This article proposes a triangular canonical form for a class of 0-flat nonlinear systems. Necessary and sufficient geometrical conditions are given in order to guarantee the existence of a local diffeomorphism to transform the studied nonlinear systems into the proposed 0-flat canonical form, which enables us to compute the flat output as well.

In this letter, we address the challenge of sensor fusion in Soft Robotics for estimating forces and deformations. In the context of intrinsic sensing, we propose the use of soft capacitive sensing to find a contact's location, and the use of pneumatic sensing to estimate the force intensity and the deformation. Using a FEM-based numerical approach, we integrate both sensing streams and model two Soft Robotics devices we have conceived. These devices are a Soft Pad and a Soft Finger. We show in an evaluation that external forces on the Soft Pad can be estimated and that the shape of the Soft Finger can be reconstructed.

This note proposes the event-triggered state-feedback and dynamic output-feedback control of positive Markovian jump systems with intermittent actuator and sensor faults. An independent Markovian process is used to establish the mathematical model of the intermittent fault. An event-triggering condition based on 1-norm is proposed to implement the event-triggered scheme. An event-triggered state-feedback controller is designed using stochastic co-positive Lyapunov functions. By virtue of a matrix decomposition approach, an event-triggered dynamic output-feedback controller is proposed for the systems. Under the designed controllers, the augmented system consisting of the original system and the controller system is positive and stochastic l1 stable. Finally, one example is provided to verify the validity of the proposed design.

This paper is concerned with the design of the distributed prescribed-time unknown input observer (DPTUIO) for a class of linear time-invariant (LTI) systems, which we refer to as the target systems. Results on distributed unknown input observers in the literature did not address the convergence time predefinition problem. To achieve fast estimation, this paper uses weakly unobservable subspace decomposition and introduces a time-scale transformation technology to develop a novel DPTUIO. This design ensures that, for each node, the entire state of the target system can be estimated within a pre-assigned time, despite each node measuring only a small portion of the system output. Detailed discussions on the existence conditions of the DPTUIO are provided. It is established that the observer matching condition, along with the jointly strongly observability condition and a strongly connected communication network, constitutes sufficient conditions for the existence of the DPTUIO. Moreover, the robustness of the developed DPTUIO against measurement and communication noise is analyzed in detail with rigorous deduction. Finally, a practical example is used to verify the effectiveness of the proposed methods.

Recently soft robotics has rapidly become a novel and promising area of research with many designs and applications due to their flexible and compliant structure. However, it is more difficult to derive the nonlinear dynamic model of such soft robots. The differential kinematics and dynamics of the soft manipulator can be formulated as a set of highly nonlinear partial differential equations (PDEs) via the classic Cosserat rod theory. In this work, we propose a discrete modeling technique named piecewise linear strain (PLS) to solve the PDEs of Cosserat-based models, based on which the associated analytic models are deduced. To validate the accuracy of the proposed Cosserat model, the static model of the conical cantilever rod under gravity as a simple example is simulated by using different discretization methods. Results indicate that PLS Cosserat model is comparable to the mechanical deformation behavior of a real-world soft manipulator. Finally, a parameters identification scheme for this model is established, and the simulation as well as experimental validation demonstrate that using this method can identify the model physical parameters with high accuracy.