基于FIGA的广义T-S模糊模型预测控制

利用广义T-S模糊模型对非线性系统具有万能逼近性得到系统的局部线性化，并利用一种快速综合性遗传算法（FIGA）对广义T-S模糊模型进行参数辨识，根据得到的线性化模型对系统采取广义预测控制（GPC）得到当前的控制动作。通过Matlab仿真证明了该方法的有效性。     

基于T-S模糊模型的非线性系统广义预测控制

针对离散非线性系统,提出一种基于T-S模糊模型的广义预测控制方法。该方法将采样点的T-S模糊模型转化为采样点线性模型与非线性误差叠加的线性形式,通过迭代修正非线性误差,使具有非线性误差的线性模型预测控制律逐渐逼近采样点T-S模糊模型预测控制律。同时,该预测控制方法也能适用于当系统受输入输出约束时的控制。仿真结果验证了所提出的TS模糊模型广义预测方法有效。     

柔顺机构动力学建模新方法

由于柔性杆大变形所引起的几何非线性因素的影响,柔顺机构动力学模型的建立变得更加复杂、困难。基于此,在充分考虑柔性杆大变形特性的基础上,基于简化思想,提出一种柔顺机构动力学建模的新方法。该方法主要以末端受纯弯矩、垂直力以及固定—导向等3种模式下的柔性杆为研究对象,根据欧拉—伯努利方程,并结合伪刚体模型所得边界条件,利用最小二乘原理,拟合柔性杆的变形曲线方程;通过求解变形曲线对时间的导数,得到其上任意点的速度,进而推出柔性杆的动能表达。基于伪刚体模型,根据功能转换关系,推导出柔性杆的变形势能。在此基础上,建立平行导向柔顺机构的动力学模型。最后,结合具体算例,通过对几种不同模型所得系统频率比较分析,验证了该方法的有效性。     

基于状态反馈线性化的阀控非对称缸系统模型预测控制

针对使用PID方法对阀控非对称液压缸位置控制中出现的超调问题,以及传统非线性模型预测控制优化求解计算时间较长的问题,提出了一种基于状态反馈线性化的阀控非对称缸模型预测控制方案。首先建立了阀控系统状态空间模型,运用微分几何理论讨论系统可反馈线性化的充要条件,并将非线性系统映射为新坐标空间内的线性系统模型;设计了反馈线性化模型预测控制器(Feedback Linearization Model Predictive Controller, FLMPC),讨论了线性系统下的约束问题,其中由于系统仿真预测时域远小于系统响应时间,对模型预测控制的损失函数加以修正。结果证明,在相同输入情况下,反馈线性化系统与原系统的位置误差满足控制需要,且在保证被控对象快速稳定控制的条件下,对比该算法与非线性模型预测控制的单步计算时间,证明该算法能够缩短计算时间。     

一类带有输入和状态约束的非线性稳定性约束模型预测控制的研究

在稳定性约束模型预测控制中,稳定性约束可从一级传递到另一级来限制用可控结构形式表示的系统状态向量大小。本文探讨了把线性模型预测控制扩展到非线性稳定性约束模型预测的控制（SCMPC）;在扩展的情况下,模型预测控制的最优化可直接用于非线性系统模型的控制,同时提供了稳定性约束模型预测控制用于带有输入和状态约束的非线性系统控制算法并将该算法用于仿真。仿真结果表明该算法是有效的。     

基于T-S模糊模型的辨识与控制

针对一类非线性系统,采用模糊辫识的方法建立系统的T-S模型,结合广义预测控制对模糊预测控制进行研究.应用模糊聚类和递推最小二乘法辫识T-S模糊模型的前提结构和结论参数.对于非线性系统来讲,T-5模糊模型有良好的描述特性,并结合广义预测控制的滚动优化,实现对非线性系统的有效控制.仿真结果说明了该算法的有效性.     

基于最小二乘支持向量机逆系统方法应用研究

针对流浆箱的内部机理模型,提出了一种基于最小二乘支持向量机逆系统的解耦控制方法。利用最小二乘支持向量机辨识得到流浆箱系统的逆模型,并采用逆系统思想,将流浆箱非线性系统解耦成多个相互独立的单入单出伪线性子系统。采用MATLAB对该解耦控制方法的有效性进行仿真验证,结果表明,该控制方法抗干扰性强,结构简单,工程上易于实现。     

全电式炮控非线性系统模型预测控制器设计

全电式炮控系统是一个强非线性的复杂控制对象,由于存在摩擦力矩、参数摄动等不确定内部因素,常规控制算法难于对其实现精确控制。针对提高全电式炮控非线性系统控制的性能,本文提出了一种基于模型预测控制的全电式炮控系统控制方法,并把菌群优化算法应用到非线性系统模型预测控制器设计。通过对控制目标的分析,将输入受限的非线性预测控制器设计问题转化为控制器参数寻优问题,并利用菌群优化算法来对参数进行寻优,提高了系统控制性能。文中对算法的稳定性进行了分析,并通过全电式炮控非线性系统实例对算法进行了验证。结果证明了算法的有效性和可行性,为全电式炮控非线性系统模型预测控制器的设计提供了一种有效的途径。     

基于粒子群优化的混合模型预测控制研究

为解决复杂非线性系统的控制精度不高,稳定性难于保证等问题、将预测控制技术应用于非线性控制中,提出了一种基于粒子群优化和混合模型的预测控制算法.混合模型预测控制算法使用模糊聚类和最小二乘法建立了系统的复合模犁,由带压缩因子的粒子群算法优化获得了非线性控制系统的控制量,在非线性模型上对模糊预测控制算法和混合模型预测控制算法...     

永磁同步电机模型预测控制的现状与发展

模型预测控制是一种优化控制算法,近年来已经成为电机控制领域研究的热点。模型预测控制根据过去和现在的信息,对系统的状态量进行预测,并综合考虑控制对象的预期值和控制量的变化等评价指标,得到最优的控制量。该文综述了基于线性模型预测的永磁同步电机的直接转矩控制、矢量控制、直接电流控制、混合控制等算法的特点,以及基于非线性模型预测的永磁同步电机控制的研究现状,阐述了模型预测控制理论与应用方面有待进一步研究的几个主要问题。     

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies.     

Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems

In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems. Second, an adaptive radial basis function neural network estimator-based continuous second order sliding mode control algorithm (CSOSMC-ANNE) is adopted. In CSOSMC-ANNE control methodology, a radial basis function neural network with adaptive parameters is exploited to approximate the unknown system parameters and improve performance against perturbations. Also, the discontinuous switching control of SOSMC is supplanted with a smooth continuous control action to completely eliminate the chattering phenomenon. The convergence and global stability of the closed-loop system are proved using Lyapunov stability method. Numerical computer simulations, with dynamical model of the nonlinear inverted pendulum system, are presented to demonstrate the effectiveness and advantages of the presented control scheme.     

基于神经网络的电动舵回路容错控制方法研究

为了克服电动舵回路故障和非线性因素的影响,提出一种基于神经网络误差反馈学习的电动舵机容错控制器设计方法,根据电动舵机系统动力学特征建立系统非线性数学模型,采用反馈误差学习控制律进行神经网络容错控制,由参考信号和实际输出信号对比产生的误差信号驱动神经网络学习从而产生控制信号,以达到实现容错重构控制的目的,仿真结果表明,神经容错网络控制器能够达到满意的控制效果。     

Multi-linear model set design based on the nonlinearity measure and H-gap metric

This paper proposes a model bank selection method for a large class of nonlinear systems with wide operating ranges. In particular, nonlinearity measure and H-gap metric are used to provide an effective algorithm to design a model bank for the system. Then, the proposed model bank is accompanied with model predictive controllers to design a high performance advanced process controller. The advantage of this method is the reduction of excessive switch between models and also decrement of the computational complexity in the controller bank that can lead to performance improvement of the control system. The effectiveness of the method is verified by simulations as well as experimental studies on a pH neutralization laboratory apparatus which confirms the efficiency of the proposed algorithm.     

Model-based adaptive sliding mode control of the subcritical boiler-turbine system with uncertainties

As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction.     

采用听觉传感策略的声品质主动控制

针对现有声品质主动控制多以心理声学参数为控制目标,声品质改善能力不足,提出一种基于听觉传感策略的声品质主动控制系统。为了使控制系统能够融入听觉非线性特性,建立了包括外/中耳模型和内耳模型的听觉传感模型,其中在基底膜建模时,考虑毛细胞对基底膜具有反馈运动调节作用,采用带宽时变的gammatone滤波器模型,该滤波器可模拟出听觉在频率和强度感知方面的非线性特性。然后,以听觉信号平方最小为控制目标,给出了基于听觉传感的主动控制系统结构,为了解决听觉传感环节的加入带来的算法计算量增加和收敛性变差问题,基于x滤波最小均方（Filtered-x least mean square,FLMS）算法,提出了延迟补偿结构与逆模型结构相结合的听觉控制算法。最后,以车速50km/h的汽车车内声为对象进行主动控制仿真,对控制前后的车内声进行了声品质客观与主观评价,并对评价结果进行比较与分析,主客观评价一致表明：与声压控制相比,听觉控制可以更好地改善听觉舒适性。     

Sliding mode fault tolerant control dealing with modeling uncertainties and actuator faults

In this paper, two sliding mode control algorithms are developed for nonlinear systems with both modeling uncertainties and actuator faults. The first algorithm is developed under an assumption that the uncertainty bounds are known. Different design parameters are utilized to deal with modeling uncertainties and actuator faults, respectively. The second algorithm is an adaptive version of the first one, which is developed to accommodate uncertainties and faults without utilizing exact bounds information. The stability of the overall control systems is proved by using a Lyapunov function. The effectiveness of the developed algorithms have been verified on a nonlinear longitudinal model of Boeing 747-100/200.     

Digital controller design for absolute value function constrained nonlinear systems via scalar sign function approach

In this paper, a scalar sign function-based digital design methodology is developed for modeling and control of a class of analog nonlinear systems that are restricted by the absolute value function constraints. As is found to be not uncommon, many real systems are subject to the constraints which are described by the non-smooth functions such as absolute value function. The non-smooth and nonlinear nature poses significant challenges to the modeling and control work. To overcome these difficulties, a novel idea proposed in this work is to use a scalar sign function approach to effectively transform the original nonlinear and non-smooth model into a smooth nonlinear rational function model. Upon the resulting smooth model, a systematic digital controller design procedure is established, in which an optimal linearization method, LQR design and digital implementation through an advanced digital redesign technique are sequentially applied. The example of tracking control of a piezoelectric actuator system is utilized throughout the paper for illustrating the proposed methodology.     

Robust friction compensation for submicrometer positioning and tracking for a ball-screw-driven slide system

Ball-screw-driven slide systems are largely used in industry for motion control applications. Their performance using standard proportional-integral-derivative (PID) control algorithm is unsatisfactory in submicrometer motion control because of nonlinear friction effects. In this article, controllers based on a bristle-type nonlinear contact model are developed and implemented for submicrometer motion. For submicrometer positioning, a proportional-derivative (PD) control scheme with a nonlinear friction estimate algorithm is developed, and its performance is compared with that of a PID controller. For tracking, a disturbance observer was added to reject external disturbances and to improve robustness. The experimental results indicate that the proposed controller has consistent performance in positioning with under 1.5% of steady-state error in the submicrometer range. For tracking performance, the proposed controller shows good and robust tracking with respect to parameter variation.     

Improving tracking performance of industrial SCARA robots using a new sliding mode control algorithm

This paper addresses the implementation of a new sliding mode control algorithm for high speed and high precision tasks, which is robust against variations in the robot parameters and load. The effects of nonlinear dynamics, which are difficult to model accurately, become prominent in high speed operations. This paper attempts to treat the nonlinear dynamics of a SCARA robot as a disturbance. Based upon this approach, a new sliding mode control algorithm is proposed, in which a switching control input can be obtained easily and is determined to satisfy the existence condition for sliding mode control. A graphic simulator is used to evaluate the proposed algorithm for a SCARA robot. Simulation results show that the proposed algorithm is robust against disturbances and can reduce the magnitude of chattering, which is an unavoidable problem in sliding mode control. Experiments are carried out to validate the simulated results with an industrial SCARA robot using DSPs.