Predictive Control Using an FPGA With Application to Aircraft Control

• Published in: IEEE transactions on control systems technology Vol. 22; no. 3; pp. 1006 - 1017
• Main Authors: Hartley, Edward Nicholas, Jerez, Juan Luis, Suardi, Andrea, Maciejowski, Jan M., Kerrigan, Eric C., Constantinides, George A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace control; Algorithm design and analysis; Algorithms; Atmospheric modeling; Computation; Control systems; Field programmable gate arrays; field-programmable gate arrays (FPGAs); Hardware; Iterative methods; Mathematical models; optimization methods; Prediction algorithms; Predictive control; Quadratic programming; Solvers; Steady-state; optimization methods; predictive control; field-programmable gate arrays (FPGAs); Aerospace control
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2013.2271791

Alternative and more efficient computational methods can extend the applicability of model predictive control (MPC) to systems with tight real-time requirements. This paper presents a system-on-a-chip MPC system, implemented on a field-programmable gate array (FPGA), consisting of a sparse structure-exploiting primal dual interior point (PDIP) quadratic program (QP) solver for MPC reference tracking and a fast gradient QP solver for steady-state target calculation. A parallel reduced precision iterative solver is used to accelerate the solution of the set of linear equations forming the computational bottleneck of the PDIP algorithm. A numerical study of the effect of reducing the number of iterations highlights the effectiveness of the approach. The system is demonstrated with an FPGA-in-the-loop testbench controlling a nonlinear simulation of a large airliner. This paper considers many more manipulated inputs than any previous FPGA-based MPC implementation to date, yet the implementation comfortably fits into a midrange FPGA, and the controller compares well in terms of solution quality and latency to state-of-the-art QP solvers running on a standard PC.