The electro-hydraulic servo system (EHSS) usually faces multiple sensor faults and disturbances, which is difficult to achieve good tracking control, reliability, and stability control. In this article, an advanced fault-tolerant controller is proposed for an EHSS to deal with the above challenge. The three fault observers, called nonlinear unknown input observers (NUIOs), are developed to effectively estimate the position, velocity, pressure sensor faults and the system states. The fault detection, estimation, and isolation are then presented as effective for multiple sensors failure at a time. The first NUIO for position sensor fault is utilized for the tracking control, while the other NUIOs are used for alarm proposal. The adverse effects caused by the matched and unmatched disturbances are eliminated by two extended state observers (ESOs). In addition, to avoid the “explosion of complexity” when computing the derivatives of virtual control laws, the dynamic surface control is applied to design the fault-tolerant control (FTC) scheme. The Lyapunov principle ensures system stability under lumped disturbance and faulty conditions. Finally, simulation studies and evaluation results are performed to demonstrate the validity of the proposed FTC algorithm.

In this paper, a novel adaptive fault-tolerant controller is proposed for a typical electro-hydraulic rotary actuator in the presence of disturbances, internal leakage fault, and sensor fault simultaneously. To construct the suggested controller, a nonlinear unknown input observer is developed to effectively identify the sensor fault, which is unaffected by not only internal leakage fault but also mismatched disturbances/uncertainties. Furthermore, a radial basis function neural network is designed to compensate for the mismatched disturbances/uncertainties caused by payload variation and unknown friction nonlinearities. Besides, an adaptive law based on the projection mapping function is applied to tackle the effect of the internal leakage fault. The integration of the above-mentioned techniques into the adaptive backstepping terminal sliding mode is investigated to obtain high tracking performance, robustness as well as fast convergence. The stability of the closed-loop system is proven by the Lyapunov theory. Finally, the capability and effectiveness of the proposed approach are validated via simulation results under various faulty scenarios.

This article proposes a novel fault-tolerant controller for a double-rod electro-hydraulic actuator whilst the motion control system faces with system disturbances/uncertainties and internal leakage fault. Firstly, taking the advantage of the coordinate transformation, the nonlinear system is converted to a linear system to apply the control design tools in linear control theory. Besides, the matched, mismatched disturbances, and internal leakage fault are integrated into a new lumped uncertainty based on this transformation. Inspired by the great capability of time delay estimation technique, the suggested controller is developed to effectively detect and compensate for the internal leakage fault. To enhance the performance of the control system, an adaptive integral sliding mode control approach is deployed to effectively suppress the lump estimated error, and the effects of fault. The perfect combination of input-output feedback linearization, adaptive integral sliding mode, and time delay estimation is investigated to achieve high-precision tracking control and strong robustness in the presence of matched, mismatched disturbances, and faults, simultaneously. Moreover, the global stability of the suggested control algorithm is demonstrated by the Lyapunov theory. Finally, several tracking performance comparisons of the proposed approach with the existing controllers to demonstrate the efficiency are exhibited through simulation analyses and experiment results.

In this paper, a disturbance observer‐based adaptive neural backstepping integral sliding mode control (BISMC) is developed for a flexible joint robot (FJR) with the integration of an adjustable stiffness rotary actuator (ASRA). This system suffers from unknown system dynamics, external disturbance, and the influence of variable stiffness, which is a challenge for achieving precision tracking performance. Considering the lumped disturbances in FJR generated by the hydraulic system and the stiffness modulation of the ASRA, we investigate the structural dynamics nonlinear model of the FJR system, including hydraulic actuator dynamics. While other linear control strategies are applied for the FJR, the proposed controller uses BISMC, neural networks (NN), and nonlinear disturbance observers to deal with the disadvantages mentioned above. Radial basis function neural networks (RBFNN) are designed to tackle unknown nonlinear functions, and the disturbance observers are introduced to compensate for the influence of the variable stiffness, disturbance, and the approximation error caused by NN. Simulations and experiments are independently implemented to demonstrate the effectiveness and feasibility of the proposed controller. Results exhibit that the integral absolute error‐index is reduced by 20.4% when the proposed method is deployed for the experiment with a multistep trajectory.