6,398 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
Simulation-based reachability analysis for nonlinear systems using componentwise contraction properties
A shortcoming of existing reachability approaches for nonlinear systems is
the poor scalability with the number of continuous state variables. To mitigate
this problem we present a simulation-based approach where we first sample a
number of trajectories of the system and next establish bounds on the
convergence or divergence between the samples and neighboring trajectories. We
compute these bounds using contraction theory and reduce the conservatism by
partitioning the state vector into several components and analyzing contraction
properties separately in each direction. Among other benefits this allows us to
analyze the effect of constant but uncertain parameters by treating them as
state variables and partitioning them into a separate direction. We next
present a numerical procedure to search for weighted norms that yield a
prescribed contraction rate, which can be incorporated in the reachability
algorithm to adjust the weights to minimize the growth of the reachable set
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