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- W2017301246 abstract "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, and optimizes some performance criteria such as LQR-LQG. 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. We give a technique to synthesize embedded control software that is Pareto optimal w.r.t. both performance criteria and practical stability regions. Our technique uses static analysis to estimate quantization-related errors for specific controller implementations, and performs stochastic local search over the space of possible controllers using particle swarm optimization. The effectiveness of our technique is illustrated using several standard control system examples: in most examples, we find controllers with close-to-optimal LQR-LQG performance but with implementation errors, hence regions of practical stability, several times as small." @default.
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- W2017301246 date "2012-10-07" @default.
- W2017301246 modified "2023-10-18" @default.
- W2017301246 title "Synthesis of minimal-error control software" @default.
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- W2017301246 doi "https://doi.org/10.1145/2380356.2380380" @default.
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