  The paper introduces an interactive machine learning mechanism to process the
measurements of an uncertain, nonlinear dynamic process and hence advise an
actuation strategy in real-time. For concept demonstration, a
trajectory-following optimization problem of a Kinova robotic arm is solved
using an integral reinforcement learning approach with guaranteed stability for
slowly varying dynamics. The solution is implemented using a model-free value
iteration process to solve the integral temporal difference equations of the
problem. The performance of the proposed technique is benchmarked against that
of another model-free high-order approach and is validated for dynamic payload
and disturbances. Unlike its benchmark, the proposed adaptive strategy is
capable of handling extreme process variations. This is experimentally
demonstrated by introducing static and time-varying payloads close to the rated
maximum payload capacity of the manipulator arm. The comparison algorithm
exhibited up to a seven-fold percent overshoot compared to the proposed
integral reinforcement learning solution. The robustness of the algorithm is
further validated by disturbing the real-time adapted strategy gains with a
white noise of a standard deviation as high as 5%.
