Neural Network-Based Fixed-Time Control for Flexible Two-link Manipulators with State Constraints and Input Deadzone
Addressing the inherent low stiffness of flexible manipulators, existing control schemes often face an intrinsic contradiction where rapid convergence leads to increased vibration amplitudes, making it challenging to achieve high-precision trajectory tracking while effectively suppressing elastic vibrations. To tackle this issue, this paper proposes a neural network-based Fixed-Time learning control strategy. This strategy is capable of simultaneously handling output constraints, model uncertainties, and input dead-zone nonlinearities of the system. The designed controller effectively compensates for the adverse effects of the input deadzone, ensuring that all system states converge to a small neighborhood around the origin within a fixed time, thereby significantly improving the system’s convergence speed and transient performance. By introducing a logarithmic Barrier Lyapunov Function (BLF), the prescribed tracking error constraints are strictly guaranteed. Furthermore, high-frequency chattering is mitigated through a smooth approximation of the sign function. Experimental results demonstrate that, compared with the PSF controller, the proposed Fixed-Time control scheme reduces the steady-state tracking errors by 61.9% and 69.2%, respectively. In terms of vibration suppression, the steady-state values of elastic vibrations are reduced by 49.2% and 32.6%, respectively. These results fully validate the superiority and robustness of the proposed control strategy in balancing rapid convergence with vibration suppression.