Instability detection of ARMA systems based on AR system identification

The ability of parametric autoregressive (AR) system identification methods to detect the instability of an autoregressive moving average (ARMA) system of an unknown order is investigated. The collection of least squares AR estimators of various orders is shown to have the capacity to detect the instability of the underlying system. Necessary information is not the order of the system but, instead, an upper bound of the number of unstable poles with the maximal magnitude outside the unit circle.     

A parameter set division and switching gain-scheduling controllers design method for time-varying plants

This paper presents a new technique to design switching gain-scheduling controllers for plants with measurable time-varying parameters. By dividing the parameter set into a sufficient number of subsets, and by designing a robust controller to each subset, the designed switching gain-scheduling controllers achieve a desired L₂-gain performance for each subset, while ensuring stability whenever a controller switching occurs due to the crossing of the time-varying parameters between any two adjacent subsets. Based on integral quadratic constraints theory and Lyapunov stability theory, a switching gain-scheduling controllers design problem amounts to solving optimization problems. Each optimization problem is to be solved by a combination of the bisection search and the numerical nonsmooth optimization method. The main advantage of the proposed technique is that the division of the parameter region is determined automatically, without any prespecified parameter set division which is required in most of previously developed switching gain-scheduling controllers design methods. A numerical example illustrates the validity of the proposed technique.     

Estimation of parameters in fractional subdiffusion equations by the time integral characteristics method

This paper presents an extension to the time integral characteristics method for estimation of parameters in fractional subdiffusion equations containing Riemann-Liouville and Caputo fractional time derivatives. The explicit representations of the fractional diffusion coefficient and order of fractional differentiation via a Laplace transform of the concentration field are obtained. A technique of optimal Laplace parameter determination by minimization of relative errors bounds is described. The effectivity of the proposed approach is illustrated by numerical example.     

Double chains quantum genetic algorithm with application to neuro-fuzzy controller design

This paper proposes a double chains quantum genetic algorithm (DCQGA), and shows its application in designing neuro-fuzzy controller. In this algorithm, the chromosomes are composed of qubits whose probability amplitudes comprise gene chains. The quantum chromosomes are evolved by quantum rotation gates, and mutated by quantum non-gates. For the direction of rotation angle of quantum rotation gates, a simple determining method is proposed. The magnitude of rotation angle is computed by integrating the gradient of the fitness function. Furthermore, a normalized neuro-fuzzy controller (NNFC) is constructed and designed automatically by the proposed algorithm. Application of the DCQGA-designed NNFC to real-time control of an inverted pendulum system is discussed. Experimental results demonstrate that the designed NNFC has very satisfactory performance.     

Differential constraints for bounded recursive identification with multivariate splines

The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and bounded model extrapolation in the framework of a model-based control system. A new type of linear constraints, the differential constraints, are used as differential boundary conditions within the recursive estimator which limit polynomial divergence when extrapolating data. The differential constraints are derived with a new, one-step matrix form of the de Casteljau algorithm, which reduces their formulation into a single matrix multiplication. The recursive estimator is demonstrated on a bivariate dataset, where it is shown to provide a speedup of two orders of magnitude over an ordinary least squares batch method. Additionally, it is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation.     

A design of bilateral teleoperation systems using composite adaptive controller

This paper presents a synchronization scheme of bilateral teleoperation systems using composite adaptive controller. To design a controller for bilateral teleoperation systems, all the parameters of the master and the slave robots need to be known. However, there exist parameter uncertainties in the robot manipulators. A composite adaptive controller is designed for convergence of states and parameters of the master and the slave robots in the presence of parameter uncertainties. Consequently, position and force tracking problems in free and contact motion are solved in a synchronized manner. Through a number of simulations, the superiority of the proposed method over existing works is illustrated. Furthermore, for the validation of utility of the proposed method in an actual embedded system, the algorithms are implemented and tested in FPGA-based hardware controller.     

基于控制参数化的切换系统最优控制方法研究

针对一类特殊的混杂系统——切换系统的最优控制问题．利用控制函数参数化方法和时间尺度转化法,将切换系统最优控制问题转化为最优参数选择问题,可利用Miser3．2来求解该问题,这样能有效地简化求解切换系统最优控制的难度,而且算法易于理解,编程易于实现。     

人工智能问题中的相变现象研究

在人工智能研究领域的很多问题中存在着相变现象(phase transition)。问题中的某个参数发生较小的变化便会引起问题的某个属性发生突变。相变现象与问题求解结构有很大关系,将从相变现象的发展、相关概念以及相变现象与问题求解的关系等方面对相变现象做全面的论述,提出了有待解决的开放性问题,并对相变现象的研究给以总结和展望。     

预测控制的预报误差校正法研究

基于时间序列方法对模型误差过程建立模型，用递推最小二乘法估计模型误差过程的参数，由最小方差预报原理给出模型误差的预报，在上原理的基础上，对误差预报校正器进行了设计，并在动态知控制（DMC）中，用预报误差校正代替一般的误差校正，经过系统仿真比较，可以看出误差预后和误差预报校正的结果令人满意。