6 research outputs found
Adaptive Fractional-Order Sliding Mode Controller with Neural Network Compensator for an Ultrasonic Motor
Ultrasonic motors (USMs) are commonly used in aerospace, robotics, and
medical devices, where fast and precise motion is needed. Remarkably, sliding
mode controller (SMC) is an effective controller to achieve precision motion
control of the USMs. To improve the tracking accuracy and lower the chattering
in the SMC, the fractional-order calculus is introduced in the design of an
adaptive SMC in this paper, namely, adaptive fractional-order SMC (AFOSMC), in
which the bound of the uncertainty existing in the USMs is estimated by a
designed adaptive law. Additionally, a short memory principle is employed to
overcome the difficulty of implementing the fractional-order calculus on a
practical system in real-time. Here, the short memory principle may increase
the tracking errors because some information is lost during its operation.
Thus, a compensator according to the framework of Bellman's optimal control
theory is proposed so that the residual errors caused by the short memory
principle can be attenuated. Lastly, experiments on a USM are conducted, which
comparative results verify the performance of the designed controller.Comment: 9 pages, 9 figure
Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints
In the recent literature, significant and substantial efforts have been
dedicated to the important area of multi-agent decision-making problems.
Particularly here, the model predictive control (MPC) methodology has
demonstrated its effectiveness in various applications, such as mobile robots,
unmanned vehicles, and drones. Nevertheless, in many specific scenarios
involving the MPC methodology, accurate and effective system identification is
a commonly encountered challenge. As a consequence, the overall system
performance could be significantly weakened in outcome when the traditional MPC
algorithm is adopted under such circumstances. To cater to this rather major
shortcoming, this paper investigates an alternate data-driven approach to solve
the multi-agent decision-making problem. Utilizing an innovative modified
methodology with suitable closed-loop input/output measurements that comply
with the appropriate persistency of excitation condition, a non-parametric
predictive model is suitably constructed. This non-parametric predictive model
approach in the work here attains the key advantage of alleviating the rather
heavy computational burden encountered in the optimization procedures typical
in alternative methodologies requiring open-loop input/output measurement data
collection and parametric system identification. Then with a conservative
approximation of probabilistic chance constraints for the MPC problem, a
resulting deterministic optimization problem is formulated and solved
efficiently and effectively. In the work here, this intuitive data-driven
approach is also shown to preserve good robustness properties. Finally, a
multi-drone system is used to demonstrate the practical appeal and highly
effective outcome of this promising development in achieving very good system
performance.Comment: 10 pages, 6 figure