基于动态BP网络误差修正的广义预测控制

针对建模误差对非线性系统预测控制鲁棒性的影响 ,提出了一种基于动态 BP网络的广义预测控制算法 .该算法运用动态 BP网络对模型预测误差进行在线补偿 ,以提高预测精度 .仿真结果证明了本文提出的广义预测控制算法对于非线性系统是有效的     

卫星姿态跟踪的间接自适应模糊预测控制

对含模型不确定性和未知干扰的卫星姿态系统提出了具有间接自适应模糊补偿的广义预测跟踪控制方法. 首先基于卫星姿态动力学模型设计了非线性广义预测控制律, 再利用自适应模糊系统逼近预测控制律中的模型不确定项, 使得所得到的预测控制算法可实施.证明了当卫星姿态模型中不确定项满足一定条件时, 所设计的控制律可使卫星姿态跟踪误差收敛到原点的小邻域内,并仿真结果验证了所提出方法的有效性.     

粒子群优化的收敛分析及在广义预测控制中的应用

在进行粒子群优化的收敛性理论分析的基础上,推出了保证粒子群优化算法收敛性的参数设置区域,合理选择粒子群算法的关键参数,将粒子群优化与广义预测控制有机融合,用粒子群算法来解决广义预测控制的优化问题,提出基于粒子群优化的广义预测控制算法,通过工业过程对象的仿真并和传统的广义预测控制算法进行了对比分析,表明了该算法的有效性,特别是算法具有良好的输出跟踪精度和较强的鲁棒性.     

Hénon混沌系统的模糊广义预测控制与同步

针对Hénon混沌系统,本文给出了一种基于T-S模糊模型的混沌系统广义预测控制算法。该方法将模糊辨识和广义预测控制结合起来应用到Hénon混沌系统中。首先,应用T-S模糊模型对Hénon混沌系统进行辨识,模糊聚类法辨识模型的前件参数,递推最小二乘法辨识结论参数。基于辨识模型,采用广义预测控制算法对其进行控制,实现了系统的跟踪与同步。仿真结果表明,与其它算法相比,该算法能够保证系统输出快速、有效地跟踪设定值。     

广义双线性系统的输出变结构控制

首次利用广义Lyapunov函数方法针对不确定广义双线性系统的输出变结构控制问题进行研究.首先,选取设计了带有滑动模动态补偿器的切换函数,保证了系统在准切换流形上的渐近稳定性.其次,在不确定参数和扰动范数有界的条件下,设计了变结构控制器,使得在控制下闭环系统在有限时间内实现滑模运动.最后,通过数值算例说明了设计方法的合理性和有效性.     

非线性多时滞系统的直接自适应模糊控制

针对一类单输入单输出非线性多时滞系统,提出了一种自适应模糊跟踪控制方案.该方案结合了自适应控制和H∞控制.构建了自适应时滞模糊逻辑系统用来逼近未知时滞函数;设计了H∞补偿器来抵消模糊逼近误差和外部扰动.根据跟踪误差给出了参数调节规律.证明了误差闭环系统满足期望的H∞跟踪性能.仿真结果表明了该方案的有效性.     

基于预测控制的直接侧向力气动力复合控制方法研究

针对大气层内拦截导弹直接侧向力与气动力复合控制系统设计问题, 首先, 根据发动机的配置建立了复合控制系统模型;其次, 提出了复合控制策略, 包括动态分配算法、直接力控制子系统、气动力控制子系统3部分; 然后, 在考虑两套执行机构动态特性差别的情况下,基于预测控制思想给出了过载误差动态分配算法; 在此基础上, 根据直接侧向力的离散特性, 基于预测控制方法设计了直接力控制规律, 考虑到直接力控制作用对弹体产生的扰动, 基于自抗扰方法设计了气动力子系统; 最后, 通过仿真验证了直接力气动力复合控制策略与方法的有效性.     

离散多时滞广义不确定系统的变结构控制

研究了线性离散多时滞广义不确定系统的变结构控制的综合与设计问题.首先引入了一种新的受限系统等价分解形式,把所给的系统分解成两个低维的子系统:一个是不带控制项的差分系统;一个是带有控制项的差分系统.其次,根据上面的分解形式及离散时滞广义系统鲁棒稳定性的有关结果,设计了带有差分补偿器的切换函数,使得系统在准切换流形上的运动渐近稳定.然后在不确定项有界的条件下,设计了离散变结构控制律,使得在此控制律的作用下,系统从状态空间中任意一点出发的解的轨迹,于有限步内或者到达准切换流形,此后在准切换流形上渐近滑向原点;或者进入准切换流形的一个小邻域内,并稳定于原点邻域内的一个小的抖振.最后给出了数值例子以说明该综合设计方法的可行性与简便性.     

非仿射高阶关联大系统的分散模糊输出反馈控制

针对一类仅子系统输出变量可测的非仿射不确定非线性关联大系统,考虑其关联项是关于系统状态的普通高阶多项式,在无须严格正实(SPR)的条件下采用Leunberger观测器观测未知状态向量,结合模糊系统的万能逼近原理,监督控制技术和自适应控制技术,借助反证法提出一种新的分散自适应模糊输出反馈控制方案,该方案不仅取消了逼近误差平方可积的假设条件,而且控制增益隐含数一阶导数的上界是更具一般性的未知非线性函数,基于Lyapunov稳定理论分析证明了闭环系统的全局稳定性,跟踪误差收敛到零,仿真结果表明所提控制方案的有效性.     

不确定非线性系统的鲁棒自适应控制器

在ｂａｃｋｓｔｅｐｐｉｎｇ程序中，把非线性自适应控制和鲁棒控制连接起来，为参数化的严格反馈系统在不确定性存在的情况下，建立了一种鲁棒自适应控制方案．非线性自适应控制被用来处理系统的线性参数化部分，而鲁棒控制通过引进非线性阻尼项被用来处理不确定性部分．与现有的方案不同，作者给出了非线性阻尼项的无限种选择，而不是仅仅一种选择．通过使用一种合适的选择，能够设计一个鲁棒自适应控制器．它不仅能够保证对不确定性的鲁棒性，而且能够使输出误差任意小，以及用较小的控制努力取得较好的性能．     

An improved generalized predictive control algorithm based on the difference equation CARIMA model for the SISO system with known strong interference

ABSTRACT

The single input single output (SISO) system with known strong interference is widely used in various occasions. Due to its strong interference, the control accuracy is hard to guarantee. To solve this problem, an improved generalized predictive control (IGPC) algorithm is developed. The IGPC firstly builds the difference equation CARIMA (Controlled Auto-Regressive Integrated Moving-Average) model of the SISO system and then treats the system as a two input single output (TISO) system and calculates its predictive vector, then transforms it into a SISO system and uses the TISO system predictive vector to calculate the SISO system control increment. A new parameter called phase coefficient is added to inhibit the control lag. Simulations are performed to make the comparison among the traditional GPC, PID control, velocity synchronization control (VSC), fuzzy adaptive PID control (FAPID), model-based robust PID control (BPID) and the IGPC. Results show that IGPC has best performance compared to the others. Finally, experiments are developed which proved that the IGPC algorithm has a higher accuracy in the SISO system with known strong interference than that of VSC.     

用奇异值分解求解的PI型有约束广义预测控制算法

本文推得了一种比例积分型广义预测控制算法，在算法中加入了对系统的输入和输出信号的约束以加强算法应用能力，在控制值的优化示解中使用了矩阵奇异值分解以增加算法的精度和数值稳定性。     

Adaptive tracking control for a class of switched uncertain nonlinear systems under a new state-dependent switching law

This paper deals with the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and the adaptive backstepping and dynamic surface control techniques are adopted. First, a new state-dependent switching method is proposed. By introducing convex combination technique and designing a state-dependent switching law, only the solvability of the adaptive tracking control problem for a convex combination of the subsystems is necessary. Second, a new common Lyapunov function with switched adaptive parameters is constructed to reduce the conservatism. Third, to avoid Zeno behavior, a modified state-dependent switching law with dwell time is proposed. It is shown that under the proposed control and switching laws, all the signals of the closed-loop system are bounded and all the state tracking errors can converge to a priori accuracy, even if some subsystems are uncontrollable. Finally, the effectiveness of the proposed method is illustrated through two simulation examples.     

Design of adaptive LQ regulators for MIMO systems based on multirate sampling of the plant output

In this paper, the certainty equivalence principle is used to combine a discrete-time adaptive law with a control structure derived from the linear-quadratic regulation problem. The proposed control structure is mainly based on multirate sampling of the output of the continuous-time plant under control and allows us to regulate the sampled closed-loop system, subject to a quadratic performance criterion, without making assumptions on the plant other than controllability and observability and the knowledge of two sets of structural indices. Using the proposed algorithm, the adaptive regulation problem is reduced to the determination of a fictitious static state feedback controller. Known techniques usually resort to the computation of fullorder adaptive state observers, thus introducing high-order exogenous dynamics in the control loop. Moreover, persistence of excitation and parameter convergence of the plant are provided without making any additional assumption on the system, as compared to known adaptive linear-quadratic regulation schemes.     

Secure communications based on the synchronization of the hyperchaotic Chen and the unified chaotic systems

This paper deals with the adaptive synchronization of two identical hyperchaotic master and slave systems. The master system and the slave system each consists of two subsystems: a hyperchaotic Chen subsystem and a unified chaotic subsystem. The asymptotic convergence of the errors between the states of the master system and the states of the slave system is proven using Lyapunov theory. Simulation results are presented to illustrate the ability of the control law to synchronize the master and slave systems. Moreover, the proposed control scheme is applied to encrypt and decrypt discrete signals such as digital images where computer simulation results are provided to show that the proposed control law works well.     

A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems

An observer-based adaptive controller developed from a hierarchical fuzzy-neural network (HFNN) is employed to solve the controller time-delay problem for a class of multi-input multi-output (MIMO) non-affine nonlinear systems under the constraint that only system outputs are available for measurement. By using the implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the HFNN adaptive controller are derived. According to the design of the HFNN hierarchical fuzzy-neural network, the observer-based adaptive controller can alleviate the online computation burden. Moreover, the common adaptive controller is utilized to control all the MIMO subsystems. Hence, the number of adjusted parameters of the HFNN can be further reduced. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and that the outputs of the closed-loop system track asymptotically the desired output trajectories.     

The Waveform Relaxation method for systems of differential/algebraic equations

This paper reports efforts towards establishing a parallel numerical algorithm known as Waveform Relaxation (WR) for simulating large systems of differential/algebraic equations. The WR algorithm was established as a relaxation based iterative method for the numerical integration of systems of ODEs over a finite time interval. In the WR approach, the system is broken into subsystems which are solved independently, with each subsystem using the previous iterate waveform as “guesses” about the behavior of the state variables in other subsystems. Waveforms are then exchanged between subsystems, and the subsystems are then resolved repeatedly with this improved information about the other subsystems until convergence is achieved.

In this paper, a WR algorithm is introduced for the simulation of generalized high-index DAE systems. As with ODEs, DAE systems often exhibit a multirate behavior in which the states vary as differing speeds. This can be exploited by partitioning the system into subsystems as in the WR for ODEs. One additional benefit of partitioning the DAE system into subsystems is that some of the resulting subsystems may be of lower index and, therefore, do not suffer from the numerical complications that high-index systems do. These lower index subsystems may therefore be solved by less specialized simulations. This increases the efficiency of the simulation since only a portion of the problem must be solved with specially tailored code. In addition, this paper established solvability requirements and convergence theorems for varying index DAE systems for WR simulation.     

Strict Chebyshev approximation for general systems of linear equations

Summary An ascent exchange algorithm for computing the strict Chebyshev solution to general systems of linear equations is presented. It uses generalized exchange rules to ensure convergence and splits up the entire system into subsystems by means of a canonical decomposition of a matrix obtained by Gaussian elimination methods. All updating procedures are developed and several numerical examples illustrate the efficiency of the algorithm.