Abstract
This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be re-parameterized as ideal nonlinear feedforward and feedback evaluated within a Simulink simulation. Comparison is made to a custom proportional, derivative, integral controller (modified versions of classical proportional-integral-derivative control) implemented as a feedback control with a specific term to account for the nonlinear coupled motion. Consistent proportional, derivative, and integral gains were used throughout the duration of the experiments. The simulation results will show that akin feedforward control, deterministic self-awareness statements lack an error correction mechanism, relying on learning (which stands in place of feedback control), and the proposed combination of optimal self-awareness statements and a newly demonstrated analytically optimal learning yielded the highest accuracy with the lowest execution time. This highlights the potential effectiveness of a learning control system.
Highlights
The goal of rotational mechanics control is to have a system that can move to and hold a specific orientation in three-dimensional space, relative to an inertial frame
The results show that Figure the deterministic artificial intelligence system (D.A.I.)
The implemented experiment compared the effects of a feedforward, feedback, and a
Summary
The goal of rotational mechanics control is to have a system that can move to and hold a specific orientation in three-dimensional space, relative to an inertial frame. The goal may be viewed through three different lenses: classical control, modern control, and/or artificial intelligence (either stochastic or deterministic). These lenses explain the same control theory in three different contexts. With regard to classical control, both feed-forward and feedback controller are implemented in order to eliminate error between a desired and commanded signal [2]. With regard to modern control, the classical notion of feedforward and feedback is contemplated in terms of an estimation [3] and correction method [4,5,6] implemented using a non-linear control estimator coupled with a nonlinear corrector in order to reduce error. The third context relates control systems to deterministic artificial intelligence and machine learning
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