Adaptive synchronization control of coupled chaotic neurons in an external electrical stimulation

In this paper we present a combined algorithm for the synchronization control of two gap junction coupled chaotic FitzHugh-Nagumo (FHN) neurons in an external electrical stimulation. The controller consists of a combination of dynamical sliding mode control and adaptive backstepping control. The combined algorithm yields an adaptive dynamical sliding mode control law which has the advantage over static sliding mode-based controllers of being chattering-free, i.e., a sufficiently smooth control input signal is generated. It is shown that the proposed control scheme can not only compensate for the system uncertainty, but also guarantee the stability of the synchronized error system. In addition, numerical simulations are also performed to demonstrate the effectiveness of the proposed adaptive controller.     

ADAPTIVE NONLINEAR FEEDBACK CONTROL OF CHAOTIC SYSTEMS BASED ON REDUCED PARAMETER QUADRATIC PREDICTORS

An adaptive nonlinear feedback-control method is proposed to control continuous-time chaotic dynamical systems, where the adaptive nonlinear controller acts on only one-dimensional error signal between the desired state and the observed chaotic state of a system. The reduced parameter adaptive quadratic predictor used in adaptive feedback cancellation of the nonlinear terms can control the system to any desired state. Computer simulation results on the Lorenz system are shown to demonstrate the effectiveness of this feedback-control method.     

最优控制在车载惯性平台稳定回路中的应用

在分析车载惯性平台数学模型的基础上,针对平台的扰动特性，提出了稳定伺服回路的一种改进型线性二次高斯 (LQG) 控制方法。该方法在反馈中加入了积分项，可以消除稳态偏差，并且依据滤波器收敛性的判据，分别利用Sage Husa自适应滤波算法和强跟踪Kalman滤波器进行状态估计,既保证了估计精度，又具有跟踪突变状态的能力。仿真和实验表明：该方法在一定程度上降低了对系统模型误差和噪声统计特性误差的要求。     

The adaptive immersed interface finite element method for elliptic and Maxwell interface problems

We propose self-adaptive finite element methods with error control for solving elliptic and electromagnetic problems with discontinuous coefficients. The meshes in the methods do not need to fit the interfaces. New error indicators are introduced to control the error due to non-body-fitted meshes. Flexible h-adaptive strategies are developed, which can be systematically extended to a large class of interface problems. Extensive numerical experiments are performed to support the theoretical results and to show the competitive behavior of the adaptive algorithm even for interfaces involving corner or tip singularities.     

自适应光学系统变形镜控制电压预测

在校正大气湍流畸变波前相差的自适应光学系统中,利用基于Levenberg-Marquardt学习算法的非线性反向传播神经网络技术（LMBP）对变形镜控制电压进行预测。以对受横向风影响的大气湍流畸变波前斜率数据为研究对象,通过数值仿真方法,研究了基于LMBP算法的自适应光学系统变形镜电压非线性预测控制算法。通过实验发现,预测电压和变形镜实际控制电压拟合效果良好。讨论了回溯帧数对预测效果的影响,并与基于递推最小二乘（RLS）算法的线性预测算法进行比较。对比结果表明,基于LMBP算法的非线性电压预测方法比基于递推最小二乘法的线性电压预测方法能更有效地降低系统由伺服延迟引起的误差。     

基于在线误差修正自适应SVR的非线性不确定分数阶混沌系统滑模控制

提出了一种基于在线误差修正自适应SVR的滑模控制方法, 用于解决一类非线性不确定分数阶混沌系统的控制问题. 分别通过对混沌系统非线性函数的离线SVR估计和基于增量学习的状态跟踪误差在线SVR预测, 解决了不确定分数阶混沌系统模型难以预测的问题. 同时根据Lyapunov稳定性理论设计出SVR权值自适应调整律. 本文以分数阶Arneodo 系统为例进行仿真, 仿真结果表明了, 对于带有外界噪声扰动的非线性不确定分数阶混沌系统, 该方法可以在有限时间内将系统稳定至期望状态, 提高对非线性函数的预测精度, 改善控制性能.     

A simplified adaptive feedback active noise control system

In the adaptive feedback active noise control system based on the internal model control (IMC) structure, the reference signal is regenerated by synthesizing the error signal and the secondary signal filtered with the estimation of the secondary path, hence more computation load and extra programming are required. Motivated by the engineering truth that the primary noise cannot be completely cancelled in most practical active noise control applications and the error signal still contains some portions of the primary noise, a simplified adaptive feedback active noise control system is proposed in this paper, which adopts the error signal directly as the reference signal in an adaptive feedforward control system and utilizes the leaky filtered-x LMS algorithm to update the controller. The convergence properties of the proposed system are investigated and its advantages are discussed by comparing with other feedback control systems as well as the weakness. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed system.     

一种自适应光学闭环系统预测控制算法的仿真研究

介绍了校正大气湍流畸变波前像差的自适应光学系统中,基于预测控制技术对变形镜控制电压进行预测以减少自适应光学系统中时间延迟误差的方法.对受横向风影响的大气湍流畸变波前斜率数据,利用数值仿真方法,研究了基于递推最小二乘(RLS)算法的线性预测控制算法对自适应光学系统变形镜控制电压进行超前预测的方法,并与采用比例积分(PI)...     

Improving robustness of filtered-x least mean p-power algorithm for active attenuation of standard symmetric-α-stable impulsive noise

The paper concerns active control of impulsive noise having peaky distribution with heavy tail. Such impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance (second order moment) of error signal, and hence, becomes unstable for the impulsive noise. In order to improve the robustness of adaptive algorithms for processes having distributions with heavy tails (i.e. signals with outliers), either (1) a robust optimization criterion may be used to derive the adaptive algorithm or (2) the large amplitude samples may be ignored or replaced by an appropriate threshold value. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm (approach 1). The other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the FxLMS algorithm (approach 2). In this paper we propose two solutions to improve the robustness of the FxLMP algorithm. In first proposed algorithm, the reference and the error signals are thresholded before being used in the update equation of FxLMP algorithm. As another solution to improve the performance of FxLMP algorithm, a modified normalized step size is proposed. The computer simulations are carried out, which demonstrate the effectiveness of the proposed algorithms.     

Constrained Active Fault Tolerant Control Based on Active Fault Diagnosis and Interpolation Optimization

A new active fault tolerant control scheme based on active fault diagnosis is proposed to address the component/actuator faults for systems with state and input constraints. Firstly, the active fault diagnosis is composed of diagnostic observers, constant auxiliary signals, and separation hyperplanes, all of which are designed offline. In online applications, only a single diagnostic observer is activated to achieve fault detection and isolation. Compared with the traditional multi-observer parallel diagnosis methods, such a design is beneficial to improve the diagnostic efficiency. Secondly, the active fault tolerant control is composed of outer fault tolerant control, inner fault tolerant control and a linear-programming-based interpolation control algorithm. The inner fault tolerant control is determined offline and satisfies the prescribed optimal control performance requirement. The outer fault tolerant control is used to enlarge the feasible region, and it needs to be determined online together with the interpolation optimization. In online applications, the updated state estimates trigger the adjustment of the interpolation algorithm, which in turn enables control reconfiguration by implicitly optimizing the dynamic convex combination of outer fault tolerant control and inner fault tolerant control. This control scheme contributes to further reducing the computational effort of traditional constrained predictive fault tolerant control methods. In addition, each pair of inner fault tolerant control and diagnostic observer is designed integratedly to suppress the robust interaction influences between estimation error and control error. The soft constraint method is further integrated to handle some cases that lead to constraint violations. The effectiveness of these designs is finally validated by a case study of a wastewater treatment plant model.     

基于补偿控制的无人地面车辆轨迹跟踪方法

针对无人地面车辆轨迹跟踪精度不高,鲁棒性差的问题,提出了一种基于补偿控制的算法。该算法分为运动学和动力学两部分：基于方向角调整策略的运动学控制律能确保无人地面车辆有效跟踪参考轨迹;基于PD与模型参考模糊滑模自适应控制相结合控制的动力学控制律能有效补偿建模不精确和外界扰动带来的影响。仿真结果表明：该算法能够有效跟踪参考轨迹,控制量分配合理且鲁棒性较好。     

一类离散时间广义系统的迭代学习控制

针对一类离散时间广义系统,提出了一种离散迭代学习控制算法.首先,通过非奇异变换将离散时间广义系统分解为正常离散状态方程和代数方程的形式.然后,利用上一次迭代学习获得的前一时刻误差和当前时刻误差来修正上一次的控制量,从而获得下一次迭代学习的新控制量,并对算法的收敛性进行了理论证明,给出了算法收敛的充分条件.研究结果表明,所提算法能够在有限时间区间内实现系统状态对期望状态的完全跟踪.最后,通过仿真算例进一步验证了所提算法的有效性.     

基于无记忆状态观测器的多时滞不确定非线性系统的自适应控制

针对一类多时滞不确定非线性系统,研究了基于无记忆状态观测器的自适应控制问题.时滞状态扰动的上界未知,在控制中通过自适应律估计未知参数,并利用估计值设计了不依赖于时滞的无记忆状态观测器和控制器,基于Lyapunov-Krasovskii函数证明了观测误差渐近收敛到零.最后仿真结果说明了该方法的有效性.     

Optimal tracking control with zero steady-state error for time-delay systems with sinusoidal disturbances

The problem of optimal tracking control with zero steady-state error for linear time-delay systems with sinusoidal disturbances is considered. Based on the internal model principle, a disturbance compensator is constructed such that the system with external sinusoidal disturbances is transformed into an augmented system without disturbances. By introducing a sensitivity parameter and expanding power series around it, the optimal tracking control problem can be simplified into the problem of solving an infinite sum of linear optimal control series without time-delay and disturbance. The obtained optimal tracking control law with zero steady-state error consists of accurate linear state feedback terms and a time-delay compensating term, which is an infinite sum of an adjoint vector series. The accurate linear terms can be obtained by solving a Riccati matrix equation and a Sylvester equation, respectively. The compensation term can be approximately obtained through a recursive algorithm. A numerical simulation shows that the algorithm is effective and easily implemented, and the designed tracking controller is robust with respect to the sinusoidal disturbances.     

HL-2A 等离子体垂直不稳定性控制研究

采用了PID 控制、输入误差直接自适应控制和输出误差直接自适应控制三种控制方式对HL- 2A 装置的等离子体垂直不稳定性进行了研究。模拟结果显示, 在现有条件下三种控制方式都能满足控制要求, 而两种自适应控制系统具有更好的系统性能、更强的鲁棒性、对电源要求更低的特点, 尤其是输出误差直接自适应控制系统结构非常简单, 具有可实施性。     

执行依赖启发式动态规划PMSM控制策略研究

永磁同步电机算法中的PID参数固定不变,自适应能力较差。近似动态规划具有自学习能力,能动态调整参数,增强系统的自适应能力。本文将近似动态规划中的代表性算法——执行依赖启发式动态规划与类PI网络相结合,根据环境反馈对各个策略进行评价,选取评价最优的策略在线调整动作网络参数,对永磁同步电机进行自适应最优控制。仿真结果表明,执行依赖启发式动态规划加PI控制算法在系统速度变化和负载变化过程中,动态效果和稳态精度优于传统PID控制。     

基于非对称模型的变电所数据融合误差优化研究

牵引变电所作为电力系统中实现变换电能的场所,给电力机车提供稳定可靠的电能。由于机车具有非线性运载特点,在吸收电网电流的同时也在输入谐波电流,引起电网干扰波动和噪声误差、电压畸变等现象,对供电系统安全构成威胁。本文通过建立变电所三相匹配与失配模型,利用自适应数据融合算法仿真检测控制误差的效果和优化误差的最佳方案,合理控制参数的状态和优化参数的规律。结果表明数据融合可以解决局部电网误差波动电流超标和电压畸变,在保障供电质量和电网安全方面效果显著。     

Equivalent a Posteriori Error Estimator of Spectral Approximation for Control Problems with Integral Control-State Constraints in One Dimension

In this paper, we investigate the Galerkin spectral approximation for elliptic control problems with integral control and state constraints. Firstly, an a posteriori error estimator is established, which can be acted as the equivalent indicator with explicit expression. Secondly, appropriate base functions of the discrete spaces make it probable to solve the discrete system. Numerical test indicates the reliability and efficiency of the estimator, and shows the proposed method is competitive for this class of control problems. These discussions can certainly be extended to two- and three-dimensional cases.     

On Scientific Data and Image Compression Based on Adaptive Higher-Order FEM

We present a novel compression algorithm for 2D scientific data and images based on exponentially-convergent adaptive higher-order finite element methods (FEM). So far, FEM has been used mainly for the solution of partial differential equations (PDE), but we show that it can be applied to data and image compression easily. The adaptive compression algorithm is trivial compared to adaptive FEM algorithms for PDE since the error estimation step is not present. The method attains extremely high compression rates and is able to compress a data set or an image with any prescribed error tolerance. Compressed data and images are stored in the standard FEM format, which makes it possible to analyze them using standard PDE visualization software. Numerical examples are shown. The method is presented in such a way that it can be understood by readers who may not be experts of the finite element method.     

压电驱动快速反射镜的自适应反演滑模控制

由压电驱动器驱动的快速反射镜(FSM)广泛应用于各种精密稳定跟踪系统,FSM的控制精度决定了系统的跟踪精度。但压电驱动器具有严重的迟滞非线性干扰,针对这一缺点,应用自适应径向基RBF神经网络对迟滞干扰进行非线性逼近,并在此基础上结合滑模控制和反演法,设计了自适应反演滑模(ABSM)控制器。仿真实验表明,相对于滑模控制器,ABSM控制器的最大跟踪误差和均方根误差为分别降低了57.26%和52.53%,提高了FSM的控制精度。