Grant Maloy Smith

Wednesday, June 19, 2024 · 0 min read

# What is a PID Controller?

Proportional-integral-derivative PID controllers are fundamental components of today’s automation and control systems. PIDs utilize a closed-loop feedback control mechanism that continuously adjusts outputs based on the difference between a desired setpoint and the measured value. In this article, you will:

• Learn how a PID controller works

• Understand their primary applications

• See what the future holds for PID controllers

## Key elements of a PID controller

PID controllers represent a sophisticated feedback mechanism vital for controlling dynamic systems. At their core, they operate using three basic terms: Proportional (P), Integral (I), and Derivative (D). Each term uniquely modulates the output signal based on the difference between the desired setpoint and the actual measured value, commonly known as the error.

The Proportional (P) term responds to the present error, generating an output proportional to its magnitude. By applying immediate corrective action, the P term minimizes errors quickly.

The Integral (I) term addresses any persistent errors or long-term deviations from the setpoint by accumulating the error over time. By integrating the error signal, the I term ensures that the system approaches and maintains the setpoint accurately, eliminating steady-state errors.

The Derivative (D) term anticipates future changes in the error by evaluating its rate of change. This approach dampens oscillations and stabilizes the system, especially during transient responses.

## How does a PID controller work?

A PID controller continuously calculates an error signal as the difference between a desired setpoint (the target value) and the current process variable (the measured value). Based on this error signal, the controller adjusts the system's control input to minimize the error and maintain the process variable close to the setpoint.

Here's how it works in more detail:

Error Calculation: The PID controller continuously calculates the error signal as the difference between the desired set point and the current process variable.

Proportional Control: The proportional (P) term responds to the current error by producing an output proportional to the magnitude of the error.

Integral Control: The integral (I) term responds to the cumulative sum of past errors and aims to eliminate any steady-state error. It is calculated as the integral of the error over time.

Derivative Control: The derivative (D) term responds to the rate of change of the error and helps dampen rapid changes in the system. It is calculated as the derivative of the error over time.

Control Output: The control output is the sum of the proportional, integral, and derivative terms.

Adjustment of Control Input: The control output is applied as the input to the system being controlled. It adjusts system parameters such as valve positions, motor speeds, or heating elements to bring the process variable closer to the set point.

Feedback Loop: The process variable is continuously measured and fed back to the controller, closing the control loop. The controller adjusts the control input based on the feedback, aiming to minimize the error and maintain the process variable at the setpoint.

## PID controller formats

PID controllers are available as dedicated instruments, as shown below. However, they are commonly integrated with Programmable Logic Controllers (PLCs) in industrial automation and control systems. PLCs are programmable devices that control and monitor industrial processes, while PID controllers are specialized control algorithms designed to regulate specific process variables.

Engineers can configure PID parameters, setpoints, and tuning values using ladder logic or structured text programming languages within the PLC programming environment. Some PLC manufacturers include pre-programmed PID function blocks that simplify the implementation of PID control loops. PLCs communicate with PID controllers using industrial protocols like Modbus, Profibus, Ethernet/IP, or OPC (Open Platform Communications). PID controllers receive process variable measurements from PLCs and send control signals to adjust actuators in response.

Supervisory Control and Data Acquisition (SCADA) systems monitor and control industrial processes. PID controllers integrated with PLCs can be seamlessly incorporated into SCADA systems, allowing operators to monitor and adjust control loops remotely from a central interface.

In addition to integration with PLC and SCADA systems, PID controllers are sometimes implemented in microcontroller and embedded systems, where real-time control is needed, but a PLC would be overkill. PID algorithms can control devices such as motors, heaters, or sensors directly.

## Tuning a PID controller

Tuning the PID parameters Kp​, Ki​, and Kd​ is crucial in PID controller design. Tuning must be customized for each of the many PID applications. Key tuning parameters include:

Proportional Gain (Kp​): This parameter determines the proportion of the error signal contributing to the controller output. A higher Kp​ value results in a stronger response to the current error. Too high a Kp​ can lead to oscillations or instability, while too low a value can result in a sluggish response.

Integral Gain (Ki​): The integral term considers the accumulation of past errors and amplifies them over time. It helps eliminate steady-state error by continuously adjusting the control signal. A higher Ki​ value helps reduce steady-state error but can lead to overshoot or instability if set too high.

Derivative Gain (Kd​): The derivative term predicts the future behavior of the error based on its current rate of change. It helps dampen oscillations by counteracting rapid changes in the error signal. Increasing Kd​ enhances damping and reduces overshoot, but too high a value can lead to instability or sensitivity to noise.

The tuning process involves adjusting these parameters to achieve desired system performance, such as stability, responsiveness, and minimal overshoot. Several methods are used for PID tuning, including manual tuning, Ziegler-Nichols method, and optimization algorithms. Let’s take a closer look at each of these methods:

In manual tuning, the engineer adjusts the parameters based on their understanding of the system dynamics and the desired performance criteria. This method involves iteratively tweaking the parameters while observing the system's response until satisfactory performance is achieved.

The Ziegler-Nichols Method provides a systematic approach to PID tuning based on step response experiments. The integral and derivative gains are set to zero and gradually increased until the system oscillates at a constant amplitude. The proportional gain and oscillation period are determined from the oscillation period and amplitude, which are then used to calculate suitable PID parameters. Several other tuning methods exist, including Cohen-Coon, Lambda, and Dead Time

Optimization algorithms such as gradient descent, genetic algorithms, or particle swarm optimization automatically search for optimal PID parameters based on specified performance criteria and system models.

PID tuning is a critical step in control system design. It ensures that the controller effectively regulates the system while meeting performance requirements.

## Main applications of PID controllers

PID controllers are used in closed-loop systems where precise control of processes is required. Some of the main applications include:

PID controllers are extensively used to regulate temperature, including heating, cooling, and HVAC systems, where they help to maintain precise temperatures in environments such as industrial processes, buildings, and climate control systems.

PID controllers are integral in industrial automation for controlling parameters such as pressure, flow rate, level, and pH in manufacturing processes like chemical processing, water treatment, and food production.

In robotics and motion control systems, PID controllers regulate motor speed, position, and torque, ensuring smooth and accurate movement in applications such as CNC machines, robotic arms, and automated guided vehicles (AGVs).

Various process control applications use PID controllers to regulate pressure, level, and composition in systems such as oil refineries, power plants, and wastewater treatment facilities.

Electronics applications use PID controllers for tasks like voltage regulation, motor control, and power conversion in applications ranging from power supplies to motor drives.

Automotive systems employ PID controllers for engine control, cruise control, anti-lock braking systems, and automatic transmission control, optimizing safety, fuel efficiency, emissions, and vehicle performance.

In agricultural automation, PID controllers are used for greenhouse climate control, irrigation management, and livestock feeding systems, ensuring optimal growing conditions and resource utilization.

PID controllers are used in biomedical engineering and numerous medical devices to deliver drugs, regulate patient temperature, and control blood glucose in insulin pumps, incubators, and anesthesia machines.

PID controllers are used in renewable energy systems, such as solar power and wind turbines, where they optimize energy capture and regulate voltages.

Consumer electronics and household appliances use PID controllers to control tasks such as oven and refrigerator temperature control, washing machine water level control, and fan and pump speed control.

## Future trends in PID controllers

PID controllers are evolving to integrate with Internet of Things (IoT) platforms and Industry 4.0 initiatives, enabling remote monitoring, data analytics, and predictive maintenance. This integration enhances system intelligence, facilitates real-time optimization, and improves efficiency.

Future PID controllers may be used in adaptive and self-tuning controllers capable of automatically adjusting controller parameters based on changing process dynamics and operating conditions. These controllers exhibit adaptability to fluctuating environments, enhance performance, and reduce the need for manual tuning.

Advances in artificial intelligence (AI) and machine learning (ML) are revolutionizing control systems, including PID controllers. AI-based PID algorithms can learn from data, predict system behavior, and optimize control parameters in real time, leading to more robust and adaptive control strategies.

PID controllers are transitioning towards Distributed Control Systems (DCS), where multiple controllers communicate and collaborate within a networked ecosystem. DCS fosters decentralized decision-making, fortifies fault tolerance, and amplifies scalability, enhancing system robustness and flexibility.

## Integration with data acquisition systems

PID controller algorithms can be implemented in Data Acquisition (DAQ) systems from Dewesoft. Dewesoft offers high-speed DAQ systems like SIRIUS and industrial series like IOLITE, which combines DAQ and real-time control capabilities in one instrument. EtherCAT interconnectivity allows these devices to be daisy-chained together and precisely synchronized. PID operation is available as a math module in DewesoftX software provided with these devices.

## Conclusion

PID controllers epitomize precision and versatility in control systems, offering unparalleled regulation across diverse processes and applications. By delving into their mechanisms, applications, and future trajectories, engineers can harness the full potential of PID controllers to optimize control performance and drive innovation in an ever-evolving technological landscape.