What Is Model Predictive Control? MPC Explained for Robotics.

What Is Model Predictive Control?

Model predictive control, or MPC, is a control method that uses a mathematical model to predict how a system may behave over a future time period and calculate suitable control actions.

Rather than selecting commands based only on the current error, MPC considers possible future behavior and relevant constraints.

For example, an autonomous vehicle may use MPC to calculate steering and acceleration commands while considering its intended path and mechanical limits.

The method is widely used in industrial control and robotics applications involving complex movement.

Key Takeaways

• MPC uses a system model to predict future behavior and optimize control actions.
• It can explicitly account for constraints such as speed, acceleration, and actuator limits.
• MPC repeatedly recalculates its plan using updated information, but its computing requirements can be substantial.

How Does Model Predictive Control Work?

Imagine a mobile robot approaching a curved path.

The controller knows the robot's current position and velocity.

It also has a mathematical model describing how the robot responds to movement commands.

MPC predicts what might happen under different sequences of commands over a future time horizon.

It evaluates those sequences against an objective.

For example, the objective may penalize deviations from the desired route and excessive steering changes.

The controller selects a sequence that best satisfies the objective and constraints.

However, it executes only the first control action.

At the next update, it receives new information and solves the problem again.

This repeated process is called receding-horizon control.

MPC vs. PID Control

A PID controller uses current, accumulated, and changing error information to determine control adjustments.

MPC uses a predictive model to evaluate future behavior and constraints.

For example, a PID controller may regulate a motor's speed.

An MPC controller may coordinate several movement variables while accounting for actuator limits and future path requirements.

MPC can handle certain multivariable problems effectively, but often requires more computation and modeling work.

PID remains widely used because it can be simpler to implement and tune for suitable tasks.

Why Is MPC Useful in Robotics?

Robots frequently need to manage multiple objectives simultaneously.

A walking robot must maintain balance while moving its legs.

A mobile robot must follow a route while respecting its steering and speed limits.

An industrial manipulator may need to track movement without exceeding motor torque limits.

MPC provides a framework for incorporating these requirements into a repeated optimization problem.

What Are the Limitations?

MPC depends on having a useful system model.

Inaccurate predictions can reduce performance.

The optimization must also finish within the available control interval.

A controller that produces a good solution too late may be unsuitable for a real-time application.

Frequently Asked Questions

Does MPC require artificial intelligence?

No. Conventional MPC uses mathematical models and optimization. AI techniques can be combined with it, but they are not required.

Is MPC the same as model-based reinforcement learning?

No. MPC is a control technique. Certain model-based reinforcement learning approaches can use MPC as part of their decision-making process.

The Bottom Line

Model predictive control uses forecasts of system behavior to select actions while considering constraints. It is particularly useful for complex robotic movement that benefits from coordinated, forward-looking control.