Probabilistic Robust Linear Parameter-varying Control of a Small Helicopter Using Iterative Scenario Approach

In this paper, we present an iterative scenario approach (ISA) to design robust controllers for complex linear parameter-varying (LPV) systems with uncertainties. The robust controller synthesis problem is transformed to a scenario design problem, with the scenarios generated by identically extracting random samples on both uncertainty parameters and scheduling parameters. An iterative scheme based on the maximum volume ellipsoid cutting-plane method is used to solve the problem. Heuristic logic based on relevance ratio ranking is used to prune the redundant constraints, and thus, to improve the numerical stability of the algorithm. And further, a batching technique is presented to remarkably enhance the computational efficiency. The proposed method is applied to design an output-feedback controller for a small helicopter. Multiple uncertain physical parameters are considered, and simulation studies show that the closed-loop performance is quite good in both aspects of model tracking and dynamic decoupling. For robust LPV control problems, the proposed method is more computationally efficient than the popular stochastic ellipsoid methods. © 2014 Chinese Association of Automation.

ARAIM可用性预测系统的设计与实现

随着全球导航卫星系统空间星座的增多以及双频技术的发展,用于航路阶段的常规接收机自主完好性监测算法不能满足LPV-200进近对完好性的要求,因此,出现应用于进近阶段的高级接收机自主完好性监测(ARAIM)技术。针对LPV-200进近,基于对垂直保护限值的理论分析,研究ARAIM可用性预测算法,并设计ARAIM瞬时可用性预测系统。仿真结果表明,该系统能满足LPV-200进近对完好性的要求,在中国区域内,当可用性设置为99.9%时,ARAIM预测算法的覆盖率可达100%。

二阶Lipschitz非线性系统自然观测器设计

机械系统的动力学方程是用状态速度和状态加速度描述的二阶系统,而传统的基于一阶系统表示的观测器理论并不完全适用于二阶系统,为此,文中针对一类二阶Lipschitz非线性系统提出了自然观测器的设计问题。所提出的自然观测器与被观测的二阶系统具有相同的代数结构,能保证位置估计量的导函数恰好是速度估计量。为降低结果的保守性,利用非线性函数的Lipschitz性质,将估计误差系统表示为LPV(Linear Parameter Varying)系统,在此基础上,引入参数依赖的Lyapunov函数分析估计误差系统的稳定性,并建立了自然观测器存在的充分条件。该条件表示为一组带有可调参数的线性矩阵不等式,通过求解该组线性矩阵不等式,便可获得自然观测器的增益矩阵和估计误差的收敛速率。最后通过数值例子验证了本文结果的有效性和实用性。

基于鲁棒H_(∞)控制的无人机纵向自动驾驶仪设计

针对传统增益调度技术在许多无人机(unmanned aerial vehicle,UAV)应用中存在的昂贵和耗时的缺点,通过某型固定翼无人机的6自由度非线性模型,在已知气动系数、飞机转动惯量和推力系数的情况下,利用雅可比线性化方法从6自由度非线性模型推导出线性变参数(linear parameter varying,LPV)飞机模型。应用张量-乘积(tensor product,TP)模型变换方法将无人机纵向非线性参数依赖的LPV模型变换为TP型凸多面体模型形式,将增益调度输出反馈鲁棒H_(∞)控制器设计方法应用于所得到的TP凸多面体模型,设计了常规固定翼无人机的鲁棒增益预测自动驾驶仪飞行控制系统。在MATLAB SIMULINK环境下对该控制系统进行了全六自由度的飞行器仿真试验,结果表明闭环控制系统具有良好的指令跟随性和干扰抑制能力,在定义的飞行包线范围内具有较好的稳定性和鲁棒性,验证了所提出的飞行控制系统设计方法的有效性。

Direct thrust control for multivariable turbofan approach

A novel turbofan Direct Thrust Control(DTC)architecture based on Linear ParameterVarying(LPV)approach for a two-spool turbofan engine thrust control is proposed in this paper.Instead of transforming thrust command to shaft speed command and pressure ratio command,the thrust will be directly controlled by an optimal controller with two control variables.LPV model of the engine is established for the designing of thrust estimator and controller.A robust LPV H∞filter is introduced to estimate the unmeasurable thrust according to measurable engine states.The thrust estimation error system is proved to be Affinely Quadratically Stable(AQS)in the whole parameter box with a prescribed H∞performance indexγ.Due to the existence of overdetermined equations,the solving of controller parameters is a multi-solution problem.Therefore,Particle Swarm Optimization(PSO)algorithm is used to optimize the controller parameters to obtain satisfactory control performance based on the engine’s LPV model.Numerical simulations show that the thrust estimator can acquire smooth and accurate estimating results when sensor noise exists.The optimal controller can receive desired control performance both in steady and transition control tasks within the engine working states above the idle,verifying the effectiveness of the proposed DTC architecture’s application in thrust direct control problem.