This paper proposes a novel approach for designing the observer and controller for Takagi-Sugeno (T-S) fuzzy systems with an Enlarged Class of Disturbances. The class of the disturbances in this study is significantly enlarged because they do not need to satisfy the constraints such as bounded, generated from an exogenous system, or finite derivative is equal to zero. First, the state/disturbance observer is proposed to estimate the un-measurable states and disturbances, simultaneously. Then the controller incorporating with the observer is synthesized to attenuate the disturbances completely and stabilize the T-S fuzzy systems. On the basis of the Lyapunov function and linear matrix inequality (LMI) technique, the sufficient conditions for the synthesis of the observer and controller are derived in the main theorems. Finally, the effectiveness of the proposed method is verified via two examples.

In this paper we combine several image processing techniques with the depth images captured by a Kinect sensor to successfully recognize the five distinct human postures of sitting, standing, stooping, kneeling, and lying. The proposed recognition procedure first uses background subtraction on the depth image to extract a silhouette contour of a human. Then, a horizontal projection of the silhouette contour is employed to ascertain whether or not the human is kneeling. If the figure is not kneeling, the star skeleton technique is applied to the silhouette contour to obtain its feature points. We can then use the feature points together with the centre of gravity to calculate the feature vectors and depth values of the body. Next, we input the feature vectors and the depth values into a pre-trained LVQ (learning vector quantization) neural network; the outputs of this will determine the postures of sitting (or standing), stooping, and lying. Lastly, if an output indicates sitting or standing, one further, similar feature identification technique is needed to confirm this output. Based on the results of many experiments, using the proposed method, the rate of successful recognition is higher than 97% in the test data, even though the subjects of the experiments may not have been facing the Kinect sensor and may have had different statures. The proposed method can be called a “hybrid recognition method”, as many techniques are combined in order to achieve a very high recognition rate paired with a very short processing time.

In this study, we propose an assistive system for helping visually impaired people walk outdoors. This assistive system contains an embedded system—Jetson AGX Xavier (manufacture by Nvidia in Santa Clara, CA, USA) and a binocular depth camera—ZED 2 (manufacture by Stereolabs in San Francisco, CA, USA). Based on the CNN neural network FAST-SCNN and the depth map obtained by the ZED 2, the image of the environment in front of the visually impaired user is split into seven equal divisions. A walkability confidence value for each division is computed, and a voice prompt is played to guide the user toward the most appropriate direction such that the visually impaired user can navigate a safe path on the sidewalk, avoid any obstacles, or walk on the crosswalk safely. Furthermore, the obstacle in front of the user is identified by the network YOLOv5s proposed by Jocher, G. et al. Finally, we provided the proposed assistive system to a visually impaired person and experimented around an MRT station in Taiwan. The visually impaired person indicated that the proposed system indeed helped him feel safer when walking outdoors. The experiment also verified that the system could effectively guide the visually impaired person walking safely on the sidewalk and crosswalks.

Observer design for a Takagi–Sugeno (T–S) fuzzy system with uncertainties is extremely difficult because the estimation error is incapable of approaching zero asymptotically due to the existence of uncertainty terms. In this study, by regarding the uncertainty as an unknown input, with some particular derivation, the authors successfully synthesize a fuzzy observer which guarantees that the error will converge to zero asymptotically. Based on Lyapunov theory and linear matrix inequality tools, the main theorem is derived for the fuzzy observer synthesis. This study does not limit the size of uncertainties, but the uncertainties have to satisfy a specific matching condition in order to use the unknown input concept. Finally, a numerical example is given to show that the proposed approach is effective in estimating system's states subject to system uncertainties.

Designing an observer for Takagi–Sugeno (T–S) fuzzy systems in the presence of uncertainties is a great challenge for researchers. Owing to the uncertainties, it is hard to design an observer to make the state estimation error converge to zero asymptotically. To resolve this problem, the authors consider the uncertainties to be unknown inputs and apply the unknown inputs method. The existing literature concerning observer design for T–S fuzzy systems using the unknown inputs method generally have only a single output matrix. In addition, the method of converting uncertainties into unknown inputs requires a matching condition which includes a common constant matrix to match all uncertainties in all local models of T–S fuzzy system. Therefore, the above two constraints lead the observer design to be very conservative. To overcome the above drawbacks, a special transformation is used to transform the original T–S fuzzy system into a new system form, and then they propose a method to synthesise an observer for an uncertain T–S fuzzy system which allows different output matrices and relaxes the matching condition of the uncertainties for all local models. Finally, two examples are presented to demonstrate the efficiency of the proposed method.