4,709 research outputs found
Synthesis of Minimal Error Control Software
Software implementations of controllers for physical systems are at the core
of many embedded systems. The design of controllers uses the theory of
dynamical systems to construct a mathematical control law that ensures that the
controlled system has certain properties, such as asymptotic convergence to an
equilibrium point, while optimizing some performance criteria. However, owing
to quantization errors arising from the use of fixed-point arithmetic, the
implementation of this control law can only guarantee practical stability:
under the actions of the implementation, the trajectories of the controlled
system converge to a bounded set around the equilibrium point, and the size of
the bounded set is proportional to the error in the implementation. The problem
of verifying whether a controller implementation achieves practical stability
for a given bounded set has been studied before. In this paper, we change the
emphasis from verification to automatic synthesis. Using synthesis, the need
for formal verification can be considerably reduced thereby reducing the design
time as well as design cost of embedded control software.
We give a methodology and a tool to synthesize embedded control software that
is Pareto optimal w.r.t. both performance criteria and practical stability
regions. Our technique is a combination of static analysis to estimate
quantization errors for specific controller implementations and stochastic
local search over the space of possible controllers using particle swarm
optimization. The effectiveness of our technique is illustrated using examples
of various standard control systems: in most examples, we achieve controllers
with close LQR-LQG performance but with implementation errors, hence regions of
practical stability, several times as small.Comment: 18 pages, 2 figure
Variance-constrained dissipative observer-based control for a class of nonlinear stochastic systems with degraded measurements
The official published version of the article can be obtained from the link below.This paper is concerned with the variance-constrained dissipative control problem for a class of stochastic nonlinear systems with multiple degraded measurements, where the degraded probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over a given interval. The purpose of the problem is to design an observer-based controller such that, for all possible degraded measurements, the closed-loop system is exponentially mean-square stable and strictly dissipative, while the individual steady-state variance is not more than the pre-specified upper bound constraints. A general framework is established so that the required exponential mean-square stability, dissipativity as well as the variance constraints can be easily enforced. A sufficient condition is given for the solvability of the addressed multiobjective control problem, and the desired observer and controller gains are characterized in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programming method. Finally, a numerical example is presented to show the effectiveness and applicability of the proposed algorithm.This work was supported in part by the Distinguished Visiting Fellowship of the Royal Academy of Engineering of the UK, the Royal Society of the UK, the GRF HKU 7137/09E, the National Natural Science Foundation of China under Grant 61028008, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany
Observer-based correct-by-design controller synthesis
Current state-of-the-art correct-by-design controllers are designed for
full-state measurable systems. This work first extends the applicability of
correct-by-design controllers to partially observable LTI systems. Leveraging
2nd order bounds we give a design method that has a quantifiable robustness to
probabilistic disturbances on state transitions and on output measurements. In
a case study from smart buildings we evaluate the new output-based
correct-by-design controller on a physical system with limited sensor
information
Design of State-based Schedulers for a Network of Control Loops
For a closed-loop system, which has a contention-based multiple access
network on its sensor link, the Medium Access Controller (MAC) may discard some
packets when the traffic on the link is high. We use a local state-based
scheduler to select a few critical data packets to send to the MAC. In this
paper, we analyze the impact of such a scheduler on the closed-loop system in
the presence of traffic, and show that there is a dual effect with state-based
scheduling. In general, this makes the optimal scheduler and controller hard to
find. However, by removing past controls from the scheduling criterion, we find
that certainty equivalence holds. This condition is related to the classical
result of Bar-Shalom and Tse, and it leads to the design of a scheduler with a
certainty equivalent controller. This design, however, does not result in an
equivalent system to the original problem, in the sense of Witsenhausen.
Computing the estimate is difficult, but can be simplified by introducing a
symmetry constraint on the scheduler. Based on these findings, we propose a
dual predictor architecture for the closed-loop system, which ensures
separation between scheduler, observer and controller. We present an example of
this architecture, which illustrates a network-aware event-triggering
mechanism.Comment: 17 pages, technical repor
Non-fragile H∞ control with randomly occurring gain variations, distributed delays and channel fadings
This study is concerned with the non-fragile H∞ control problem for a class of discrete-time systems subject to randomly occurring gain variations (ROGVs), channel fadings and infinite-distributed delays. A new stochastic phenomenon (ROGVs), which is governed by a sequence of random variables with a certain probabilistic distribution, is put forward to better reflect the reality of the randomly occurring fluctuation of controller gains implemented in networked environments. A modified stochastic Rice fading model is then exploited to account for both channel fadings and random time-delays in a unified representation. The channel coefficients are a set of mutually independent random variables which abide by any (not necessarily Gaussian) probability density function on [0, 1]. Attention is focused on the analysis and design of a non-fragile H∞ outputfeedback controller such that the closed-loop control system is stochastically stable with a prescribed H∞ performance. Through intensive stochastic analysis, sufficient conditions are established for the desired stochastic stability and H∞ disturbance attenuation, and the addressed non-fragile control problem is then recast as a convex optimisation problem solvable via the semidefinite programme method. An example is finally provided to demonstrate the effectiveness of the proposed design method
Robust quantum parameter estimation: coherent magnetometry with feedback
We describe the formalism for optimally estimating and controlling both the
state of a spin ensemble and a scalar magnetic field with information obtained
from a continuous quantum limited measurement of the spin precession due to the
field. The full quantum parameter estimation model is reduced to a simplified
equivalent representation to which classical estimation and control theory is
applied. We consider both the tracking of static and fluctuating fields in the
transient and steady state regimes. By using feedback control, the field
estimation can be made robust to uncertainty about the total spin number
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