输出不可量测非线性系统的神经模型参考自适应控制

该文针对被控对象输出不可量测的非线性系统,引入一个便于在线辨识的扩展神经网络模型,提出一种基于前馈-反馈结构的神经网络模型参考自适应控制方法。给出了具有全局收敛性的网络训练算法,并分析了控制系统的稳定性。仿真结果表明该控制方法是有效的,而且对网络初始权值的选取及被控对象特性参数的扰动都具有良好的鲁棒性。     

Precise angular speed control of permanent magnet DC motors in presence of high modeling uncertainties via sliding mode observer-based model reference adaptive algorithm

The major concentration of this study is on developing a control scheme with parameter- and load-insensitive features capable of precise angular speed regulation of a permanent magnet (PM) DC motor in the presence of modeling uncertainties. Towards this objective, first, an appropriate nonlinear dynamic model of friction, the modified LuGre model, is opted and incorporated into the mathematical model of a PM DC motor. Then a sliding mode observer (SMO) is designed to estimate the state variable of the friction model. Next, a model reference adaptive control system into which estimated values of the friction state and parameters are fed is designed to track the desired speed trajectory while alleviating the adverse effects of model uncertainties and friction. Stability of the proposed SMO-based MRAC system is discussed via the Lyapunov stability theorem, and its asymptotic stability is verified. In addition to simulation studies, the algorithm is implemented on a new variable structure test-bed which gives us the ability to simulate desired parameter variations and external disturbance changes in experiment. The main contribution of the proposed scheme is the bounded estimation of the system’s friction parameter. While similar control solutions do estimate these parameters, there is no guarantee that they will estimate the correct value of friction parameters. However, in the proposed method, by properly choosing the design parameters, if certain criteria is satisfied, the estimated friction parameters will be in the bounded vicinity of their actual values. The obtained results show the effectiveness of the proposed tracking algorithm and its robustness against load and system parameters’ variations.     

一种基于期望模型的自适应Singer模型滤波算法

针对使用固定模型滤波算法跟踪机动目标时滤波精度依赖于模型固有参数的问题,提出了一种基于期望模型的自适应Singer模型滤波算法。首先利用3组代表不同机动强弱的典型Singer模型组成基础模型集合,然后通过实时计算目标综合残差确定目标机动等级,根据目标机动等级的变化来生成期望模型,并实时扩充基础模型集合进行交互式多模型（Interacting Multiple Model,IMM）滤波。该算法降低了对基础模型选取的依赖性,具有更好的环境适应性,在目标不同机动状态下都能进行准确跟踪。     

Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

In this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.     

Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system

Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input–output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward–feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized Prandtl–Ishlinskii model. An adaptive model reference temperature control system is cascaded to the inverse hysteresis model in order to estimate the SMA electrical current for tracking the reference signal. In addition, a closed-loop proportional–integral controller with position feedback is added to the feedforward controller to increase the accuracy as well as eliminate the steady state error in position control process. Experimental results indicate that the proposed controller has great accuracy in tracking some square wave signals. It is also experimentally shown that the suggested controller has precise tracking performance in presence of environmental disturbances.     

Multirate adaptive robust control for discrete-time non-minimum phase systems and application to linear motors

It is well known that a plant becomes non-minimum phase in discrete-time domain when the relative degree of the original continuous-time plant is greater than 2 even if the plant is minimum-phase in continuous-time domain. Thus, it was difficult to apply the conventional adaptive controllers directly to these systems. In this paper, multirate adaptive robust control (MARC) is proposed for these systems. This scheme is developed by the good combination of perfect tracking control (PTC) with multirate feedforward control which has been proposed by the first author and discontinuous projection based adaptive robust control (ARC) which has been proposed by the second author. Although the original PTC can assure perfect tracking only for nominal plant, the proposed MARC can guarantee: 1) perfect tracking for plant with parametric uncertainty and 2) overall stability even if there exist modeling error and disturbance. The proposed scheme is applied to the high-speed position control of a linear motor, and the advantages are demonstrated through experiments.     

A self‐adaptive load‐dispatching control framework for device data accessing in IoT‐based systems

The Internet of things (IoT) information system plays important roles in disposing of huge volumes of real‐time service requests from heterogeneous devices, targeting for different complex application requirements. Load‐dispatching control (LDC) is a key problem to be solved for devices accessing concurrently in cluster systems. Self‐adaptive LDC optimizes the resource allocation to ensure no overloading node, thus, improving the performance of IoT systems. This paper focuses on adaptive dispatching control problem in IoT information system. First, a device data access platform is proposed for reducing the load imbalance and improving the efficiency of data processing. Then, we propose a processing capability prediction model to evaluate the system performance. On the basis of the model, we present a practical self‐adaptive LDC framework with a self‐adaptive control strategy and a load dispatching method. Finally, a case study is given to verify the framework and the control strategy. Experimental results show that the proposed strategy can meet the requirements of dynamic load balancing with the ability to avoid the load imbalance problem, and the LDC‐based device access platform can process data accessing effectively and ubiquitously.     

参数不确定混沌系统的自适应同步控制

用同步误差最小化的自适应方法与替代状态变量的同步法相结合,来研究混沌系统的同步控制,并且估计驱动系统的未知参数,所需的信息只要一个这个系统的状态变量.未知参数可能显含在这个已知状态变量的发展方程中,也可能不显含在这个发展方程中,只要满足一定的条件,都可以实现参数估计与系统的混沌同步.以Lorenz系统为例,进行了具体分析与数值模拟,所得结果说明该控制策略是有效的.     

磁悬浮系统的简单自适应控制

利用动力学和电磁学方法建立了磁悬浮系统的模型,利用劳斯判据得到了系统稳定的控制参数的范围。给出了一种简单的自适应控制方法和实验数据,最后详细介绍了控制系统的硬件结构以及算法的具体实现。     

一种广义预测自适应控制算法

广义预测控制是一种基于被控对象参数模型的控制算法,适用于复杂的、数学模型难以精确建立的控制系统。现研究了该算法,并对一个被控对象模型进行了仿真。结果表明,广义预测控制算法响应速度快,无超调,控制效果理想。     

通信系统中自适应回波抵消的一种新方法

语音分组传输系统的出现，对回波抵消技术提出了新的挑战。在回波抵消中一个关键的问题是需要快速有效地估计延迟时间。基于直接计算输入信号互相关函数的延迟估计方法，在实际应用中回波消除性能差，运算量大。为此提出了基于混合模型的延时估计方法，对输入信号进行带通滤波和降低抽样率抽样，使其具有白噪声特性，然后快速计算互相关函数的估计值。理论分析和实验结果证明新的延迟估计方法有很好的回波消除性能和较少的运算量，在实际通信系统中有较好的应用前景。     

针对时间交织采样的后台自适应频域校准技术

针对高速双通道时间交织采样(Time-interleaved Analog-to-Digital Converter, TIADC)系统通道失配的问题,提出了一种后台自适应频域校准技术。该技术采用数字混频+低通滤波技术,将失配镜像点搬移至零频,并形成I/Q复数信号;然后基于信号统计学进行校准系数计算,并利用该系数完成共轭对消校准;最后将校准后信号通过数字混频搬移回原始频率,完成整个校准过程。进一步提出了基于FPGA/ASIC的算法实现电路。经实物测试验证,在6 Gsample/s双通道TIADC系统中,该技术能够优化失配比达33.3 dBc以上,失配优化程度高,同时具有全流水实时后台处理特性,且不需要增加额外系统资源。     

面向医疗大数据的风险自适应的访问控制模型

面对医疗大数据,策略制定者难以预测医生的访问需求,进而制定准确的访问控制策略。针对上述问题,提出一种基于风险的访问控制模型,能够适应性地调整医生的访问能力,保护患者隐私。该模型通过分析医生的访问历史,使用信息熵和EM算法量化医生侵犯隐私造成的风险。利用量化的风险,监测和控制对于医疗记录的过度访问以及特殊情况下的访问请求。实验结果表明,该模型是有效的,并且相比于其他模型能更为准确地进行访问控制。     

空天飞行器姿态运动的模糊滑模鲁棒控制研究

针对空天飞行器(ASV)的姿态系统的非线性、强耦合和不确定性,提出了一种分散模糊滑模变结构控制方法.首先基于反馈线性化方法将ASV姿态系统解耦成3个独立的子系统;然后应用模糊滑模变结构控制方法分别设计了各子系统的姿态控制器.通过结合模糊控制与滑模变结构控制,有效抑制了ASV姿态系统的建模误差以及外部干扰.理论分析和仿真研究表明,所提方法具有控制精度高、鲁棒性强、便于工程实现等优点.     

基于动态模型库的多模自适应预测控制

针对非线性复杂系统,提出了一种基于动态模型库的多模自适应预测控制方法.采用最小二乘支持向量机对非线性系统进行建模,对此模型进行线性化,并将此线性化模型加入到固定模型库中,对固定模型库进行动态更新;通过一定的选择机制,从固定模型库中选择出与当前时刻系统工作点较接近的部分模型构成一个动态模型库,并基于此动态模型库重构相应的控制器.仿真结果证明了该方法的有效性.     

卫星通信中的一种小参量跳频BPSK/DPSK相位估计算法

军事卫星通信系统中很多情况下采用跳频通信,这就导致PSK的相干检测比较困难,通常使用增加参考符号来对接收信号的相位进行估计.因此很多系统采用差分相位调制,比如DPSK调制方式就不需要相位信息,但DPSK比PSK的功率效率要低.本文提出一种新的相位估计算法,该算法利用小数据量的参考信号对相位调制信号的相干检测来提高功率效率,而且在同一编码块(同一跳)中实现相位估计.本文在3种通信信道环境下进行模拟试验(蒙特卡罗仿真),最后以输出的误码率来验证算法的先进性.     

Dynamic and adaptive access control model

With the development of cloud computing technology, smart city and mobile office and emergence of mobile smart devices, the resources' environment is increasing complex. The traditional access control model has been difficult to meet the diverse access control requirements and the dynamic and adaptive access control policy. A dynamic and adap-tive access control model combining the resource life cycle management based on ABAC was proposed. The model fo-cused on resource life cycle management considering the relevance of the resource life cycle management and access control policy. In this model, the policy can be changed as the resource life cycle states' change, so the applicability has been improved. In addition, the user access behavior history management function was added in this model, which can adapt the environment better by considering history of user access behavior. At last, the model in general and cloud com-puting access control system was implemented and verified.     

基于模型不确定逼近的RBF网络机器人自适应控制

利用RBF网络能以任意精度逼近任意的连续函数的特点,设计一种基于模型不确定逼近RBF网络机器人的自适应控制器。采用RBF网络可以大大加快学习速度,并避免局部极小问题,适合于实时控制要求。仿真结果表明,该控制算法具有较强的鲁棒性和较好的跟踪性。     

基于Matlab/Simulink的直接转矩控制仿真系统

直接转矩控制技术是一种具有优良静、动态性能的交流电机控制技术。文章介绍了直接转矩控制技术的基本原理,并采用Matlab／Simulink软件对直接转矩控制仿真系统进行了建立,通过对仿真系统的建立和仿真结果的分析,能为后续工作中正确建立硬件系统和编写软件应用程序具有指导意义,从而大大缩短了系统开发的时间,为下一步直接转矩控制的成功实现提供了基础。     

一种基于神经网络的超稳定自适应控制算法

提出了一种用于控制复杂非线性系统的超稳定自适应控制算法。使用波波夫超稳定性原理设计控制器。用神经网络在线辨识系统的建模误差及不确定性因素，辨识结果作为补偿信号以实现系统的鲁棒控制。对一双输入双输出非线性系统的仿真结果表明，所提出的超稳定自适应控制算法具有较好的性能。