具有未建模动态的时变关联系统的鲁棒分散自适应镇定

针对一类具有动态、静态关联项和未建模动态的时变关联系统,通过利用反推设计方法和σ-修正自适应律,研究了鲁棒分散自适应镇定问题．当时变参数的变化率及未建模动态的幅值分别在某些范围内变化时,证明了闭环系统的所有信号都有界,且每一个子系统的输出与系统参数的变化率有关．仿真例子验证了控制方案的有效性．     

油气井出砂信号的动态监测与处理

针对油气井出砂监测进行了研究，设计了“出砂动态监测系统”。该系统结合传统的振动测量思想与现代信号处理技术，突破了以往在出砂监测中将出砂信号视为静态信号，进而相应地采取静态信号处理的方法，运用功率谱、概率密度函数等数理统计方法，综合识别出砂信号。理论分析与实验研究表明，该实验系统在国内具有先进性。     

机车励磁调节器智能检测系统的模块化设计

通过对传统机车用励磁调节器检测过程的研究，在原有测试系统物理硬件的基础上，采用计算机智能测试技术，设计了一套用于检测机车用励磁调节器的智能检测系统。励磁调节器及信号发生器信号经一单片机采集，经串口输入到计算机中，并开发了基于MATLAB的软件对信号进行处理。从而获得励磁调节器多项静态与动态参数，给出是否合格的信息。该系统的开发提高了励磁调节器检测的精度和速度。     

一类具有未建模动态的非线性系统模糊自适应鲁棒控制

针对一类单输入单输出未建模动态不确定非线性系统,提出一种模糊自适应backstepping控制方法.设计中利用模糊逻辑系统逼近系统的未知函数,应用非线性阻尼项抵消系统的非线性不确定项,通过引入一个动态信号克服未建模动态.该模糊自适应控制方法保证了整个闭环系统的有界性,输出信号可调节到零的小邻域内.仿真结果进一步验证了该方法的有效性.     

小样本ATS测量不确定度评定方法研究

提出一种基于灰色系统理论和贝叶斯信息融合理论评定小样本自动测试系统（Automatic Test System,ATS）测量不确定度的新方法。该方法在已有测量不确定度灰色评定模型的基础上,引入灰色关联理论,建立新灰色评定模型,并引入贝叶斯信息融合思想,融合历史测量数据和当前测量数据的评定结果,得到最终测量不确定度。以某ATS测量链中具体的传递单元作为实例,按该方法计算测量不确定度,并将评定结果与其他常用方法评定结果进行比较,达到很好的一致性,且该方法评定小样本ATS静态测量得到的结果准确度高,评定小样本ATS动态测量,更符合其动态测量特性且计算量小。     

面向控制性能的一类非线性关联系统输出反馈动态面控制

针对一类含有完全未知关联项的多输入/多输出非线性系统,提出了输出反馈动态面自适应控制方案,克服了反推控制中的微分爆炸问题;利用神经网络逼近系统中的未知关联项,对于每个子系统只需对一个参数设计自适应律;引入性能函数和输出误差变换,跟踪误差信号的收敛速率、最大超调量和稳态误差等控制性能指标均可得到保证.理论证明了闭环系统的所有信号半全局一致有界,仿真结果验证了所提方案的有效性.     

带有死区输入的非线性系统的自适应控制

针对一类具有死区非线性输入的非线性系统,同时考虑系统中存在未建模不确定项,设计了自适应控制器及未知参数的自适应估计率.该控制器使得闭环系统全局稳定且实现了输出信号对参考信号的精确跟踪.仿真结果进一步证实了该控制器能对未知死区及未建模动态进行有效的补偿。     

基于文本追踪合并的检测算法研究

针对传统的静态检测方法准确率低下,动态检测方式难以获得较高吞吐率的瓶颈,提出了一种动态静态混合模式的恶意网页检测系统,该系统采用二级串联的检测方式。在基于规则的分类器属性提取中,应用关联文本追踪合并的思想,大幅提高了静态检测算法的准确率;利用设计的轻量级虚拟机来代替传统的系统级虚拟化检测环境,使得动态检测系统具有更高的任务吞吐率。最后对混合模式的检测准确率和吞吐率与各级检测子系统的依赖关系进行了推导和分析,实验结果表明了该模式的实用性。     

一类具有未建模动态的非线性系统模糊自适应鲁棒控制

针对一类单输入单输出未建模动态不确定非线性系统,提出一种模糊自适应Backstepping控制方法.设计中利用模糊逻辑系统逼近系统的未知函数,应用非线性阻尼项抵消系统的非线性不确定项,通过引入一个动态信号克服未建模动态.该模糊自适应控制方法保证了整个闭环系统的有界性,输出信号可调节到零的小邻域内.仿真结果进一步验证了该方法的有效性.

     

广义系统输出反馈严格正实控制器设计

研究连续广义系统的正实控制问题。目的是设计静态输出反馈和动态输出反馈严格正实控制器使得闭环系统容许且扩展严格正实。基于广义系统严格正实引理，利用线性矩阵不等式和广义代数Riccati不等式，给出了广义系统静态输出反馈和动态输出反馈严格正实控制器存在的充要条件及设计方法。对动态输出反馈情形，给出了一个数值算例说明控制器设计方法的有效性及可行性。     

Robust decentralized adaptive stabilization for a class of interconnected systems

The robust decentralized adaptive output-feedback stabilization for a class of interconnected systems with static and dynamic interconnections by using the MT-filters and backstepping design method is studied. By introducing a new filtered tramfomnation, the adaptive laws were derived for measurement. Under the assurnption of the nonlinear growth conditions imposed on the nonlinear interconnections and by constructing the error system and using a new proof method, the global stability of the closed-loop system was effectively analyzed, and the exponential convergence of all the signals except for parameter estimates were guaranteed.     

Robust decentralized adaptive stabilization for a class of interconnected systems with unmodelled dynamics

This paper considers the problem of robust decentralized adaptive output feedback stabilization for a class of interconnected systems with static and dynamic interconnections, and unmodelled dynamics by using MT filters and the back-stepping design method. It is shown that the closed-loop system is globally stable, and the asymptotic convergence of all the signals except for the parameter estimates can be guaranteed. The result is better than those obtained in other related papers. The effectiveness of the proposed scheme is demonstrated by simulation.     

Decentralised zero-sum differential game for a class of large-scale interconnected systems via adaptive dynamic programming

In this paper, a novel decentralised differential game strategy for large-scale nonlinear systems with matched interconnections is developed by using adaptive dynamic programming technique. First, the Nash-equilibrium solutions of the corresponding isolated differential game subsystems are found by appropriately redefining the associated cost functions accounting for the bounds of interconnections. Then, the decentralised differential game strategy is established by integrating all the modified Nash-equilibrium solutions of the isolated subsystems to stabilise the overall system. Next, the solutions of Hamilton–Jacobi–Isaaci equations are approximated online by constructing a set of critic neural networks with adaptation law of weights. The stability analysis of each subsystem is provided to show that all the signals in the closed-loop system are guaranteed to be bounded by utilising Lyapunov method. Finally, the effectiveness of the proposed decentralised differential game method is illustrated by a simple example.     

Decentralized adaptive output feedback control via input/output inversion

A decentralized adaptive output feedback control design method is presented for control of large-scale interconnected systems. It is assumed that all the controllers share prior information about the subsystem reference models. Based on that information, a linear dynamic output feedback compensator and linearly parameterized neural network (NN) are introduced for each subsystem to partially cancel the effect of the interconnections on the tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.     

Decentralized robust adaptive control of nonlinear systems withunmodeled dynamics

The authors present a decentralized robust adaptive output feedback control scheme for a class of large-scale nonlinear systems of the output feedback canonical form with unmodeled dynamics. A modified dynamic signal is introduced for each subsystem to dominate the unmodeled dynamics and an adaptive nonlinear damping is used to counter the effects of the interconnections. It is shown that under certain assumptions, the proposed decentralized adaptive control scheme guarantees that all the signals in the closed-loop system are bounded in the presence of unmodeled dynamics, high-order interconnections and bounded disturbances. Furthermore, by choosing the design constants appropriately, the tracking error can be made arbitrarily small regardless of the interconnections, disturbances, and unmodeled dynamics in the system. An illustration example demonstrates the effectiveness of the proposed scheme     

非线性关联系统自适应神经网络输出反馈分散控制

针对一类带有完全未知关联项的非线性大系统,提出一种自适应神经网络输出反馈分散控制方法.采用神经网络逼近未知的关联项,因此对关联项常做的假设如匹配条件,被上界函数所界定等不再要求.在神经元输入中采用参考信号取代关联信号,从而成功地避免了对关联信号的微分.保证了闭环系统所有信号半全局一致最终有界,证明了跟踪误差收敛于一个包含原点的小残集.     

Neural-Network-Based Decentralized Adaptive Control for a Class of Large-Scale Nonlinear Systems With Unknown Time-Varying Delays

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov–Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.      

Globally decentralized adaptive backstepping neural network tracking control for unknown nonlinear interconnected systems

A globally stable decentralized adaptive backstepping neural network tracking control scheme is designed for a class of large‐scale systems with mismatched interconnections. Under the assumption that the subsystems share the reference signals from the other subsystems, neural networks are used to approximate the unknown interconnections dependent on all reference signals such that the NN approximation domain can be determined a priori based on the bounds of reference signals. The proposed control approach can guarantee that all closed‐loop signals are globally uniformly ultimately bounded and that the tracking errors converge to a small residual set around the origin. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Testing interconnections to static RAMs

A method for testing the interconnections of ordinary static RAMs with a processor that has a boundary-scan register and an IEEE 1149.1 test-access port is described. The method uses an enhanced boundary-scan-register design that manipulates the test-access-port controller states to meet the static RAM's timing constraints. The implementation is more economical than a boundary-scan register that strictly conforms to IEEE 1149.1. Test operation is more efficient, requiring a third of the number of scan operations. A test-pattern set and a method for detecting and diagnosing the interconnection faults on RAMs are also described. The test-pattern set can be enhanced as necessary to increase coverage and diagnosing ability and to handle any RAM configuration. The implementation of the proposed boundary-scan register is independent of the test algorithm used. It is believed that the methodology is extendable to RAMs that use an access protocol different from the one described, for example dynamic RAMs and synchronous RAMs     

Adaptive Decentralized Asymptotic Tracking Control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections, unknown time-varying parameters, and disturbances. First, by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time, all extra signals in the framework of decentralized control are filtered out, thereby removing all additional assumptions imposed on the interconnections, such as upper bounding functions and matching conditions. Second, by introducing two integral bounded functions, asymptotic tracking control is realized. Moreover, the nonlinear filters with the compensation terms are introduced to circumvent the issue of “explosion of complexity”. It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero asymptotically. In the end, a simulation example is carried out to demonstrate the effectiveness of the proposed approach.