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We present a framework for developing computationally efficient, first-order optimisation solvers using gradient filtering techniques. These solvers are similar in form to Nesterov's fast gradient method, however they are differentiated by the increased number of historical iterations used in the gradient filter. The filter tap weights are selected offline by solving a system of algebraic equations...
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. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate,...
Model predictive control (MPC) in resource-constrained embedded platforms requires faster, cheaper and more power-efficient solvers for convex programs than is currently offered by software-based solutions. In this paper we present the first field programmable gate array (FPGA) implementation of a fast gradient solver for linear-quadratic MPC problems with input constraints. We use fixed-point arithmetic...
This paper investigates optimal active control schemes applied to a point absorber wave energy converter within a receding horizon fashion. A variational formulation of the power maximization problem is adapted to solve the optimal control problem. The optimal control method is shown to be of a bang-bang type for a power take-off mechanism that incorporates both linear dampers and active control elements...
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