Robust self-triggered MPC with fast convergence for constrained linear systems

In this paper, a robust self-triggered model predictive control (MPC) scheme is proposed for linear discrete-time systems subject to additive disturbances, state and control constraints. To reduce the amount of computation on controller sides, MPC optimization problems are only solved at certain sampling instants which are determined by a novel self-triggering mechanism. The main idea of the self-triggering mechanism is to choose inter-sampling times by guaranteeing a fast decrease in optimal costs. It implies a fast convergence of system states to a compact set where it is ultimately bounded and a reduction of computation times to stabilize the system. Once the state enters a terminal region, the system can be stabilized to a robust invariant set by a state feedback controller. Robust constraint satisfaction is ensured by utilizing the worst-case set-valued predictions of future states in such a way that recursive feasibility is guaranteed for all possible realisations of disturbances. In the case where a priority is given to reducing communication costs rather than improvement in control performance in a neighborhood of the origin, a feedback control law based on nominal state predictions is designed in the terminal region to avoid frequent feedback. Performances of the closed-loop system are demonstrated by a simulation example.     

Markov parameters identification and adaptive iterative learning control for linear discrete-time MIMO systems with higher-order relative degree

For a kind of linear discrete-time-invariant multi-input-multi-output systems with a higher-order relative degree that repetitively operates within a finite time length, the paper exploits a Markov parameters identification method by making use of the multi-operation inputs and outputs obeying a criterion. Simultaneously, an adaptive iterative learning control scheme is architected by formulating the compensator with the sequentially identified Markov parameters and the tracking error in minimizing a performance index consisting of the quadratic tracking error of the next iteration and the compensation cost. Algebraic manipulations including the singular value decomposition of a matrix and the eigenvalues estimation conduct that the identification error of the Markov parameters is monotonically declining as the iteration goes on and a smaller identification ratio in the criterion delivers a faster decline rate. Meanwhile, a rigorous derivation achieves that under the assumption that the initial identification error is within an appropriate range the tracking error is monotonously convergent for the case when the relative degree is unit whilst the tracking error is asymptotically bounded for a positive level for the case where the relative degree is higher. Numerical simulations illustrate the validity and efficiency.     

An iterative state-space identification method with data correlation for MIMO systems with measurement noise

This work presents an iterative concept of the State-space Realization Algorithm with Data Correlation (SSRA-DC) to identify MIMO systems with measurement noise and subjected to a reduced number of samples acquired from the process. The measurement noise is characterized as a random signal with properties of white noise and having up to 1% of the output signal amplitude. The proposed technique is based on the Markov parameters matrix’s feedback in an iterative algorithm supported by the SSRA-DC method. A gain factor takes part in the closed-loop to update the Markov parameters matrix, reducing their residues at each iteration. A fixed value for the gain is applied all over the iterations. The Gaussian White Noise (GWN) is employed as the input excitation signal in simulated experiments of mass-damper-springer models with 50 and 100 degrees of freedom. For some algorithm settings, one hundred simulations, each holding more than 100 iterations, are performed to statistically demonstrate the iterative algorithm’s effectiveness compared to the conventional SSRA-DC. Further comparative analysis is accomplished between the iterative method with the ARMAX and N4SID algorithms.     

Time-optimal control problem for a linear parameter varying system with nonlinear item

In this paper, we considered a time-optimal control problem for a new type of linear parameter varying (LPV) system which is obtained through data identification in the process of dealing with actual problems. The addition of non-linear terms is compensation for the method that does not require linear expansion at the equilibrium point. Since the objective function is the terminal time which is an implicit function concerning decision variables, it is a non-standard optimal control problem with uncertain terminal time. To find the global optimal solution to this problem, firstly, the control parameterization method is used to transform it into a nonlinear optimization problem of parameter selection, and then the modifed particle swarm optimization (PSO) algorithm is combined to solve the equivalent nonlinear programming problem. Numerical examples are used to illustrate the effectiveness of the proposed algorithm.     

A direct approach using the finite element method for the solution of the linear optimal control problem with a quadratic criterion

The present paper proposes a numerical approach to a linear optimal control problem with a quadratic performance index. In this technique, the time interval is divided into a number of time segments and all of the unknown functions which appear in the performance index are either interpolated linearly with respect to time or assumed to be constant in each time segment. The augmented performance index is discretized within each time element through the ordinary finite element technique.The main advantage of the present method is as follows: all of the necessary conditions for the performance index to be stationary can be expressed in the form of algebraic equations and the performance sequence of the state variables can be eliminated. As a result, the optimal control problem is reduced to the simple one of finding the sequence of control variables alone, which minimizes the quadratic performance index.A general formulation of the method is given and simple numerical examples are shown to demonstrate the effectiveness of the technique.     

A variational method for the numerical simulation of a boundary controllability problem for the linear and semilinear 1D wave equations

We pursue the numerical implementation of a boundary controllability problem for the 1D wave equation based on a recent variational approach to deal with such situations that consists in analyzing an error functional defined for feasible functions complying with appropriate initial, boundary, and final constraints. The nature of such scheme, as a minimization process for a certain error functional, leads to a natural numerical implementation by using typical descent algorithms. Our aim here is to explore the basic ingredients to set up such practical numerical approximations which allow us to address linear and semilinear equations with the same numerical scheme.     

中国建筑业全要素生产率及其收敛趋势

基于Malmquist指数法,分析了中国建筑业TFP的变化趋势和区域差异,并对建筑业TFP的发展趋势进行了σ收敛、β收敛和"俱乐部"收敛检验。结果证明,中国建筑业TFP总体上在提高,其主要动力是技术进步,各地区建筑业TFP的空间分异较为明显。σ收敛显示近年来中国建筑业TFP差距呈现出先缩小后扩大的趋势。β收敛则显示无论分时间段还是整个样本期间建筑业TFP均呈现出明显的收敛趋势,"俱乐部"收敛检验则只在东部和中部得到了验证。     

预应力箱梁张拉技术、钢束伸长值计算方法及应用

本文主要介绍了预应力张拉技术、钢束伸长量计算方法及在实际工程中的应用情况。     

Interval observer filtering-based fault diagnosis method for linear discrete-time systems with dual uncertainties

A novel interval observer filtering-based fault diagnosis method for linear discrete-time systems with dual uncertainties is proposed to detect actuator faults. The idea of minimization is adopted to design a fault-free state estimator by merging unknown but bounded and Gaussian disturbances and noises according to the signal average power principle. Using a fault-free state interval and measurement residual of the system, a fault detection indicator is designed based on the residual probability ratio, to achieve dynamic fault detection, isolation and identification. Finally, various simulation examples are provided to demonstrate the accuracy and effectiveness of the proposed method.     

An adaptive risk-sensitive filtering method for Markov jump linear systems with uncertain parameters

In this paper, an adaptive risk-sensitive multiple-model filtering method which relaxes the restrictive assumption that risk-sensitive parameter is chosen as a prior is proposed for a class of discrete-time Markov jump linear systems (MJLSs) with uncertain parameters. Some analysis is presented to illustrate the essential effect of the risk sensitivity added into the filtering process and show the intrinsic reasons for the improvement of robustness. Then, a quite useful principle is developed to obtain the risk-sensitive parameter using the measurements in an online fashion. To avoid overregulation under mismatched modes and mitigate the problem of smearing the feature of each model, a minimization mechanism is resorted to. Computer simulations are presented to reveal the effectiveness of our method.     

Fault-tolerant control for commercial aircraft with actuator faults and constraints

In this paper, a constrained control scheme based on model reference adaptive control is investigated for the longitudinal motion of a commercial aircraft with actuator faults and saturation nonlinearities. Actuator faults and constraints are both important factors adversely affecting the stability and performance of flight control systems. An adaptive adjustment law based on Lyapunov function is utilized to adjust the fault-tolerant control law. Both additive and multiplicative faults are considered in the designed controller to deal with the three types of actuator faults: locked in place, loss of effectiveness, and bias. Moreover, different techniques are implemented in the basic and fault-tolerant controller to anti-windup. Proofs for the stability of the two modified controllers which improve the performance of control system operating in the presence of actuator faults and saturations are proposed. Finally, a numerical example of the anti-windup fault-tolerant controller for a commercial aircraft is demonstrated. The stability and performance improvements can be accrued with the presented fault-tolerant control scheme.     

A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand.     

A matrix method for solving high-order linear difference equations with mixed argument using hybrid legendre and taylor polynomials

A numerical method for solving the higher order linear difference equations with variable coefficients and mixed argument under the mixed conditions is presented. The method is based on the hybrid Legendre and Taylor polynomials. The solution is obtained in terms of Legendre polynomials. IIIustrative examples are included to demonstrate the validity and applicability of the technique.     

Schedule and control co-design for networked control systems with bandwidth constraints

A new solution of networked control systems with bandwidth constraints is proposed in this paper. First, at the smart sensor side, a new stochastic communication logic scheduling strategy is designed based on a Poisson Process with time-dependent intensity. Under this strategy, the system only needs a finite-time state update. Hence the quantity of transmission of message is reduced. With the proof that the stochastic communication logic is essentially a Markov chain, the NCS is modeled as a jump system and the necessary and sufficient condition of stability for the state feedback system is presented as well. With the proposed stochastic communication logic, based on the update time, the controller is given in terms of a LMI. The simulation result shows that the scheduling strategy can decrease the network traffic, while the controller can guarantee certain good system performance.     

绝缘层厚度与芯线直径测量偏离方法的比对和探讨

本文探讨了GB／T2951．1—1997中绝缘层厚度与芯线直径测量装置的选择问题。通过西藏出入境检验检疫局与珠海出入境检验检疫局进行比对实验，证明只要用满足标准规定精度的测量仪器去测量，就能达到要求，并不一定要用读数显微镜，标准中方法是可以偏离的。     

Smoothed L1/2 regularizer learning for split-complex valued neuro-fuzzy algorithm for TSK system and its convergence results

This paper investigates an evolving split-complex valued neuro-fuzzy (SCVNF) algorithm for Takagi–Sugeno–Kang (TSK) system. In a bid to avoid the contradiction between boundedness and analyticity, splitting technique is traditionally employed to independently process the real part and the imaginary part of weight parameters in the system, which doubles weight dimension and causes oversized structure. For improving efficiency of structural optimization, previous studies have revealed that L_1/2-norm regularizer can be effective in such sparse tasks thus is regarded as a representative of L_q (0?<?q?<?1) regularizer. To eliminate oscillation phenomenon and stabilize training procedure, a smoothed L_1/2 regularizer learning is facilitated by smoothing the original one at the origin flexibly. It is rigorously proved that the real-valued cost function is monotonic decreasing during learning course, and the sum of gradient norm trends closer to zero. Plus some very general condition, the weight sequence itself is also convergent to a fixed point. Experimental results for the SCVNF are demonstrated, which match the theoretical analysis.     

低质量专利的识别方法及应用研究

随着专利申请授权数量的快速攀升,低质量专利也大量涌现,这给专利管理带来很大困扰;面对海量的专利数据,如何构建有效的低质量专利识别方法,筛选出其中的低质量专利是一项紧迫的工作。基于专利质量与专利维持的相关分析,研究提出低质量专利的识别方法和定量指标;以电通信领域的发明专利为研究样本,对低质量专利进行定量识别和评价分析。     

Event-triggered fuzzy control for nonlinear time-delay system with full-state constraints and unknown hysteresis

This article considers the nonlinear time-delay system with full-state constrains and actuator hysteresis. Compared with the previous research on input hysteresis phenomenon, all states in the system are required to be constrained in a bounded compact set and the direction of hysteresis is unknown. Thus, the system is difficult to be stabilized and get perfect error tracking performance, and the design procedure is more complicated. By combining barrier Lyapunov functions (BLFs) and Nussbaum functions, a new virtual controller is designed, which combines the properties of Nussbaum function with fuzzy logic systems (FLSs). Furthermore, considering that the rate-dependent characteristic of actuator hysteresis will adversely affect the stability of networked control systems (NCSs), a first-order filter is used to solve the problem, but it brings challenges to the design of Lyapunov–Krasovskii functions (KLFs). Thus, a new LKFs is constructed to compensate for the adverse effects of state delay on the nonlinear system. What’s more, this article propose event-triggered technique to solve the coupling effect of the system communication resource constrains. The proposed adaptive control strategy ensures the boundedness of all signals and does not violate the state constraints, and the controller avoids Zeno behavior, and the tracking error fluctuates around zero in a predetermined compression range. Finally, two simulations results verify the effectiveness of the adaptive control strategy.     

科研项目评分程序与方法

定量评分不正确一直是科研项目（课题）评价中存在的大问题。本方法采用先定性排序,后定量评分的程序,增加了评分对课题的重要程序的认识,从而能较容易地把握评分尺度,减少评分的错误。第一步用符号检验法去掉错误数据,按各课题的重要程度排出前后次序;第二步定量评分,新创了用“波动系数平均值”来确定评分值的控制线,在掉落在上下控制线之外的不合理数据,最后到了较为满意的评分结果。该方法基本上解决了课题评分不正确的问题,据验证,正确率从原来的70％左右可提高到95％。它不但适用课题评分,同样也适应其它技术经济评估。     

Robust state estimation for discrete time systems with colored noises and communication constraints

This paper investigates the state estimation problem for networked systems with colored noises and communication constraints. The colored noises are considered to be correlated to itself at other time steps, and communication constraints include two parts: (1) the information is quantized by a logarithmic quantizer before transmission, (2) only one node can access the network channel at each instant based on a specified media access protocol. A robust recursive estimator is designed under the condition of colored noises, quantization error and partially available measurements. The upper bound of the covariance of the estimation error is then derived and minimized by properly designing estimator gains. An illustrative example is finally given to demonstrate the effectiveness of the developed estimator.