一类具有不确定的互联大系统分散鲁棒输出跟踪控制器设计

针对一类具有不确定的互联大系统，研究了使受控系统鲁棒稳定和渐近跟踪参考输入的分散鲁棒跟踪控制器的设计问题，并对具有分散反馈控制器的闭环大系统，给出了其鲁棒稳定以及渐近跟踪的证明。     

Integrated control design for driver assistance systems based on LPV methods

The paper proposes a control design method for a driver assistance system. In the operation of the system, a predefined trajectory required by the driver with a steering command is followed. During manoeuvres the control system generates differential brake moment and the auxiliary front-wheel steering angle and changes the camber angles of the wheels in order to improve the tracking of the road trajectory. The performance specifications are guaranteed by the local controllers, i.e. the brake, the steering, and the suspension systems, while the coordination of these components is provided by the supervisor. The advantage of this architecture is that local controllers are designed independently, which is ensured by the fact that the monitoring signals are taken into consideration in the formalisation of their performance specifications. The fault-tolerant control can be achieved by incorporating the detected fault signals in their performance specifications. The control system also uses a driver model, with which the reference signal can be generated. In the control design, the parameter-dependent linear parameter-varyingmethod, which meets the performance specifications, is used. The operation of the control system is illustrated through different normal and emergency vehicle manoeuvres with a high-accuracy simulation software.     

Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator

In cold season, wet snow ice accretion on overhead transmission lines increases wind load effects which in turn increases line tension. This increased line tension causes undesirable effects in power systems. This paper discusses the design of an observer-based boundary sliding mode control (BSMC) for 3 DOF overhead transmission line de-icing robot manipulator (OTDIRM). A robust radial basis functional neural network (RBFNN) observer-based neural network (NN) controller is developed for the motion control of OTDIRM, which is a combination of BSMC, NN approximation and adaptation law. The RBFNN-based adaptive observer is designed to estimate the positions and velocities. The weights of both NN observer and NN approximator are tuned off-line using particle swarm optimization. Using Lyapunov analysis the closed loop tracking error was verified for a 3 DOF OTDIRM. Finally, the robustness of the proposed neural network-based adaptive observer boundary sliding mode control (NNAOBSMC) was verified against the input disturbances and uncertainties.     

低信噪比条件下正弦信号幅度盲估计

研究了电子侦察信号处理中正弦信号幅度盲估计问题。提出了一种基于相关累加及线性回归的正弦信号幅度盲估计算法。先对接收到的信号进行频率估计,建立参考信号,将接收信号与参考信号相关后变换到基带并作累加,后将相关累加曲线进行最小二乘线性回归,以回归得到的直线斜率值作为信号幅度的估计值。仿真结果表明,当信噪比大于[-3 dB]时,方法的估计均方根误差小于1.1倍克拉美罗限,可在低信噪比条件下,实现正弦信号幅度的盲估计。     

参数不确定统一混沌系统鲁棒最优控制

针对含有参数不确定性的统一混沌系统,基于鲁棒最优控制理论,提出了一种简单的线性状态反馈控制策略.该策略给出了系统渐进稳定的充分条件,并可以通过求解线性矩阵不等式(LMI),快速有效地求得系统反馈的控制增益,利用Lyapunov方法证明了闭环系统的稳定性.仿真结果验证了所提出的控制策略的有效性.     

Adaptive Robust Control for a Class of Uncertain MIMO Non-affine Nonlinear Systems

In this paper, the adaptive robust tracking control scheme is proposed for a class of multi-input and multioutput (MIMO) non-affine systems with uncertain structure and parameters, unknown control direction and unknown external disturbance based on backstepping technique. The MIMO nonaffine system is first transformed into a time-varying system with strict feedback structure using the mean value theorem, and then the bounded time-varying parameters are estimated by adaptive algorithms with projection. To handle the possible "controller singularity" problem caused by unknown control direction, a Nussbaum function is employed, and the dynamic surface control (DSC) method is applied to solve the problem of "explosion of complexity" in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through Lyapunov stability theorem and decoupled backstepping method. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.      

Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises

The information fusion estimation problems are investigated for multi-sensor stochastic uncertain systems with correlated noises. The stochastic uncertainties caused by correlated multiplicative noises exist in the state and observation matrices. The process noise and the observation noises are one-step auto-correlated and two-step cross-correlated, respectively. While the observation noises of different sensors are one-step cross-correlated. The optimal centralized fusion filter, predictor and smoother are proposed in the linear minimum variance sense via an innovative analysis approach. To enhance the robustness and flexibility, a distributed fusion filter is put forward, which requires the calculation of filtering error cross-covariance matrices between any two local filters. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection (CI) fusion algorithm, which can reduce the computational cost. A simulation example is given to show the effectiveness of the proposed algorithms.     

多变量鲁棒自校正解耦间接算法

本文提出了一种多变量鲁棒自校正解耦间接算法，并在一定的条件下给出了算法的鲁棒性分析，通过仿真实验证明了算法的有效性。     

大型空间飞行器的高阶滑模姿态控制律设计

针对大型空间飞行器的大角度姿态控制问题,考虑航天器惯量矩阵中的不确定性和外部扰动力矩,应用高阶滑模控制方法设计了姿态跟踪控制律.采用的二阶滑模控制方法改善了系统针对不确定性及外部扰动的鲁棒性,并减弱了振颤现象.针对所设计的控制器进行了仿真验证,并与一阶滑模控制进行了对比,仿真结果表明了所提出方法的有效性.     

Faster ARMA maximum likelihood estimation

A new likelihood based AR approximation is given for ARMA models. The usual algorithms for the computation of the likelihood of an ARMA model require O(n) flops per function evaluation. Using our new approximation, an algorithm is developed which requires only O(1) flops in repeated likelihood evaluations. In most cases, the new algorithm gives results identical to or very close to the exact maximum likelihood estimate (MLE). This algorithm is easily implemented in high level quantitative programming environments (QPEs) such as Mathematica, MatLab and R. In order to obtain reasonable speed, previous ARMA maximum likelihood algorithms are usually implemented in C or some other machine efficient language. With our algorithm it is easy to do maximum likelihood estimation for long time series directly in the QPE of your choice. The new algorithm is extended to obtain the MLE for the mean parameter. Simulation experiments which illustrate the effectiveness of the new algorithm are discussed. Mathematica and R packages which implement the algorithm discussed in this paper are available [McLeod, A.I., Zhang, Y., 2007. Online supplements to “Faster ARMA Maximum Likelihood Estimation”, 〈http://www.stats.uwo.ca/faculty/aim/2007/faster/〉]. Based on these package implementations, it is expected that the interested researcher would be able to implement this algorithm in other QPEs.