Feedforward Compensation

The defining characteristic of feedforward control is the direct measurement of a disturbance variable using a sensor. This measurement is the input to a feedforward controller, which computes a compensatory control action. Common measured disturbances include:

• Inlet flow rate changes in a chemical reactor
• Ambient temperature fluctuations in a curing oven
• Upstream pressure variations in a hydraulic line
• Raw material composition shifts in a blending process The disturbance sensor must have a faster response time than the process dynamics to be effective.

The feedforward controller relies on a mathematical model that maps the measured disturbance to the required control action. This model captures the process's disturbance-to-output dynamics and actuator-to-output dynamics. Key model types include:

• Steady-state models: Simple gain-based compensation for slow processes
• Dynamic models: Transfer functions or state-space models that account for time delays and lags
• Non-linear models: Neural networks or first-principles equations for processes with non-linear disturbance responses The accuracy of compensation is directly proportional to model fidelity.

Feedforward is rarely deployed in isolation. It is almost always combined with a feedback controller in a complementary architecture:

• Feedforward handles the measured, predictable disturbances
• Feedback corrects for unmeasured disturbances, model inaccuracies, and setpoint tracking The feedforward signal is summed with the feedback controller output. This hybrid approach reduces the burden on the feedback loop, enabling tighter overall control and minimizing the magnitude of process variable deviations.

Feedforward compensators are categorized by their temporal sophistication:

• Static feedforward: Applies a simple gain multiplier to the disturbance measurement. Effective only when disturbance and control action dynamics are negligible relative to process response.
• Dynamic feedforward: Incorporates lead-lag elements or full dynamic inversion to synchronize the timing of the compensatory action with the disturbance's propagation through the process. This is critical when the disturbance path and actuator path have different time constants or dead times. Mismatched dynamics can worsen performance compared to feedback alone.

A common industrial application of feedforward is ratio control, where the flow rate of one stream is manipulated to maintain a fixed proportion to a measured wild stream. Examples:

• Fuel-to-air ratio in combustion systems
• Reagent-to-feedstock ratio in chemical reactors
• Diluent-to-concentrate ratio in blending operations This is a form of static feedforward where the disturbance is the wild flow and the model is a simple multiplicative ratio factor. It ensures stoichiometric or recipe consistency despite upstream variability.

Feedforward compensation has inherent limitations that dictate its applicability:

• Unmeasured disturbances: Cannot compensate for disturbances without sensors
• Model mismatch: Errors in the process model directly translate to imperfect compensation
• Actuator saturation: The feedforward signal can push the final control element to its limits, leaving no room for feedback correction
• Inverse response: Processes with non-minimum phase behavior require careful dynamic inversion to avoid instability
• Sensor failure: A failed disturbance sensor silently disables the feedforward path, potentially degrading control without alarm

An advanced control algorithm that uses a dynamic process model to predict future outputs and compute an optimal sequence of control moves over a finite receding horizon. Unlike feedforward's reactive disturbance cancellation, MPC proactively optimizes trajectories subject to constraints.

• Handles MIMO systems with input/output constraints
• Solves a constrained optimization problem at each time step
• Naturally pairs with feedforward for disturbance preview

An optimal recursive algorithm that estimates the internal state of a linear dynamic system from a series of noisy sensor measurements by minimizing the mean squared error. It provides the clean state estimates that feedforward controllers require to anticipate disturbance propagation.

• Fuses multiple sensor streams with known noise characteristics
• Provides uncertainty covariance alongside state estimates
• Essential when disturbances cannot be measured directly

A hierarchical architecture where the output of a primary controller serves as the setpoint for a secondary controller to rapidly reject disturbances affecting the inner loop. Feedforward compensation is often embedded in the outer loop to preemptively adjust the inner setpoint.

• Inner loop rejects disturbances before they propagate
• Outer loop handles slower process dynamics
• Dramatically improves rejection of measured load disturbances

A model-agnostic strategy that treats internal non-linearities and external disturbances as a total disturbance, estimating and canceling it in real-time via an extended state observer. ADRC extends feedforward principles to unmeasured disturbances.

• Does not require an explicit disturbance measurement
• Estimates and cancels the lumped disturbance online
• Robust to significant plant-model mismatch

The field of building mathematical models of dynamic systems from observed input-output data. Accurate feedforward compensator design depends on precise models of the disturbance-to-output transfer function obtained through system identification.

• Estimates parameters of candidate model structures
• Uses techniques like prediction error minimization
• Critical for designing the feedforward transfer function

A model-based tuning methodology where the controller explicitly contains a process model, providing an intuitive trade-off between closed-loop performance and robustness. The IMC structure naturally separates feedforward and feedback paths.

• Feedforward path contains the process model inverse
• Feedback path handles model mismatch and unmeasured disturbances
• Single tuning parameter balances performance and robustness