Randomized Algorithms for Probabilistic Optimal Robust Performance Controller Design

Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.

Robust Performance of AlGaN-Channel Metal-Insulator-Semiconductor High-Electron-Mobility Transistors at High Temperatures

Superior characteristics of Al Ga N-channel metal-insulator-semiconductor（MIS） high electron mobility transistors（HEMTs） at high temperatures are demonstrated in detail. The temperature coefficient of the maximum saturation drain current for the Al GaN-channel MIS HEMT can be reduced by 50% compared with the Ga N-channel HEMT. Moreover, benefiting from the better suppression of gate current and reduced leakage current in the buffer layer, the Al Ga N-channel MIS HEMT demonstrates an average breakdown electric field of 1.83 MV/cm at25℃ and 1.06 MV/cm at 300℃, which is almost 2 times and 3 times respectively larger than that of the reference Ga N-channel HEMT. Pulsed mode analyses suggest that the proposed device suffers from smaller current collapse when the temperature reaches as high as 300℃.

Vehicle Active Steering Control Research Based on Two-DOF Robust Internal Model Control

Because of vehicle＇s external disturbances and model uncertainties,robust control algorithms have obtained popularity in vehicle stability control.The robust control usually gives up performance in order to guarantee the robustness of the control algorithm,therefore an improved robust internal model control（IMC） algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness.The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties.In order to separate the design process of model tracking from the robustness design process,the improved 2 degree of freedom（DOF） robust internal model controller structure is given from the standard Youla parameterization.Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm,on the basis of a nonlinear vehicle simulation model with a magic tyre model.Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance,which can enhance the vehicle stability and handling,regardless of variations of the vehicle model parameters and the external crosswind interferences.Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

H∞ Robust Control System of a High-Purity Distillation Column

This paper presents the design of a robust control system for a high-purity distillation column. It is concerned with the design of a two degree-of-freedom (2DOF) product-composition controller for a high-purity distillation column. The H∞ optimization problem is set up to ensure a guaranteed level of robust stability, robust disturbance attenuation and robust reference tracking performance.

Science Letters:A robust polysilicon-assisted SCR in ESD protection application

A novel polysilicon-assisted silicon-controlled rectifier （SCR） is presented and analyzed in this paper, which is fabricated in HHNEC＇s 0.18μm EEPROM process. The polysilicon-assisted SCRs take advantage of polysilicon layer to help bypass electro-static discharge （E S D） current without occupying extra layout area. TLP current-voltage （I-V） measurement results show that given the same layout areas, robustness performance of polysilicon-assisted SCRs can be improved to 3 times of conventional MLSCR＇s. Moreover, one-finger such polysilicon-assisted SCRs, which occupy only 947 [3mz layout area, can undergo 7-kV HBM ESD stress. Results further demonstrate that the S-type I-V characteristics of polysilicon-assisted SCRs are adjustable to different operating conditions by changing the device dimensions. Compared with traditional SCRs, this new SCR can bypass more ESD currents and consumes smaller IC area.

Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.

Forward robust portfolio selection: The binomial case

We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model ambiguity set incrementally in time while,also,updating his risk preferences forward in time.This dynamic alignment of preferences and ambiguity updating results in time-consistent policies and provides a richer,more accurate learning setting.For each investment period,the investor solves a worst-case portfolio optimization over possible market models,which are represented via a Wasserstein neighborhood centered at a binomial distribution.Duality methods from Gao and Kleywegt[10];Blanchet and Murthy[8]are used to solve the optimization problem over a suitable set of measures,yielding an explicit optimal portfolio in the linear case.We analyze the case of linear and quadratic utilities,and provide numerical results.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

Optimal investment and consumption with forward preferences and uncertain parameters

We introduce and analyze a class of forward performance criteria in incomplete markets in the presence of model ambiguity.Incompleteness stems from general investment constraints,while model uncertainty is represented by a convex and compact set of plausible model parameter processes.Following the max-min criteria in traditional(backward)robust control,we formulate similar criteria for the robust forward performance processes and focus on the rich class of time-monotone processes.We provide a novel PDE characterization and a semi-explicit saddle-point construction of the robust forward performance criteria and their optimal policies.Furthermore,we present additional results within the class of homothetic constant relative risk aversion(CRRA)processes.Within this class,we investigate the relationship between forward performance processes on wealth and those on consumption,establishing an interesting dominance through time.

Reliability and Incentive of Performance Assessment for Decentralized Clouds

Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing demands.As any user or enterprise can be the cloud provider in the decentralized cloud,the performance assessment of the heterogeneous computing resources is of vital significance.However,with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric,the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized cloud.In this paper,we present a robust performance assessment solution RODE to solve this problem.RODE mainly consists of a performance monitoring mechanism and an assessment of the claimed performance(AoCP)mechanism.The performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud providers.Based on the performance monitoring results,the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as claimed.Via extensive experiments,we show RODE can accurately monitor the performance of cloud providers on the premise of reliability,and incentivize cloud providers to honestly present the performance information and maintain the performance stability.