Energy-aware MPC co-design for DC-DC converters

In this paper, we propose an integrated controller design methodology for the implementation of an energy-aware explicit model predictive control (MPC) algorithms, illustrat- ing the method on a DC-DC converter model. The power consumption of control algorithms is becoming increasingly important for low-power embedded systems, especially where complex digital control techniques, like MPC, are used. For DC-DC converters, digital control provides better regulation, but also higher energy consumption compared to standard analog methods. To overcome the limitation in energy efficiency, instead of addressing the problem by implementing sub-optimal MPC schemes, the closed-loop performance and the control algorithm power consumption are minimized in a joint cost function, allowing us to keep the controller power efficiency closer to an analog approach while maintaining closed-loop op- timality. A case study for an implementation in reconfigurable hardware shows how a designer can optimally trade closed-loop performance with hardware implementation performance

Predictive Control for Alleviation of Gust Loads on Very Flexible Aircraft

In this work the dynamics of very flexible aircraft are described by a set of non-linear, multi-disciplinary equations of motion. Primary structural components are represented by a geometrically-exact composite beam model which captures the large dynamic deformations of the aircraft and the interaction between rigid-body and elastic degrees-of-freedom. In addition, an implementation of the unsteady vortex-lattice method capable of handling arbitrary kinematics is used to capture the unsteady, three-dimensional flow-eld around the aircraft as it deforms. Linearization of this coupled nonlinear description, which can in general be about a nonlinear reference state, is performed to yield relatively high-order linear time-invariant state-space models. Subsequent reduction of these models using standard balanced truncation results in low-order models suitable for the synthesis of online, optimization-based control schemes that incorporate actuator constraints. Predictive controllers are synthesized using these reduced-order models and applied to nonlinear simulations of the plant dynamics where they are shown to be superior to equivalent optimal linear controllers (LQR) for problems in which constraints are active

Multivariable predictive controller for a test stand of air conditionning

In this paper a Multivariable Predictive Controller has been proposed in a stochastic framework for a M-input N-output system. It has been investigated using a simulation study based on an experimental model of an industrial test stand of air conditioning. Comparisons with the existing PID regulation show a great improvement : both step response and coupling effect limitation have been improved. With a 32 ms calculation time on a PC with 486DX processor (or 8 ms with a Pentium 100 processor), this regulator is able to answer the problems raised by this industrial test stand. Compatible with the industrial regulation hardware, this control algorithm will be soon set up and tested to lead the future air conditioning tests

Co-design of a controller and its digital implementation: the MOBY-DIC2 toolbox for embedded model predictive control

Several software tools are available in the literature for the design and embedded implementation of linear model predictive control (MPC), both in its implicit and explicit (either exact or approximate) forms. Most of them generate C code for easy implementation on a microcontroller, and the others can convert the C code into hardware description language code for implementation on a field programmable gate array (FPGA). However, a unified tool allowing one to generate efficient embedded MPC for an FPGA, starting from the definition of the plant and its constraints, was still missing. The MOBY-DIC2 toolbox described in this brief bridges this gap. To illustrate its functionalities, the tool is exploited to embed the controller and observer for a real buck power converter in an FPGA. This implementation achieves a latency of about 30 µs with the implicit controller and 240 μs with the approximate explicit controller

Evolvable Hardware Based Optimal Position Control of Quadcopter

Trading off performance metrics in control design for position tracking is unavoidable. This has severe consequences in mission-critical systems such as quadcopter applications. The controller area and propulsion energy are conflicting design parameters, whereas the reliability and tracking speed are related metrics to be optimized. In this research, a switching-based position controller was co-simulated with the quadcopter model. Performance analysis of the Field Programmable Gate Array (FPGA)-based controller validates a better scheme for tracking speed, propulsion energy, and reliability optimization under similar error performance. To improve the computation power and controller area, the dynamic partial reconfiguration(DPR) approach has been adapted and implemented on FPGA using the Vivado Integrated Development Environment (IDE), where a ranking-based approach brings into action either proportional derivative, sliding mode, or model predictive controllers for each dimension of position tracking. It is verified by analyzing the cumulative tracking speed, reliability, controller area, and propulsion energy metrics that the proposed controller can optimize all these metrics within three successive iterations of tracking either in the same direction or in any combination of directions. Concerning the implementation results of the controller with the switching-based controller, there is 6 % computation power and 30 % resource savings due to DPR

Digital VLSI Implementation of Piecewise-Affine Controllers Based on Lattice Approach

This paper presents a small, fast, low-power consumption solution for piecewise-affine (PWA) controllers. To achieve this goal, a digital architecture for very-large-scale integration (VLSI) circuits is proposed. The implementation is based on the simplest lattice form, which eliminates the point location problem of other PWA representations and is able to provide continuous PWA controllers defined over generic partitions of the input domain. The architecture is parameterized in terms of number of inputs, outputs, signal resolution, and features of the controller to be generated. The design flows for field-programmable gate arrays and application-specific integrated circuits are detailed. Several application examples of explicit model predictive controllers (such as an adaptive cruise control and the control of a buck-boost dc-dc converter) are included to illustrate the performance of the VLSI solution obtained with the proposed lattice-based architecture