Institute of Electrical & Electronic Engineers
Faster, cheaper, and more power efficient optimization solvers than those currently possible using general-purpose techniques are required for extending the use of Model Predictive Control (MPC) to resource-constrained embedded platforms. The authors propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. They provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.