A New Strategy for Obesity Treatment: Revealing the Frontiers of Anti-obesity Medications.

Obesity dramatically increases the risk of type 2 diabetes, fatty liver, hypertension, cardiovascular disease, and cancer, causing both declines in quality of life and life expectancy, which is a serious worldwide epidemic. At present, more and more patients with obesity are choosing drug therapy. However, given the high failure rate, high cost, and long design and testing process for discovering and developing new anti-obesity drugs, drug repurposing could be an innovative method and opportunity to broaden and improve pharmacological tools in this context. Because different diseases share molecular pathways and targets in the cells, anti-obesity drugs discovered in other fields are a viable option for treating obesity. Recently, some drugs initially developed for other diseases, such as treating diabetes, tumors, depression, alcoholism, erectile dysfunction, and Parkinson's disease, have been found to exert potential anti-obesity effects, which provides another treatment prospect. In this review, we will discuss the potential benefits and barriers associated with these drugs being used as obesity medications by focusing on their mechanisms of action when treating obesity. This could be a viable strategy for treating obesity as a significant advance in human health.

Dynamic Event-Based Adaptive Fixed-Time Control for Uncertain Strict-Feedback Nonlinear Systems With State Constraints.

In this article, the event-triggered fixed-time tracking control is investigated for uncertain strict-feedback nonlinear systems involving state constraints. By employing the universal transformed function (UTF) and coordinate transformation techniques into backstepping design procedure, the proposed control scheme ensures that all states are constrained within the time-varying asymmetric boundaries, and meanwhile, the undesired feasibility condition existing in other constrained controllers can be removed elegantly. Different from the existing static event-triggered mechanism, a dynamic event-triggered mechanism (DETM) is devised via constructing a novel dynamic function, so that the communication burden from the controller to actuator is further alleviated. Furthermore, with the aid of adaptive neural network (NN) technique and generalized first-order filter, together with Lyapunov theory, it is proved that the states of closed-loop system converge to small regions around zero with fixed-time convergence rate. The simulation results confirm the benefits of developed scheme.

Error analysis and realization of a phase-modulated diffraction grating used as a displacement sensor.

A grating-based interferometric cavity produces coherent diffraction light field in a compact size, serving as a promising candidate for displacement measurement by taking advantage of both high integration and high accuracy. Phase-modulated diffraction gratings (PMDGs) make use of a combination of diffractive optical elements, allowing for the diminishment of zeroth-order reflected beams and thus improving the energy utilization coefficient and sensitivity of grating-based displacement measurements. However, conventional PMDGs with submicron-scale features usually require demanding micromachining processes, posing a significant challenge to manufacturability. Involving a four-region PMDG, this paper establishes a hybrid error model including etching error and coating error, thus providing a quantitative analysis of the relation between the errors and optical responses. The hybrid error model and the designated process-tolerant grating are experimentally verified by micromachining and grating-based displacement measurements using an 850 nm laser, confirming the validity and effectiveness. It is found the PMDG achieves an energy utilization coefficient (the ratio of the peak-to-peak value of the ±1st order beams to the 0th-order beam) improvement of nearly 500% and a four-fold reduction in 0th-order beam intensity compared with the traditional amplitude grating. More importantly, this PMDG maintains very tolerant process requirements, and the etching error and coating error can be up to 0.5 µm and 0.6 µm, respectively. This offers attractive alternatives to the fabrication of PMDGs and grating-based devices with wide process compatibility. This work first systematically investigates the influence of fabrication errors and identifies the interplay between the errors and the optical response for PMDGs. The hybrid error model allows further avenues for the fabrication of diffraction elements with practical limitations of micromachining fabrication.

Stochastic Optimal Control for Robot Manipulation Skill Learning Under Time-Varying Uncertain Environment.

In this article, a novel stochastic optimal control method is developed for robot manipulator interacting with a time-varying uncertain environment. The unknown environment model is described as a nonlinear system with time-varying parameters as well as stochastic information, which is learned via the Gaussian process regression (GPR) method as the external dynamics. Integrating the learned external dynamics as well as the stochastic uncertainties, the complete interaction system dynamics are obtained. Then the iterative linear quadratic Gaussian with learned external dynamics (ILQG-LEDs) method is presented to obtain the optimal manipulation control parameters, namely, the feedforward force, the reference trajectory, as well as the impedance parameters, subject to time-varying environment dynamics. The comparative simulation studies verify the advantages of the presented method, and the experimental studies of the peg-hole-insertion task prove that this method can deal with complex manipulation tasks.

Discrete-time practical robotic control for human-robot interaction with state constraint and sensorless force estimation.

Employing a continuous-time control algorithm to control the practical system based on discrete-time digital computer will lead to the cost of performance degeneration. To address this issue, this paper proposes a discrete-time barrier Lyapunov function based controller for human-robot interaction in constrained task space to guarantee control performance. The Euler discrete-time stability of closed-loop system controlled by the proposed method is proved, and a feasible difference scheme to support the stability analysis is uncovered based on monotonic scaling. The parameter dependence of this study is well discussed, which involves sample interval and preset boundary of state constraints, and based on the architecture of barrier Lyapunov function, the dependence relationship is demonstrated by using analytical synthesis technique. With a certain sample interval, the proposal of controller parameters is qualified to guarantee that end-effector states are constrained with preset boundary. The discrete-time neural network estimation is designed to approximate the human being's behavior to rebuild the reference trajectory from the desired trajectory and impedance for smoothing the human-robot interaction. Controlled discrete-time states and estimated force are uniformly ultimately bounded, and the convergence vicinity around the origin is proven to be determined by sample interval, lumped uncertainty and preset boundary of state constraints. Numerical simulation and experimental results verify the effectiveness of proposed discrete-time barrier Lyapunov function based methods.

Stable Spinning Deployment Control of a Triangle Tethered Formation System.

The tethered formation system has been widely studied due to its extensive use in aerospace engineering, such as Earth observation, orbital location, and deep space exploration. The deployment of such a multitethered system is a problem because of the oscillations and complex formation maintenance caused by the space tether's elasticity and flexibility. In this article, a triangle tethered formation system is modeled, and an exact stable condition for the system's maintaining is carefully analyzed, which is given as the desired trajectories; then, a new control scheme is designed for its spinning deployment and stable maintenance. In the proposed scheme, a novel second-order sliding mode controller is given with a designed nonsingular sliding-variable. Based on the theoretical proof, the addressed sliding variable from the arbitrary initial condition can converge to the manifold in finite time, and then sliding to the equilibrium in finite time as well. The simulation results show that compared with classic second sliding-mode control, the proposed scheme can speed up the convergence of the states and sliding variables.

Adaptive Neural-Network Controller for an Uncertain Rigid Manipulator With Input Saturation and Full-Order State Constraint.

This article proposes an adaptive neural-network control scheme for a rigid manipulator with input saturation, full-order state constraint, and unmodeled dynamics. An adaptive law is presented to reduce the adverse effect arising from input saturation based on a multiply operation solution, and the adaptive law is capable of converging to the specified ratio of the desired input to the saturation boundary while the closed-loop system stabilizes. The neural network is implemented to approximate the unmodeled dynamics. Moreover, the barrier Lyapunov function methodology is utilized to guarantee the assumption that the control system works to constrain the input and full-order states. It is proved that all states of the closed-loop system are uniformly ultimately bounded with the presented constraints under input saturation. Simulation results verify the stability analyses on input saturation and full-order state constraint, which are coincident with the preset boundaries.

Fixed-time consensus tracking control with connectivity preservation for strict-feedback nonlinear multi-agent systems.

This paper deliberates fixed-time consensus tracking control for strict-feedback nonlinear multi-agent systems with limited communication/sensing range constraints. First, both potential function and coordinate error transformation surface are designed to make the constraints implicit. Next, based on the synthesis of neural network and adaptive technology, the fixed-time virtual variable is proposed without the upper bounds of estimation errors and disturbances. Then, a fixed-time distributed consensus tracking protocol is designed under backstepping method with a fixed-time differentiator to avoid singularity. Lyapunov stability analysis demonstrates that the closed-loop system under the designed control strategy can accomplish the convergence within fixed time, simultaneously connectivity preservation can be guaranteed. Finally, numerical emulation corroborates the availability of the designed control strategy.

A TSR Visual Servoing System Based on a Novel Dynamic Template Matching Method.

The so-called Tethered Space Robot (TSR) is a novel active space debris removal system. To solve its problem of non-cooperative target recognition during short-distance rendezvous events, this paper presents a framework for a real-time visual servoing system using non-calibrated monocular-CMOS (Complementary Metal Oxide Semiconductor). When a small template is used for matching with a large scene, it always leads to mismatches, so a novel template matching algorithm to solve the problem is presented. Firstly, the novel matching algorithm uses a hollow annulus structure according to a FAST (Features from Accelerated Segment) algorithm and makes the method be rotation-invariant. Furthermore, the accumulative deviation can be decreased by the hollow structure. The matching function is composed of grey and gradient differences between template and object image, which help it reduce the effects of illumination and noises. Then, a dynamic template update strategy is designed to avoid tracking failures brought about by wrong matching or occlusion. Finally, the system synthesizes the least square integrated predictor, realizing tracking online in complex circumstances. The results of ground experiments show that the proposed algorithm can decrease the need for sophisticated computation and improves matching accuracy.

SVM-based learning control of space robots in capturing operation.

In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.