A Learning-Based Stable Servo Control Strategy Using Broad Learning System Applied for Microrobotic Control

As the controller parameter adjustment process is simplified significantly by using learning algorithms, the studies about learning-based control attract a lot of interest in recent years. This article focuses on the intelligent servo control problem using learning from desired demonstrations. Compared with the previous studies about the learning-based servo control, a control policy using the broad learning system (BLS) is developed and first applied to a microrobotic system, since the advantages of the BLS, such as simple structure and no-requirement for retraining when new demos' data is provided. Then, the Lyapunov theory is skillfully combined with the complex learning algorithm to derive the controller parameters' constraints. Thus, the final control policy not only can obtain the movement skills of the desired demonstrations but also have the strong ability of generalization and error convergence. Finally, simulation and experimental examples verify the effectiveness of the proposed strategy using MATLAB and a microswimmer trajectory tracking system.

Automated summary

This article focuses on intelligent servo control using learning from desired demonstrations, where a control policy using the broad learning system (BLS) is developed and combined with Lyapunov theory to derive controller parameters' constraints. The final control policy not only obtains the movement skills of desired demonstrations but also has strong generalization and error convergence abilities.