Adaptive fixed‐time fault‐tolerant control for rigid spacecraft using a double power reaching law

Abstract

Summary In this paper, an adaptive fixed‐time fault‐tolerant control scheme is presented for rigid spacecraft with inertia uncertainties and external disturbances. By using an inverse trigonometric function, a novel double power reaching law is constructed to speed up the state stabilization and reduce the chattering phenomenon simultaneously. Then, an adaptive fixed‐time fault‐tolerant controller is developed for the spacecraft with the actuator faults, such that the fixed‐time convergence of the attitude and angular velocity could be guaranteed, and no prior knowledge on the upper bound of the lumped uncertainties is required anymore in the controller design. Comparative simulations are provided to illustrate the effectiveness and superior performance of the proposed scheme.

A Repeatable Optimization for Kinematic Energy System with Its Mobile Manipulator Application

“…In Section 3, a universal repeatable optimization of the kinematic energy system is presented and formulated for trajectory planning of mobile manipulators with three wheels. To solve the optimal scheme (24), we use the solution of terminal-time Zhang neural network (TTZNN) approach (15) to calculate the convergent time . By deriving the variableΘ with Lagrangian theory, we get the following timevarying equation: The vector-valued convergent error function is given as…”

“…Miah et al [14] firstly proposed an online optimization algorithm for trajectory moving when the mechanism parameters and measurement error of the nonholonomic differential-drive mobile manipulators were unknown. Tao et al [15] proposed a kind of adaptive neural network model for spacecraft given special tasks in a coordinated control with the consideration of arriving delays and operative uncertainties. In [16], a near-optimal trajectory planning scheme was proposed for receding-wheeled mobile manipulators.…”

“…, Θ(0), x c , y c , z c ,  0 ) J E and each angle Θ via(15) t< T The flowchart of TTZNN model for kinematic planning of the mobile manipulator.d = √ 1 + (2 ) −1 d . By means of integral methods, we obtain the solution of (30):…”

Neural Network Identification and Sliding Mode Control for Hysteresis Nonlinear System with Backlash-Like Model

“…Theorem 2. Considering system (1) which is approximated by the NN of (11), if the identification error is defined as (12), based on the Assumption 1, the identification is semiglobally uniformly ultimately bounded (UUB) and it converges to a small compact set around zero as ‖ ‖ ≤ √2 Proof. Define the Lyapunov function as…”

“…Different from adaptive control [11,12], sliding mode control (SMC) [13] draws more attention in recent years. In some circuit, the hysteresis was utilized by the overshoot of the output voltage response.…”