Model predictive control

Quantum mechanics

Algorithm

Iterative learning control

Control (management)

Discretization

Engineering

Biology

Systems engineering

Inverted pendulum

Computer science

Mathematical optimization

Controller (irrigation)

Physics

Nonlinear system

Trajectory

Task (project management)

Mathematics

Astronomy

Agronomy

Mathematical analysis

Artificial intelligence

Control theory (sociology)

Piecewise

Iterative method

IEEE Control Systems Letters

In this paper, we present an iterative Model Predictive Control (MPC) design for piecewise nonlinear systems.We consider finite time control tasks where the goal of the controller is to steer the system from a starting configuration to a goal state while minimizing a cost function.First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time.Utilizing this construction, we present a policy iteration scheme that iteratively generates safe trajectories which have nondecreasing performance.Finally, we test the proposed strategy on a discretized Spring Loaded Inverted Pendulum (SLIP) model with massless legs.We show that our methodology is robust to changes in initial conditions and disturbances acting on the system.Furthermore, we demonstrate the effectiveness of our policy iteration algorithm in a minimum time control task.

Iterative Model Predictive Control for Piecewise Systems