Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Abstract

An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.

Adaptive second-order backstepping control for a class of 2DoF underactuated systems with input saturation and uncertain disturbances

“…For a class of 2DoF underactuated systems, some control attempts have been performed, such as continuous higher-order sliding-mode-control 12 , dynamic surface control 17 , RISE control 31 , in which the input saturation, external interference, inertial parameter uncertainty are not considered. For the TORA, which belongs to underactuated systems, a command filter controller is proposed in our research 32 and the bounded torques are replaced by the hyperbolic tangent function input 33 .…”

Disturbance Compensation-Based Optimal Tracking Control for Perturbed Nonlinear Systems

“…In recent years, tracking control for nonlinear systems has always been investigated for many real-world plants in practices, such as unmanned aerial vehicles [1], [2], robots [3], and quadrotors [4], [5]. To realize the performance of tracking, massive various control algorithms are employed for tracking control problems, including backstepping control [6], sliding model control [7], [8] and model predictive control [9]. In fact, it is inevitable that the modeling error and external disturbance exist in practical applications.…”