Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

Abstract

We develop a new robust control scheme for a non-holonomic spherical robot. To this end, the mathematical model of a pendulum driven non-holonomic spherical robot is first presented. Then, a recurrent neural network-based robust nonsingular sliding mode control is proposed for stabilization and tracking control of the system. The designed recurrent neural network is applied to approximate compound disturbances, including external interferences and dynamic uncertainties. Moreover, the controller is designed in a way that avoids the singularity problem in the system. Another advantage of the proposed scheme is its ability for tracking control while there exists control input saturation, which is a serious concern in robotic systems. Based on the Lyapunov theorem, the stability of the closed-loop system has also been confirmed. Lastly, the performance of the proposed control technique for the uncertain system in the presence of an external disturbance, unknown input saturation, and dynamic uncertainties has been investigated. Also, the proposed controller has been compared with a Fuzzy-PID one. Simulation results show the effectiveness and superiority of the developed control technique.

Performance Optimization of the Helical Heat Exchanger With Turbulator

“…The represented multi-disciplinary design optimization (MDO) method is taken to propose the best performance of the mentioned helical heat exchanger for the equilateral triangular Turbolator. Genetic Algorithm (GA) and Artificial Neural Network (ANN) (Chen et al, 2020a;Chen et al, 2020b) are adopted to propose the best pitch turn of the above represented equilateral Turbolator. This design flow is constructed based on the parametrization of the pitch turning value, optimization algorithm, and a surrogate model on CFD results.…”

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

“…Furthermore, its closed spherical shell can aid in protecting the inside electrical system and mechanical framework from collision and damage [5]. However, despite its advantages, the spherical robot is difficult to control due to its non-holonomic, nonlinear and under-actuated characteristics [6].…”

Sliding Mode Control With Limits on the Control Signal and Linear State Combinations

“…It is also worth to notice that the presented control strategy does not require any additional computer tuning in comparison to some advanced methods utilizing e.g. neural networks [38], [39].…”