Feedforward Control
An advanced control strategy that enables Autonomous Mobile Robots (AMRs) to anticipate motion requirements before errors occur. By acting proactively rather than reactively, Feedforward Control ensures smoother acceleration, precise cornering, and higher operational speeds in dynamic environments.
Core Concepts
Proactive Action
Unlike feedback loops that wait for an error to correct it, feedforward uses a mathematical model to calculate exactly what input is needed to achieve the desired motion instantly.
Inverse Dynamics
The controller uses "inverse dynamics" to understand the robot's mass and inertia, calculating the precise torque required to move the AGV from a standstill without lag.
Disturbance Rejection
Feedforward is excellent at compensating for known disturbances, such as gravity when moving up a ramp or kinetic friction on different floor surfaces.
Latency Reduction
By bypassing the "measure-compare-correct" cycle of PID controllers, feedforward significantly reduces the phase lag, making the robot feel much more responsive.
Trajectory Tracking
Essential for path following. It allows the AGV to stick to a virtual path on curved turns without undershooting (cutting the corner) or overshooting.
Hybrid Architecture
Rarely used alone, Feedforward is almost always combined with Feedback control. Feedforward handles the bulk of the work; Feedback corrects small modeling errors.
How It Works
In a standard Feedback system (like PID), the robot applies power, waits to see if it moved fast enough, calculates the error, and then adjusts. This creates an inherent delay.
Feedforward Control flips this script. Based on a physics model of the AGV, the controller calculates: "To reach 2 m/s in 1 second, given a mass of 500kg, I need to apply exactly X amount of voltage immediately."
The diagram illustrates the signal flow. The reference trajectory enters the Feedforward block (Inverse Model), which generates the control signal directly. Simultaneously, the Feedback block monitors sensors for any slight deviations caused by unexpected obstacles or wheel slippage.
This combination results in a robot that reacts instantly to commands while remaining robust against the unpredictability of the real world.
Real-World Applications
High-Speed Warehousing
In sortation centers where throughput is critical, AGVs must corner at high speeds. Feedforward compensates for centrifugal forces, ensuring the robot doesn't drift off the magnetic tape or QR grid during sharp turns.
Heavy Payload Transport
For AMRs carrying pallets (1000kg+), inertia is massive. Feedforward calculates the immense torque needed to start motion smoothly, preventing jerky starts that could topple the load.
Ramp Navigation
When an AGV detects an incline, Feedforward control adds a "gravity compensation" term. This prevents the robot from slowing down or rolling back when ascending ramps in multi-floor facilities.
Precision Docking
Connecting to charging contacts or conveyors requires millimeter precision. Feedforward allows the robot to decelerate perfectly to zero velocity exactly at the docking point without oscillation.
Frequently Asked Questions
What is the main difference between Feedback and Feedforward control?
Feedback control is reactive; it looks at the error (difference between desired and actual state) and corrects it. Feedforward is predictive; it uses a model to anticipate the required output needed to achieve a goal before any error occurs.
Why can't I just use Feedforward control alone?
Feedforward relies on a perfect mathematical model of the robot. In the real world, models are never perfect due to changing variables like tire wear, varying payload weights, or slippery floors, so Feedback is needed to correct the small remaining errors.
How does Feedforward control impact battery life?
It generally improves energy efficiency. By applying the correct amount of power immediately rather than oscillating (overshooting and undershooting) via feedback loops, the motors operate more smoothly, generating less heat and wasting less energy.
Is Feedforward control difficult to implement?
It requires more engineering effort than simple PID tuning because you must identify the physical parameters of the system (mass, friction coefficients, motor constants). However, modern auto-tuning software makes this system identification process much easier.
Does Feedforward help with "lag" in robot movement?
Yes, specifically phase lag. Because Feedforward acts immediately when the setpoint changes (rather than waiting for an error to accumulate), the robot's actual motion tracks the command signal almost instantly.
What happens if the payload weight changes on the AGV?
If the weight changes significantly and the Feedforward model isn't updated, performance will drop (undershoot or overshoot). Advanced AGVs use adaptive control or load sensors to update the mass parameter in the Feedforward equation dynamically.
How does it handle friction?
Static and kinetic friction are non-linear and hard for PID to handle smoothly. Feedforward adds a specific "friction compensation" term that applies extra torque the moment motion is requested to break static friction cleanly.
Does this require specialized hardware?
Generally, no. It is a software algorithm that runs on the standard microcontroller or PLC controlling the robot. However, it does require a controller capable of performing floating-point math efficiently.
Can Feedforward prevent collisions?
Not directly. Feedforward is a motor control strategy, not a navigation safety system. However, by making the robot more responsive to braking commands, it can reduce stopping distances, indirectly improving safety.
What is "Velocity Feedforward" vs "Acceleration Feedforward"?
Velocity Feedforward compensates for damping (friction relative to speed), while Acceleration Feedforward compensates for inertia (mass). Both are usually summed together to determine the total required torque.
Is it cost-effective for small fleets?
Yes. Since it is a software solution, the recurring cost is zero. The initial investment involves the engineering time to model the robot, but the payoff in increased throughput and reduced maintenance usually offers a high ROI.
How do I tune the Feedforward parameters?
You typically perform "step response" tests. You record the velocity profile without feedback enabled, calculate the inertia and friction based on how the robot accelerates and decelerates, and input these values into the controller.