基于模糊树模型的非线性系统内模控制

本文提出一种基于模糊树模型的非线性系统的内模控制方法,该方法采用模糊树建立非线性系统的内部模型和逆模型.仿真结果表明模糊树方法建立的非线性系统内部模型和逆模型均具有较高的建模精度,所提内模控制方法对非线性系统具有较好的控制性能、较强的抗干扰能力和鲁棒性能.     

基于神经网络逆系统的无轴承异步电机非线性内模控制

针对无轴承异步电机非线性、多变量、强耦合的特点,提出一种基于神经网络 α阶逆系统方法的非线性内模控制策略.将用动态神经网络逼近的无轴承异步电机 α阶逆模型与原系统复合,将非线性的无轴承异步电机原系统解耦成转子径向位移、转 速和转子磁链四个独立的伪线性子系统.为了保证 系统的鲁棒性,对伪线性系统引入内模控制,仿真和实验研究验证了所提控制方法的有效性.     

基于灰色预测的自适应内模PID双重控制器设计

针对一类非线性系统,提出一种基于灰色预测的自适应内模PID双重控制方法.把由系统的输入输出数据得到的灰色预测模型作为系统的内部模型,并在基本的内模控制结构上增加PID控制器,加快了跟踪误差收敛速度, 内模控制的性能明显改善.仿真结果表明,该控制方法简单而有效, 内模PID双重控制较单一内模控制具有更好的系统性能.     

一种基于遗传算法的模糊内模控制方法

针对一类非线性过程,提出了基于T-S模糊模型的非线性内模控制方法.使用遗传算法和模糊聚类方法进行模糊建模,解决了非线性内模控制方法中建立精确的模型及其逆模型困难的问题.通过模糊辨识获得过程的T-S模型及逆模型,并以此设计了内模控制器.最后,将该方法应用于一类非线性过程的控制,仿真结果表明该方法的有效性.     

基于动态神经网络的非线性内模控制

针对一类不确定仿射非线性系统，提出一种基于动态神经网络的非线性内模控制方法。利用该网络模型存在相对阶时可以解析求得逆模型的特点，避免了普通神经网络内模控制方案中求逆的困难。并在有建模误差的情况下，通过将非线性对象输入输出线性化，分析了闭环系统的鲁棒稳定性和稳态性能。仿真试验表明该方法是可行和有效的。     

基于模糊模型的非线性内模控制策略研究

针对一类非线性动态过程提出了基于模糊模型的非线性内模控制算法(NFIMC)。NFIMC控制器包括逆模糊模型控制器和滤波器。过程的模糊模型和逆模糊模型均可由模糊辨识获得。CSTR的仿真结果表明:该算法可以对强非线性过程实现有效控制,并且具有结构简单、计算效率高等优点,有利于在线应用。     

神经模糊逆模/PID复合控制在CSTR中的应用

研究了基于广义基函数神经模糊模型的逆系统实现及其直接逆模控制,并提出将直接逆模控制与PID反馈控制相结合的复合控制策略,该控制策略已应用于CSTR的反应浓度控制,仿真结果表明,神经模糊逆模／PID复合控制能克服因辨识逆模型不精确引起的缺陷,并具有良好控制性能。     

基于α阶逆的大时滞非线性动态矩阵控制

针对一类大时滞非线性系统,提出了基于α阶逆的动态矩阵控制新方法.该方法采用BP神经网络辨识逼近原非线性系统的α阶逆系统,并与原系统串联复合组成伪线性系统;采用基于线性系统的动态矩阵预测控制方法设计系统附加控制器.在系统存在建模误差、存在扰动和模型参数发生较大变化等情况下,采用该控制方法依然具有很好的动、静态性能和很强的鲁棒性.给出了详细的设计原理和步骤,并通过大量的仿真分析与已有的大时滞非线性系统内模控制研究结果进行了比较:内模控制依赖于系统模型,当模型出现严重失配的情况下,系统性能变坏,而采用提出的方法则不依赖系统精确的数学模型,计算量小,简化了非线性系统的设计;研究与仿真结果证明了所提控制方法的有效性.     

动态补偿逆的非线性内模控制在机器人中的应用

针对机器人的非线性不确定性和传统非线性内模控制在控制上存在的不足．提出一种基于动态补偿逆的非线性不确定系统RBF内模控制,在引入RBF建立逆模型的同时．将无模型自适应控制方法作为附加控制器,用于在模型偏离被控对象时在线修正逆模型。仿真结果表明,本文提出的方法不仅对机器人系统的常量摄动具有较好的鲁棒性,对时变不确定性仍能保持较好的跟踪效果．具有较好的实时性、鲁棒性和在线校正功能。     

基于T-S模型的内模控制算法及其应用

针对模糊内模控制算法中模型的建立及模型求逆困难的问题，对一种模糊建模方法进行了改进，在此基础上提出了一种基于T—S模型的内模控制方法。采用启发性知识与复合非线性优化方法相结合的综合方法求解出模糊模型的结构，由模糊辨识获得过程的T—S模型和逆模型，并以此为基础建立内模控制算法。将该算法分别应用于慢时变非线性对象和具有大时延大惯性的热工系统主蒸汽温度的控制，仿真结果表明了该方法具有结构简单，计算效率高等优点，有利于在线应用。     

非线性不确定系统的OS-LSSVMR内模控制

针对非线性、不确定性对象内模控制不易精确建模的问题,提出OS-LSSVMR(online-sparse-least-squares-support-vector-machines-regression)在线调整模型的内模控制方法.首先介绍一种具有在线建模和稀疏性解的OS-LSSVMR;再采用OS-LSSVMR建立内模控制的正向模型,对模型可逆并且唯一的非线性系统设计逆模控制器;在模型偏离被控对象时在线修正正逆模型.仿真表明,该方法对非线性不确定性系统具有较好的实时性、鲁棒性和在谮线校正功能.     

基于核岭回归的非线性内模控制

提出一种基于核蛉回归(KRR)建模的内模控制策略.该方法充分利用基干结构风险最小化为学习规则的回归方法的非线性拟合性能,建立内模控制系统,从理论上分析了内模控制系统的稳定性和稳态误差同逆模与内模估计误差的关系问题.仿真表明,在训练样本有限和有噪声污染情况下,该系统较神经网络方法具有更好的控制性能.     

Repetitive control of servo systems with time delays

In many industrial robotic servo applications there is a need to track periodic reference signals and/or reject periodic disturbances. Moreover, time-delays are usually unavoidable in control systems due to the sensoring and communication delays. This paper presents an alternative repetitive control design for systems with constant time-delays in both forward and feedback control channels, which are dedicated to track/reject periodic signals. An additional delay is introduced together with the plant delays to construct an internal model for periodic signals, and a simple compensator based on the plant model inverse is utilized to stabilize the closed-loop system. Sufficient stability conditions of the closed-loop system and the robustness analysis against modeling uncertainties are studied. The proposed idea is further extended for general time-delay systems with only a delay term in the forward control channel. The “plug-in” structure used in conventional repetitive control designs is avoided, so that it leads to a simpler control configuration, i.e. only a proportional parameter and the cutoff frequency of a low-pass filter are required to be selected. Simulations based on a hard disk drive system and practical experiments on a rotary robotic servo system are provided to evaluate the effectiveness of the proposed method.     

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

A new modeling approach for nonlinear systems with rate-dependent hysteresis is proposed. The approach is used for the modeling of the giant magnetostrictive actuator, which has the rate-dependent nonlinear property. The models built are simpler than the existed approaches. Compared with the experiment result, the model built can well describe the hysteresis nonlinear of the actuator for input signals with complex frequency. An adaptive direct inverse control approach is proposed based on the fuzzy tree model and inverse learning and special learning that are used in neural network broadly. In this approach, the inverse model of the plant is identified to be the initial controller firstly. Then, the inverse model is connected with the plant in series and the linear parameters of the controller are adjusted using the least mean square algorithm by on-line manner. The direct inverse control approach based on the fuzzy tree model is applied on the tracing control of the actuator by simulation. The simulation results show the correctness of the approach. Supported by the National Natural Science Foundation of China (Grant No. 60534020), the National Basic Research Program of China (Grant No. G2002cb312205-04), the Research Fund for the Doctoral Program of Higher Education (Grant No. 20070006060), and the Key Subject Foundation of Beijing (Grant Nos. XK100060526, XK100060422)     

基于逆系统迭代学习观测器的故障调节方法

针对一类满足Lipschitz条件的多输入多输出非线性可逆系统执行器故障问题,提出了一种基于迭代学习观测器的逆系统内模故障调节方法。引入PD型迭代学习策略,设计了迭代学习故障诊断观测器,用于对执行器未知时变故障进行快速、准确估计。根据故障估计值,结合逆系统方法对逆模型进行补偿,使得补偿后的逆模型与非线性被控对象串联仍为伪线性系统;再结合内模控制实现了伪线性系统的容错控制。最后,通过仿真算例验证了该方案的有效性。     

Neurofuzzy network based self-tuning control with offset eliminating

The design of nonlinear controllers involves first selecting the input and then determining the nonlinear functions for the controllers. Since systems described by smooth nonlinear functions can be approximated by linear models in the neighbourhood of the selected operating points, the input of the nonlinear controller at these operating points can be chosen to be identical to those of the local linear controllers. Following this approach, it is proposed that the input of the nonlinear controller are similarly chosen, and that the local linear controllers are designed based on the integrating and k-incremental suboptimal control laws for their ability to remove offsets. Neurofuzzy networks are used to implement the nonlinear controllers for their ability to approximate nonlinear functions with arbitrary accuracy, and to be trained from experimental data. These nonlinear controllers are referred to as neurofuzzy controllers for convenience. As the integrating and k-incremental control laws have also been applied to implement self-tuning controllers, the proposed neurofuzzy controllers can also be interpreted as self-tuning nonlinear controllers. The training target for the neurofuzzy controllers is derived, and online training of the neurofuzzy controllers using a simplified recursive least squares (SRLS) method is presented. It is shown that using the SRLS method, computing time to train the neurofuzzy controllers can be drastically reduced and the ability to track varying dynamics improved. The performance of the neurofuzzy controllers and their ability to remove offsets are demonstrated by two simulation examples involving a linear and a nonlinear system, and a case study involving the control of the drum water level in the boiler of a power generation system.     

Nonlinear control structures based on embedded neural system models

This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper.     

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

Real-time implementation of multi-linear model-based control strategies––an application to a bench-scale pH neutralization reactor

The real-time implementation of a set of multi-linear model-based control design methodologies is studied using a bench-scale pH neutralization system that exhibits nonlinear dynamics. It is envisaged that advanced model-based control strategy based on the multi-linear models presents a promising paradigm to design controllers for complex nonlinear plants. The multi-linear modeling philosophy is based on the selection of a set of linear models, complemented with an adaptation mechanism to explain the nonlinear plant behavior in the whole operating range. Practical implications of each control strategy are evaluated and discussed.     

A novel neural approximate inverse control for unknown nonlinear discrete dynamical systems.

A novel neural approximate inverse control is proposed for general unknown single-input-single-output (SISO) and multi-input-multi-output (MIMO) nonlinear discrete dynamical systems. Based on an innovative input/output (I/O) approximation of neural network nonlinear models, the neural inverse control law can be derived directly and its implementation for an unknown process is straightforward. Only a general identification technique is involved in both model development and control design without extra training (online or offline) for the neural nonlinear inverse controller. With less approximation made on controller development, the control will be more robust to large variations in the operating region. The robustness of the stability and the performance of a closed-loop system can be rigorously established even if the nonlinear plant is of not well defined relative degree. Extensive simulations demonstrate the performance of the proposed neural inverse control.