PID controllers have served industry well for nearly a century, but they have inherent limitations when dealing with non-linear processes, time-varying dynamics, and multivariable interactions. Neural networks offer a powerful alternative that can learn complex process relationships directly from data.
Limitations of Traditional PID Control
PID controllers work by adjusting output based on the error between a setpoint and a measured process variable. While effective for simple, linear processes with stable dynamics, they struggle with processes that exhibit non-linearity, significant dead time, multiple interacting variables, and frequent disturbances.
Tuning PID controllers for complex processes is time-consuming and often results in suboptimal performance. When process conditions change, controllers may need retuning, creating an ongoing maintenance burden.
How Neural Networks Improve Control
Non-Linear Modelling
Neural networks are universal function approximators, meaning they can model arbitrarily complex non-linear relationships. This makes them ideal for processes where the relationship between inputs and outputs is too complex for linear models.
Multivariable Control
Unlike SISO (single-input, single-output) PID controllers, neural networks naturally handle multiple inputs and outputs simultaneously. They capture the interactions between variables that make multivariable control challenging.
Adaptive Behaviour
Neural networks can be continuously updated with new data, allowing them to adapt to changing process conditions without manual retuning. Online learning algorithms enable real-time model updates.
Neural Network Architectures for Control
Feedforward Networks
Multi-layer perceptrons serve as process models that predict output variables from input variables. These models are used in model predictive control frameworks to plan optimal control trajectories.
Recurrent Neural Networks
LSTMs and GRUs capture temporal dependencies in process data, making them effective for processes with significant dynamics and dead time. They learn from sequences of past measurements to predict future behaviour.
Reinforcement Learning
Deep reinforcement learning agents learn optimal control policies through interaction with the process or a simulation of it. They discover strategies that maximise long-term performance rather than just minimising instantaneous error.
Implementation Considerations
Safety
AI-based control must operate within defined safety constraints. Implementing hard limits on actuator outputs and monitoring for model degradation are essential. Many implementations use a cascaded architecture where the AI controller sets setpoints for underlying PID loops that provide a safety net.
Training Data
Training effective neural network controllers requires data that spans the operating range of the process, including transient conditions and disturbances. Simulation environments are valuable for generating training data safely.
Validation
Rigorous validation against held-out data and comparison with existing control strategies is essential before deploying neural network controllers in production.
Hybrid Approaches
The most practical approach often combines neural networks with traditional control. AI handles optimisation and adaptation while PID loops maintain stability and safety. This leverages the strengths of both approaches.
Industry Applications
Neural network control has been successfully deployed in chemical reactors, distillation columns, cement kilns, and power generation. These applications share the characteristics of non-linearity, multivariable interaction, and economic incentives for improved performance.
EDWartens training covers the practical aspects of implementing neural network-based process control, from data collection and model training to safe deployment and monitoring.