Robot control
Model predictive control
Definition
Model predictive control repeatedly optimizes future actions using a system model, applies the next part of the solution, and replans from updated state information. It can account for objectives and constraints over a finite prediction horizon.
Also known as: MPC, Receding-horizon control
Updated
Optimizing again as the robot moves
At each update, the controller predicts how candidate actions will change the robot's state over the next several time steps. It chooses a sequence that minimizes a cost while satisfying model and constraint equations, executes the first action or short segment, and solves again with new state estimates. MIT's trajectory-optimization notes describe this receding-horizon construction.
For a humanoid, an optimization might choose contact forces that track a desired walking velocity while respecting friction limits. Other formulations optimize footsteps or full joint trajectories.
Choosing the prediction model
A linear inverted pendulum model is cheaper to optimize than the full multibody dynamics, but describes fewer physical effects. Wensing and colleagues explain how simplified models, contact assumptions, and solver choices shape what can run in a control loop.
Feasibility and timing matter
A solution that is feasible now does not automatically ensure that the next optimization will remain feasible. Terminal conditions and other design choices can establish such guarantees under stated assumptions. Model errors and computation deadlines also matter: the robot must receive a useful command before the next control update, and predicted behavior must remain close enough to the real system for replanning to help.
Sources
Related terms
Trajectory optimization
Trajectory optimization finds a time-varying motion, and often control inputs, that minimizes an objective while satisfying specified constraints. Robot applications can include geometric, kinematic, and dynamic constraints.
Whole-body control
Whole-body control coordinates a robot’s joints and contacts to satisfy several motion and force objectives together. In humanoids, it commonly combines balance, foot motion, hand tasks, and posture subject to physical constraints.
Linear inverted pendulum model
The linear inverted pendulum model approximates a walking robot by a mass moving at constant height above its support, with simplified angular-momentum dynamics. These assumptions make horizontal center-of-mass acceleration linear in the displacement from the support point.
State estimation
State estimation infers quantities describing a robot or its environment from measurements and a model. A robot state may include position, orientation, velocity, and other variables that are not all directly measured.