Adaptive switching control of discrete time nonlinear systems based on multiple models

We use the approach of “optimal” switching to design the adaptive control because the design among multiple models is intuitively more practically feasible than the traditional adaptive control in improving the performances. We prove that for a typical class of nonlinear systems disturbed by random noise, the multiple model adaptive switching control based on WLS (Weighted Least Squares) or projected-LS (Least Squares) is stable and convergent.

Adaptive switching control of a class of nonlinear systems based on mixed multiple models

The transient behaviors of traditional adaptive control may be very poor in general. A practically feasible approach to improve the transient performances is the adoption of adaptive switc- hing control. For a typical class of nonlinear systems disturbed by random noises, mixed multiple models consisting of adaptive model and fixed models were considered to design the switching con- trol law. Under certain assumptions, the nonlinear system with the switching control law was proved rigorously to be stable and optimal A simulation example was provided to compare the performance of the switching control and the traditional adaptive control.

Multiple-model-and-neural-network-based nonlinear multivariable adaptive control

A multivariable adaptive controller feasible for implementation on distributed computer systems （DCS） is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.

Multiple models adaptive feedforward decoupling controller

When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of multiple fixed models, one free-running adaptive model and one re-initialized adaptive model. The fixed models are used to provide initial control to the process. The re-initialized adaptive model can be reinitialized as the selected model to improve the adaptation speed. The free-running adaptive controller is added to guarantee the overall system stability. At each instant, the best system model is selected according to the switching index and the corresponding controller is designed. During the controller design, the interaction is viewed as the measurable disturbance and eliminated by the choice of the weighting polynomial matrix. It not only eliminates the steady-state error but also decouples the system dynamically. The gtobel convergence is obtained and several simulation examples are presented to illustrate the effectiveness of the proposed controller.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.

Novel robust fault diagnosis method for flight control systems

A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model （AMM） and unknown input observer （UIO）. The main idea of the proposed scheme stems from the fact that the actuator Lock-in-Place fault is unknown （when and where the actuator gets locked are unknown）, and multiple models are used to describe different fault scenarios, then a bank of unknown input observers are designed to implement the disturbance de-coupling. According to Lyapunov theory, proof of the robustness of the newly developed scheme in the presence of faults and disturbances is derived. Numerical simulation results on an aircraft example show satisfactory performance of the proposed algorithm.

Equivalent series system to model a multiple friction pendulum system with numerous sliding interfaces for seismic analyses

Current structural analysis software programs offer few if any applicable device-specifi c hysteresis rules or nonlinear elements to simulate the precise mechanical behavior of a multiple friction pendulum system(MFPS) with numerous sliding interfaces.Based on the concept of subsystems,an equivalent series system that adopts existing nonlinear elements with parameters systematically calculated and mathematically proven through rigorous derivations is proposed.The aim is to simulate the characteristics of sliding motions for an MFPS isolation system with numerous concave sliding interfaces without prior knowledge of detailed information on the mobilized forces at various sliding stages.An MFPS with numerous concave sliding interfaces and one articulated or rigid slider located between these interfaces is divided into two subsystems: the fi rst represents the concave sliding interfaces above the slider,and the second represents those below the slider.The equivalent series system for the entire system is then obtained by connecting those for each subsystem in series.The equivalent series system is validated by comparing numerical results for an MFPS with four sliding interfaces obtained from the proposed method with those from a previous study by Fenz and Constantinou.Furthermore,these numerical results demonstrate that an MFPS isolator with numerous concave sliding interfaces,which may have any number of sliding interfaces,is a good isolation device to protect structures from earthquake damage through appropriate designs with controllable mechanisms.

STOCHASTIC ADAPTIVE SWITCHING CONTROL BASED ON MULTIPLE MODELS

It is well known that the transient behaviors of the traditional adaptive control may be very poor in general, and that the adaptive control designed based on switching between multiple models is an intuitively appealing and practically feasible approach to improve the transient performances. In this paper, we shall prove that for a typical class of linear systems disturbed by random noises, the multiple model based least-squares(LS) adaptive switching control is stable and convergent, and has the same convergence rate as that established for the standard least-squares-based self-tunning regulators. Moreover, the mixed case combining adaptive models with fixed models is also considered.

Automated postoperative blood pressure control

It is very important to maintain the level of mean arterial pressure （MAP）. The MAP control is applied in many clinical situations, including limiting bleeding during cardiac surgery and promoting healing for patient＇ s post-surgery. This paper presents a fuzzy controller-based multiple-model adaptive control system for postoperative blood pressure management. Multiple-model adaptive control （MMAC） algorithm is used to identify the patient model, and it is a feasible system identification method even in the presence of large noise. Fuzzy control （FC） method is used to design controller bank. Each fuzzy controller in the controller bank is in fact a nonlinear proportional-integral （PI） controller,whose proportional gain and integral gain are adjusted continuously according to error and rate of change of error of the plant output, resulting in better dynamic and stable control performance than the regular PI controller, especially when a nonlinear process is involved. For demonstration, a nonlinear, pulsatile-flow patient model is used for simulation, and the results show that the adaptive control system can effectively handle the changes in patient＇s dynamics and provide satisfactory performance in regulation of blood pressure of hypertension patients.

Adaptive control of a class of nonlinear time-varying systems with multiple models

The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.

Multiple Models Direct Adaptive Controller for a Stochastic Non-minimum Phase System

For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the region where the system parameters jump and improve the transient response,while another two adaptive models are used to guarantee the stability.Utilizing generalized minimum variance design method,it adopts the stochastic system estimation algorithm with optimal controller design method to identify the controller parameter directly.Finally,the global convergence is given.The simulation proves the effectives of the controller proposed.

计及动力部分损失的动车组精准停车自适应模糊控制

列车执行器未知故障引起的动力部分损失、列车模型参数不确定和未知外界干扰可能会造成停车精度降低进而影响运行效率,为解决上述问题影响下的动车组速度跟踪和精准停车问题,考虑设计一种输入饱和的自适应控制方案。首先,考虑列车的非线性,建立执行器复合故障下的多质点列车动力学模型,使用存在有界逼近误差的光滑非线性函数来逼近饱和函数,并根据中值定理将非仿射系统转换为仿射系统。其次,基于动车组故障模型,结合自适应模糊逻辑系统和反步法设计一种不需要执行器故障信息的自适应模糊容错控制器,利用自适应模糊逻辑系统逼近系统中未知外部干扰、模型不确定项等系统函数,并通过使用范数估计方法使得每个模糊逻辑系统只需要一个自适应参数,降低系统自适应更新律的复杂度。然后,根据李雅普诺夫稳定性定理证明闭环系统所有信号均有界,并且通过选取适当的控制器参数可保证跟踪误差收敛到原点附近任意小的邻域内。最后,选取CRH5型动车组作为控制对象进行仿真试验。仿真数据显示:控制器可以确保动车组平稳运行,同时使得最终停车误差在±0.5 cm以内,并且控制输入始终在受限范围内。仿真结果说明所设计的控制器可以在执行器复合故障和输入饱和同时存在的情况下完成预期控制目标并有效抑制外界干扰与模型不确定的影响。

基于模型偏差学习的交通信号自适应优化方法

城市交通信号配时优化是保证交通系统整体运行效率的前提条件。传统基于模型的信号控制方法往往基于历史统计数据,并且底层交通流模型不可避免地存在模型偏差,影响控制方案的科学性及其实际应用效果。考虑模型与数据融合驱动,提出一种基于模型偏差学习的交通信号控制自适应优化方法,以最小化路网行程时间为目标,建立信号控制最优化模型。首先,针对交通流预测模型的模型偏差,引入偏差函数表示预测模型与实际交通流状态间的误差;其次,建立基于径向基函数(RBF)神经网络的偏差函数,结合实际交通流数据对模型偏差进行学习,提高偏差函数的拟合效果;在此基础上,提出考虑模型偏差信息的自适应优化方法,以提高信号控制方法的控制性能。以小型测试路网与实际路网为例进行算例分析,基于SUMO仿真对本文提出的模型偏差学习方法进行验证,考虑不同交通流量条件,与固定配时、模型预测控制进行比较,分析其控制性能指标。结果表明,模型与数据融合驱动的信号控制自适应优化方法能有效提高预测模型的准确性,降低路网行程时间,与基于模型的控制和模型预测控制方法相比路网累积行程时间平均减少了38.3%和25.6%,提升了路网的实际运行效果。最后在宣城实际路网的仿真验证了方法的有效性。

基于协同变异与莱维飞行策略的教与学优化算法及其应用

针对教与学优化(TLBO)算法在处理优化问题时存在搜索不均衡、易陷入局部最优、综合求解性能弱等缺陷,提出一种基于均衡优化与莱维飞行策略的改进教与学优化算法ELMTLBO。首先设计精英均衡引导策略,通过种群中多个精英个体的均衡引导提高算法的全局寻优能力;其次在TLBO算法的学习者阶段后,利用自适应权重策略对莱维飞行产生的步长进行自适应缩量,以提高种群局部寻优能力,增强个体对复杂环境的自适应性;最后设计了变异算子池逃逸策略,通过多个变异算子的协同引导,提升算法的种群多样性。为验证算法改进的有效性,将EMLTLBO算法与侏儒猫鼬优化算法(DMOA)等先进的智能优化算法以及平衡教与学优化(BTLBO)算法、标准TLBO等同类型算法在15个国际测试函数上进行综合收敛性能比较。统计实验结果表明,与先进的智能优化算法和TLBO算法变体相比,ELMTLBO算法能够有效平衡其搜索能力,不但有效求解单峰和多峰问题,而且在复杂多峰问题上仍有显著的寻优能力。在不同策略的共同作用下,ELMTLBO算法的综合优化性能突出,全局收敛性能较为稳定。此外,ELMTLBO算法成功应用于基于隐马尔可夫模型(HMM)的多序列比对(MSA)问题中,优化后得到的高质量对齐序列可用于疾病诊断、基因溯源等,可为生物信息学提供算法支撑。

基于视觉反馈的桥吊自适应滑模防摇控制

为了提高桥式吊车的运行效率,提出了一种基于视觉反馈的桥吊控制方案。首先,采用单目视觉系统、改进的均值漂移算法和最小面积圆法,获取负载的摆角,同时,提出了一种信息融合方案,用于提高检测的精度。其次,提出使用自适应积分滑模面设计控制器。控制器中通过使用积分环节,提高系统的鲁棒性,用于应对视觉系统中反馈延迟的影响,自适应项则用于改善动态性能。此外,为了削弱抖振现象,设计了一种新的自适应趋近律。其中的自适应切换增益能够灵活抑制外部扰动,提高系统的鲁棒性。最后,利用李雅普诺夫理论分析了控制系统的稳定性,并通过仿真验证了所提控制器的有效性。

基于组合多模型的电液伺服系统重构控制设计

针对电液伺服系统潜在的执行器、传感器等组件故障问题,提出了自适应控制与组合多模型相结合的重构控制策略。首先,根据电液伺服系统的物理组成和工作原理建立数学模型,并在其基础上对典型组件故障进行建模;其次,设计了基于模型跟随自适应控制的重构控制器,并给出其稳定性证明;然后,根据故障类型搭建其固定模型以及自适应模型,组成多模型集,模型集中的每个模型都有与之相对应的控制器,且每个控制器间相互独立;接着,提出合适的切换机制对故障系统进行模型匹配,使其自动切换到性能指标最小的控制器。仿真结果表明,该重构策略能够有效地补偿组件故障产生的不利影响,具有良好的鲁棒性和重构效果;同时,与基于单一模型的自适应控制进行对比,显示出其在故障出现瞬间具有更好的瞬态性能,抗扰性相对更强,电液伺服系统的安全性与可靠性得到提高。

Condition Monitoring of CNC Machining Using Adaptive Control

In this work, an adaptive control constraint system has been developed for computer numerical control （CNC） turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint （ACC） machining systems for turning. AIS14140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.

CBTC系统高速适应性分析及改进方案

从车地无线通信、车载控制模型、轨旁设备应用等方面有针对性地研究了设计速度在160~200 km/h下基于通信的列车运行控制(CBTC,Communication Based Train Control)系统的高速适应性问题,提出了速度提升后CBTC系统相对应的改进方案。从理论上分析,传统CBTC系统在完成适配性改进后,能够满足此速度等级下安全运行的要求。

基于模拟退火的立式加工水基切削精度自适应控制

为了提高立式加工水基切削控制精度,提出基于模拟退火的立式加工水基切削精度自适应控制方法。首先建立立式加工水基切削数学模型;其次设计了立式加工水基切削系统PID控制器;最后基于模拟退火方法对PID控制器进行参数优化,实现立式加工水基切削精度的自适应控制。测试结果表明:所提方法在0.25 s即完成控制动态响应;在阻尼系数、转轴加速度、进给量发生改变时,仍能保持切削力稳定,鲁棒性较强;噪声干扰下切削力偏差值小于45.3 N,切削自适应控制精度较高。