Adaptive Fuzzy Sliding Mode based on Model Predictive Control in UAV’s Robot using Optimized Deep Learning Approach

Nowadays, Unmanned Aerial Vehicles (UAVs)-Robots use in many area. Flight control after take-off is one of the main issue of UAVs-Robots in recent years. So proposing an optimal controller is so essential. Sliding Model controller is one of the controller and due to some internal and external disturbance such as noises and wind speed, considering uncertainty mode is important in adaptive mode. Also using an observer can reject these disturbances. In the other side, to satisfying some parameters, Model Predictive Control (MPC) considered. Combining observer with MPC can reject maximum disturbances. To tuning adaptive fuzzy sliding mode controller in observed-based MPC, a deep learning method applied in controller which is Deep Spiking Neural Network (DSNN). So, AFSM-DSNN (adaptive fuzzy sliding mode-deep spiking neural network) controller proposed for an UAV-Robot in flight mode with the maximum rejection of disturbances in robust forms. The stability and reliability of UAV-Robot modeled with Lyapunov equations which our model is three degree of freedom (۳DoF). At the end, we use some performance measurement for evaluation criteria to guarantee the obtained results. We determine overshoot, undershoot, setting time, Sum Squared Error (SSE), Integral of Absolute Error (IAE), Integral of Square Error (ISE), and Time-weighted Absolute Error (ITAE) in additional load and drop load parts. That experimental results represent that we optimized about ۰.۹ % and ۴% for overshoot, ۱.۲% and ۰.۳% for overshoot, ۲.۹% and ۳.۶% for setting time, ۰.۲% and ۲.۲% for SSE, ۰.۲۳% and ۰.۲۲% for IAE, ۱.۶% and ۱.۶% for ISE, and also ۴.۵% and ۴.۳% for ITAE In comparison to adaptive fuzzy sliding mode and MPC controller. Also two parameters of velocity tracking and control input in ۱۵۰ seconds tested in simulation which proposed controller optimized ۲% and ۰.۵%, respectively.

کلیدواژه ها:

Unmanned Aerial Vehicles (UAVs) ، Robot ، Adaptive Fuzzy Sliding Mode ، Model Predictive Control (MPC) ، Observer ، Deep Spiking Neural Network (DSNN)