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… Show more
“…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…”
Section: Terminal-time Znn Approachmentioning
“…Miah et al  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  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 , 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):…”
For repeatable motion of redundant mobile manipulators, the flexible base platform and the redundant manipulator have to be returned to the desired initial position simultaneously after completing the given tasks. To remedy deviations between initial position and desired position of each kinematic joint angle, a special kind of repeatable optimization for kinematic energy minimization based on terminal-time Zhang neural network (TTZNN) with finite-time convergence is proposed for inverse kinematics of mobile manipulators. It takes the advantages that each joint of the manipulator is required to return to the desired initial position not considering the initial orientation of itself for realizing repeatable kinematics control. Unlike the existed training methods, such an optimization of kinematic energy scheme based on TTZNN can not only reduce the convergent position error of each joint to zero in finite time, but also improve the convergent precision. Theoretical analysis and verifications show that the proposed optimal kinematic energy scheme accelerates the convergent rate, which is tended to be applied in practical robot kinematics. Simulation results on the manipulator with three mobile wheels substantiate the timeliness and repetitiveness of the proposed optimization scheme.
“…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…”
Section: Nn Identification Designmentioning
“…Different from adaptive control [11,12], sliding mode control (SMC)  draws more attention in recent years. In some circuit, the hysteresis was utilized by the overshoot of the output voltage response.…”
A new neural network sliding mode control (NNSMC) is proposed for backlash-like hysteresis nonlinear system in this paper. Firstly, only one neural network is designed to estimate the unknown system states and hysteresis section instead of multiscale neural network at former researches since that can save computation and simplify the controller design. Secondly, a new NNSMC is proposed for the hysteresis nonlinearity where it does not need tracking error transformation. Finally, the Lyapunov functions are adopted to guarantee the stabilities of the identification and control strategies semiglobally uniformly ultimately bounded (UUB). Two cases simulations are proved the effectiveness of the presented identification approach and the performance of the NNSMC.
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