time intervals.
• Duty cycle (D) is the ratio of time on to total time.

Lecture 4

• Converters (choppers, rectifiers, inverters, matrix converters) transfer between AC and DC power.
• Analog signals are continuous and susceptible to noise over long distances.
• Digital signals are discrete, with limited values, and less affected by signal degradation.
• Digital systems require signals to be converted from analog to digital and processed.
• Analog systems have infinite resolution (precision), while digital systems have limited resolution.

Lecture 5

• AC (alternating current) and DC (direct current) power systems, electrical components, and transformers are covered.
• Inductive loads have lagging current, capacitive loads have leading current, and resistive loads have in-phase current.
• Important concepts include active power (useful work by devices), reactive power (to produce magnetic flux), and apparent power (vector sum of active and reactive power).

Lecture 6

• Analog-to-digital converters (ADCs) convert analog signals to digital.
• Resolution and sampling rates in ADCs affect accuracy.
• ADCs are sensitive to noise, especially at high speeds.
• ADCs used in applications like sensors, gathering data, communication systems, and control systems.
• Nyquist rule: sampling frequency should be at least twice the maximum frequency of the signal.

Lecture 7

• Programmable Logic Controllers (PLCs) are used in automation systems.
• PLCs control the output devices in response to input conditions and a custom program.
• Supervisory Control and Data Acquisition (SCADA) systems monitor and control industrial processes.
• Automation objectives include reducing labor costs, improving product quality, and increasing productivity.
• Automation languages for PLCs include instruction list, structure text, function block diagrams, ladder diagrams, and sequential function charts (SFCs).

Lecture 8

• Systems change over time with respect to input and output values.
• Static systems don't change with time; dynamic systems do.
• Models represent systems with relevant features to solve complex systems; a black box model presents input-output relationships without revealing the internal workings.
• White box model reveals internal dynamics in systems.
• Example dynamic systems: spring-mass-damper, RLC circuits, linear systems.
• Approximations useful with linear systems, step, ramp and parabolic inputs.

Lecture 9

• Optimum control requires adjusting parameters to minimize values.
• Process variables (PVs) are the parameters requiring control, such as temperature, pressure, and flow rate.
• Sensors measure PVs.
• Set points (SPs) are the desired values for parameters.
• Error (e) is the difference between the SP and PV.
• Proportional, Integral, and Derivative (PID) controllers can reduce steady state errors and overshoot.

Lecture 10

• Research, design requirements, feasibility, conceptual design, preliminary design and detailed design steps are part of the design process.
• Designers should consider existing solutions (successes and problems), and evaluate internal requirements, functionality, and costs when designing a system.

Lecture 11

• Various methods for extracting different natural resources, including metals, coal, oil, and natural gas, are needed in many industries.
• Manufacturing steps, such as beneficiation, smelting, casting, for processing metals and plastics, are presented.
• Obsolescence can be due to technology, functionality, or aesthetics.

Lecture 12

• Kinematic relationships can be used to find the position and orientations of links in mechanisms and robots.
• Sensors (e.g. ADCs) measure quantities.
• Safety improvements for machines include automatic safety features, redundant protections and compliance with regulations.
• Transient system behavior, such as overshoot, critically damped and under-damped systems, is an important part of signal processing.

General Notes

• Table 7.4 shows how PID (Proportional, Integral, and Derivative) gain changes influence step response.
• Silica sand mining can be dangerous, so hazard and incident identification are important.
• Negative temperature coefficient thermistors decrease in resistance when the temperature increases.