Embedded Model Predictive Control (MPC) using a FPGA

Efficient Embedded Model Predictive Vibration Control via Convex Lifting

“…T ) which practically contribute to the optimal solution [29], yielding a constraint setΠ of the extended IpLP (12). Its solution consists of˜ I (x),κ I (x) defined over…”

“…This is where the efficiency-boosting achievements of control theory in the field of MPC come to the foreground: these developments allow one to implement better control methods with less resources. Because of the improvements in algorithm efficiency, model predictive control can now be implemented on embedded hardware such as MCUs [7], [8], programmable logic controllers (PLC) [9]- [11], or field programmable gate arrays (FPGA) [4], [12], [13], etc. Efficiency improvements in nominal or deterministic MPC can be divided into two main categories [14].…”

Model predictive control for deeply pipelined field-programmable gate array implementation: algorithms and circuitry

“…Existing work on hardware implementation of optimization solvers can be grouped into those that use interior-point methods [21]- [24] and those that use active-set methods [25], [26]. The suitability of each method for FPGA implementation was studied in [27], highlighting the advantages of interior-point methods for large problems.…”

“…The feasibility of implementing QP solvers for MPC applications on FPGAs was demonstrated in [21] with a Handel-C implementation exploiting modest levels of parallelism in the interior-point method. The implementation was shown to be able to respond to disturbances and achieve sampling periods comparable to stand-alone Matlab executables for relatively large problems.…”

Fast Model Predictive Control and its Application to Energy Management of Hybrid Electric Vehicles

“…To overcome this shortcoming, a very recent research (Mattingley & Boyd, 2010a;b;2009) has studied a development of an optimisation software package, referred to as CVXGEN, based on an earlier work by (Vandenberghe, 2010), which automates the conversion process, allowing practitioners to apply easily many class of convex optimisation problem conversions. CVXGEN is effectively a software tool which helps to specify one's problem in a higher level language, similar to other parser solvers such as SeDuMi or SDPT3 (Ling et al, 2008). The drawback of CVXGEN is that it is limited to optimization problems with up to around 4000 non-zero Karush-Kuhn-Tucker (KKT) matrix entries (Mattingley & Boyd, 2010b).…”