NN-Based Iterative Learning Control Under Resource Constraints: A Feedback Scheduling Approach
The problem of neural network based iterative learning control (NNILC) in a resource-constrained environment with workload uncertainty is examined from the real-time implementation perspective. Thanks to the iterative nature of the NNILC algorithm, it is possible to abort the optimization routine before it reaches the optimum. Taking into account the impact of resource constraints, a feedback scheduling approach is suggested, with the primary goal of maximize the control performance. The execution time of the NNILC task is dynamically adjusted to achieve a desired CPU utilization level. Thus a tradeoff is done between the available CPU time and the control performance. For the sake of easy implementation, a practical solution with a dynamic iteration stop criterion is proposed. Preliminary simulation results argue that the proposed approach is efficient and delivers better performance in the face of workload variations than the traditional NNILC algorithm.
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