Visual servoing systems provide higher safety and autonomy when dealing with unstructured environments. Visual and force sensing are essential for accurate and reliable robotic manipulation. In this article, a novel image-based visual-admittance control with prescribed performance of manipulator is presented. The method couples visual and force information in feature space, which avoids control limitations due to inconsistent driver layers and improves system flexibility. Thanks to the prescribed performance and the tan-type barrier Lyapunov function, the image feature error is satisfied under the finite field-of-view constraint, which improves the transient and steady-state performance of the visual servoing system. The proposed image-based visual-admittance control with prescribed performance is experimentally validated. The results show that the control framework improves the transient/steady-state response and task success of the system.

This paper is concerned with the problem of fault tolerant control for non‐linear systems with sensor faults based on the T‐S fuzzy model approach. First, a fault estimation (FE) observer with $L_{\infty }$ technique is designed to estimate the fault. Second, by using the fault estimation information, which are provided by the fault estimation observer, a novel fault tolerant control scheme with a fault compensation mechanism in the premise part of the fuzzy controller is presented to compensate the effect of the fault and stabilize the system. Different from the existing methods, where the fault compensation mechanism is only established in the consequent part of the controller, in the presented method, the fault compensation mechanism is established in the consequent part as well as the premise part of the fuzzy controller. Then, by using the fault compensation mechanism, the bound of the deviation between the normalized membership functions of the fuzzy model and those of the controller can be reduced such that the fault tolerant control design can be relaxed. In the end, an example is given to illustrate the validity of the developed fault tolerant control scheme.

The design of tracking control based on membership function-dependent L _∞ performance for the systems, which are described by the Takagi-Sugeno fuzzy models, is considered. In practice, most Takagi-Sugeno fuzzy systems have such a characteristic that they work on some local subsystems most of the time and on others less time. Taking advantage of this feature, an L _∞ performance index dependent on membership function is proposed, through which better performance may be guaranteed for those local subsystems that work most of the time. By means of the newly defined L _∞ performance index, the measurable premise variables, the estimation of immeasurable premise variables, and the estimation of nonlinear function, an observer-based tracking controller is designed in the form of linear matrix inequalities, which makes full use of the information of measurable premise variables. Finally, an example is provided to verify the effectiveness of the proposed approach. In the simulation, compared with the traditional L _∞ control method, the novel method has better robust tracking performance.

This paper studies the design problem of fault detection (FD) observer for unmanned marine vehicles (UMVs) based on the T-S fuzzy model. Firstly, T-S fuzzy systems are used to approximate the nonlinear dynamics in UMVs. Secondly, to improve the FD performance of UMVs, a new $H_{\infty }/H\_$ performance index, which depends on the membership functions, is defined. Then, based on the membership-function-dependent $H_{\infty }/H\_$ performance index, a new fuzzy FD observer strategy, where the fuzzy submodels are not all required to be with the same $H\_$ performance index, is developed to detect the sensor fault in UMVs; the corresponding synthesis conditions of the FD observer are derived based on the Lyapunov theory. Different from the conventional FD strategies, in the proposed membership-function-dependent FD method, the fuzzy submodels—which the system always works on—can have a larger $H\_$ performance index, such that the performance of the FD can be improved. In the end, an example is given to show the effectiveness of the presented method.