Showing papers by "Ching-Chih Tsai published in 2018"

Quaternion-Based Adaptive Backstepping RFWNN Control of Quadrotors Subject to Model Uncertainties and Disturbances

Abstract: This paper presents a quaternion-based adaptive backstepping control method using recurrent fuzzy wavelet neural network (RFWNN) for regulation and trajectory tracking of quadrotors subject to model uncertainties and disturbances. For the controller synthesis, a more complete model of an uncertain quadrotor is first obtained by incorporating with mass variations and wind disturbances, which are online learned by using the RFWNN. Afterward, a quaternion-based adaptive backstepping RFWNN controller is synthesized by integrating backstepping, quaternion control, and the RFWNN online learner. The closed-loop stability of the overall quadrotor control system is shown semi-globally uniformly ultimately bounded via Lyapunov stability theory. The effectiveness and performance of the proposed control method are well exemplified by conducting four simulations on hovering and three-dimensional sinusoidal trajectory tracking control of a quadrotor. Through the simulation results, the proposed control method is shown superior by comparing to two existing methods.

Integral Terminal Sliding-Mode Formation Control for Uncertain Heterogeneous Networked Mecanum-Wheeled Omnidirectional Robots

Abstract: In this paper, an integral terminal sliding-mode (ITSM) consensus control method is presented for a team of uncertain, networked, heterogeneous Mecanum-wheeled omnidirectional robots (MWORs) moving together in formation. After the description of the dynamic model of each kind of MWOR by a set of unified multivariable vector state equations, the interconnection structure of the multi-robots is modeled by a directed and strongly connected graph. An ITSM formation control is proposed to achieve finite-time formation control and trajectory tracking in presence of robot uncertainties. The usefulness and superiority of the proposed method are well exemplified by conducting three computer simulations on cooperative formation of four heterogeneous MWORs with a virtual leader.

A TOPSIS Multi-Criteria Decision Method-Based Function-Link Fuzzy CMAC Control System Design for Uncertain Nonlinear Systems

Abstract: This paper presents a more efficient control algorithm for selecting suitable firing nodes and removing unimportant input variables automatically to improve the computational efficiency, reduce the number of firing rules and achieve good performance for the nonlinear control systems. A novel function-link fuzzy cerebellar model articulation controller (FLFC) is designed for uncertain nonlinear systems based on a multiple attribute decision-marking method known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed control system is comprised a TOPSIS FLFC (TFLFC) and a robust compensator. The parameters of the proposed TFLFC are tuned online using the adaptation laws that are derived from a Lyapunov stability theorem, so the system's stability is guaranteed. Finally, the proposed control system is used to control an inverted pendulum system and a master-slave Duffing-Holmes chaotic system to illustrate its favorable control performance.