117 research outputs found
Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems
We present a number of novel algorithms, based on mathematical optimization
formulations, in order to solve a homogeneous multiprocessor scheduling
problem, while minimizing the total energy consumption. In particular, for a
system with a discrete speed set, we propose solving a tractable linear
program. Our formulations are based on a fluid model and a global scheduling
scheme, i.e. tasks are allowed to migrate between processors. The new methods
are compared with three global energy/feasibility optimal workload allocation
formulations. Simulation results illustrate that our methods achieve both
feasibility and energy optimality and outperform existing methods for
constrained deadline tasksets. Specifically, the results provided by our
algorithm can achieve up to an 80% saving compared to an algorithm without a
frequency scaling scheme and up to 70% saving compared to a constant frequency
scaling scheme for some simulated tasksets. Another benefit is that our
algorithms can solve the scheduling problem in one step instead of using a
recursive scheme. Moreover, our formulations can solve a more general class of
scheduling problems, i.e. any periodic real-time taskset with arbitrary
deadline. Lastly, our algorithms can be applied to both online and offline
scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c);
definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations
were correct only for special case of p_i=d_
Nonlinear predictive control of autonomous soaring UAVs using 3DOF models
We design a nonlinear model predictive control (NMPC) system for a soaring UAV in order to harvest the energy from the atmospheric updrafts. Our control framework combines an online estimation with a heuristic search method to obtain the UAV optimal trajectory. To allow for real-time computation of the control commands we solve the optimal control problem using a 3 degrees-of-freedom (DOF) model but apply the inputs to a more realistic 6DOF model. Hence, we design a 3DOF-6DOF model interaction strategy. Simulations show how the control system succeeds in energy extraction in a challenging dynamic atmospheric environment while satisfying its real-time contraints
Constrained LQR for Low-Precision Data Representation
Performing computations with a low-bit number representation results in a faster implementation that uses less silicon, and hence allows an algorithm to be implemented in smaller and cheaper processors without loss of performance. We propose a novel formulation to efficiently exploit the low (or non-standard) precision number representation of some computer architectures when computing the solution to constrained LQR problems, such as those that arise in predictive control. The main idea is to include suitably-defined decision variables in the quadratic program, in addition to the states and the inputs, to allow for smaller roundoff errors in the solver. This enables one to trade off the number of bits used for data representation against speed and/or hardware resources, so that smaller numerical errors can be achieved for the same number of bits (same silicon area). Because of data dependencies, the algorithm complexity, in terms of computation time and hardware resources, does not necessarily increase despite the larger number of decision variables. Examples show that a 10-fold reduction in hardware resources is possible compared to using double precision floating point, without loss of closed-loop performance
Robust explicit MPC design under finite precision arithmetic
We propose a design methodology for explicit Model Predictive Control (MPC) that guarantees hard constraint satisfaction in the presence of finite precision arithmetic errors. The implementation of complex digital control techniques, like MPC, is becoming increasingly adopted in embedded systems, where reduced precision computation techniques are embraced to achieve fast execution and low power consumption. However, in a low precision implementation, constraint satisfaction is not guaranteed if infinite precision is assumed during the algorithm design. To enforce constraint satisfaction under numerical errors, we use forward error analysis to compute an error bound on the output of the embedded controller. We treat this error as a state disturbance and use this to inform the design of a constraint-tightening robust controller. Benchmarks with a classical control problem, namely an inverted pendulum, show how it is possible to guarantee, by design, constraint satisfaction for embedded systems featuring low precision, fixed-point computations
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
Analytical results for the multi-objective design of model-predictive control
In model-predictive control (MPC), achieving the best closed-loop performance
under a given computational resource is the underlying design consideration.
This paper analyzes the MPC design problem with control performance and
required computational resource as competing design objectives. The proposed
multi-objective design of MPC (MOD-MPC) approach extends current methods that
treat control performance and the computational resource separately -- often
with the latter as a fixed constraint -- which requires the implementation
hardware to be known a priori. The proposed approach focuses on the tuning of
structural MPC parameters, namely sampling time and prediction horizon length,
to produce a set of optimal choices available to the practitioner. The posed
design problem is then analyzed to reveal key properties, including smoothness
of the design objectives and parameter bounds, and establish certain validated
guarantees. Founded on these properties, necessary and sufficient conditions
for an effective and efficient solver are presented, leading to a specialized
multi-objective optimizer for the MOD-MPC being proposed. Finally, two
real-world control problems are used to illustrate the results of the design
approach and importance of the developed conditions for an effective solver of
the MOD-MPC problem
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