Quasi-optimum design of control systems for moving base simulators

Optimal control of six degree of freedom moving-base simulato

Quasi-optimum design of a six degree of freedom moving base simulator control system

The design of a washout control system for a moving base simulator is treated by a quasi-optimum control technique. The broad objective of the design is to reproduce the sensed motion of a six degree of freedom simulator as accurately as possible without causing the simulator excursions to exceed specified limits. A performance criterion is established that weights magnitude and direction errors in specific force and in angular velocity and attempts to maintain the excursion within set limits by penalizing excessive excursions. A FORTRAN routine for relizing the washout law was developed and typical time histories using the washout routine were simulated for a range of parameters in the penalty- and weighting-functions. These time histories and the listing of the routine are included in the report

RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets

In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers. The Byzantine workers, during the learning process, may send arbitrary incorrect messages to the master due to data corruptions, communication failures or malicious attacks, and consequently bias the learned model. The key to the proposed methods is a regularization term incorporated with the objective function so as to robustify the learning task and mitigate the negative effects of Byzantine attacks. The resultant subgradient-based algorithms are termed Byzantine-Robust Stochastic Aggregation methods, justifying our acronym RSA used henceforth. In contrast to most of the existing algorithms, RSA does not rely on the assumption that the data are independent and identically distributed (i.i.d.) on the workers, and hence fits for a wider class of applications. Theoretically, we show that: i) RSA converges to a near-optimal solution with the learning error dependent on the number of Byzantine workers; ii) the convergence rate of RSA under Byzantine attacks is the same as that of the stochastic gradient descent method, which is free of Byzantine attacks. Numerically, experiments on real dataset corroborate the competitive performance of RSA and a complexity reduction compared to the state-of-the-art alternatives.Comment: To appear in AAAI 201