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- W3126924351 abstract "This work focused on optimizing a Proportional-Integral-Derivative (PID) controller for a single-axis, gimbal-based inertial stabilization system, for a given performance objective. This study compared the suitability and relative performance of two meta-heuristic optimization methods for tuning the PID controller. The methods compared are two nature-inspired meta-heuristic algorithms: the Teaching-Learning-Based Optimization (TLBO) algorithm, and the Flower Pollination Algorithm (FPA). This study involved tuning the common and algorithm-specific parameters for the algorithms to be able to optimize the controller. These parameters were optimized for two different problem statements which represent two different target motion objectives. Comparing the algorithm performances using fitness function value, it was found that the problem instances favoured the TLBO algorithm." @default.
- W3126924351 created "2021-02-15" @default.
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- W3126924351 date "2021-01-01" @default.
- W3126924351 modified "2023-09-27" @default.
- W3126924351 title "Parameter Tuning of Nature-Inspired Meta-Heuristic Algorithms for PID Control of a Stabilized Gimbal" @default.
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- W3126924351 doi "https://doi.org/10.1007/978-3-030-68154-8_25" @default.
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