Robust H_∞ control for neutral stochastic uncertain systems with time-varying delay

The problem of robust H_∞ control for uncertain neutral stochastic systems with time-varying delay is discussed.The parameter uncertaintie is assumed to be time varying norm-bounded.First,the stochastic robust stabilization of the stochastic system without disturbance input is investigated by nonlinear matrix inequality method.Then,a full-order stochastic dynamic output feedback controller is designed by solving a bilinear matrix inequality（BMI）,which ensures a prescribed stochastic robust H_∞ performance level for the resulting closed-loop system with nonzero disturbance input and for all admissible uncertainties.An illustrative example is provided to show the feasibility of the controller and the potential of the proposed technique.

Local minima-free design of artificial coordinating fields

In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an artificial coordinating fields (ACF) based method has been proposed recently. This paper deals with the reachability problem of the ACF, that is, how to design and choose the parameters of the ACF and how the environment should be such that the robot can reach its goal without being trapped in local minima. Some sufficient conditions for these purposes are developed theoretically. Theoretical analyses show that, the ACF can effectively remove local minima in dynamic uncertain environments with V-shape or U-shape obstacles, and guide the mobile robot to reach its goal with some necessary environment constraints and based on the methods provided in this paper to properly choose the parameters of the ACF. Comparisons between the ACF and APF, and simulations are provided to illustrate the advantages of the ACF.

On extended state based Kalman filter for nonlinear time-varying uncertain systems with measurement bias

This paper addresses the state estimation for a class of nonlinear time-varying stochastic systems with both uncertain dynam-ics and unknown measurement bias.A novel extended state based Kalman filter(ESKF)algorithm is developed to estimate the original state,the uncertain dynamics and the measurement bias.It is shown that the estimation error of the proposed algorithm is bounded in the mean square sense.Also,the estimation of the measurement bias asymptotically converges to its true value,such that the infuence of measurement bias is eliminated.Furthermore,the asymptotic optimality of the estima-tion result is proved while the uncertain dynamics approaches to a constant vector.Finally,a simulation study for harmonic oscillator system model is provided to ilustrate the effectiveness of proposed method.

Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development

Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.

Design and experimental validation of event-triggered multi-vehicle cooperation in conflicting scenarios

Platoon control is widely studied for coordinating connected and automated vehicles(CAVs)on highways due to its potential for improving traffic throughput and road safety.Inspired by platoon control,the cooperation of multiple CAVs in conflicting scenarios can be greatly simplified by virtual platooning.Vehicle-to-vehicle communication is an essential ingredient in virtual platoon systems.Massive data transmission with limited communication resources incurs inevitable imperfections such as transmission delay and dropped packets.As a result,unnecessary transmission needs to be avoided to establish a reliable wireless network.To this end,an event-triggered robust control method is developed to reduce the use of communication resources while ensuring the stability of the virtual platoon system with time-varying uncertainty.The uniform boundedness,uniform ultimate boundedness,and string stability of the closed-loop system are analytically proved.As for the triggering condition,the uncertainty of the boundary information is considered,so that the threshold can be estimated more reasonably.Simulation and experimental results verify that the proposed method can greatly reduce data transmission while creating multi-vehicle cooperation.The threshold affects the tracking ability and communication burden,and hence an optimization framework for choosing the threshold is worth exploring in future research.

A Robust Tracking Controller for Electrically Driven Robot Manipulators: Stability Analysis and Experiment

In this paper, a robust controller for electrically driven robotic systems is developed. The controller is designed in a backstepping manner. The main features of the controller are: 1) Control strategy is developed at the voltage level and can deal with both mechanical and electrical uncertainties. 2) The proposed control law removes the restriction of previous robust methods on the upper bound of system uncertainties. 3) It also benefits from global asymptotic stability in the Lyapunov sense. It is worth to mention that the proposed controller can be utilized for constrained and nonconstrained robotic systems. The effectiveness of the proposed controller is verified by simulations for a two link robot manipulator and a four-bar linkage. In addition to simulation results,experimental results on a two link serial manipulator are included to demonstrate the performance of the proposed controller in tracking a given trajectory.