高阶惯性系统自适应控制研究

热力系统中的热工过程大多属于高阶陨性系统。高阶惯性系统是一类多容、变化缓慢的动态系统，对此类系统采用一般的常规反馈控制方法是很难获得令人满意的控制效果的。本文应用IMC系统特性和模型参考自适应控制原理，并参照滞后系统Smith预估控制的方法，提出了控制高阶陨性系统的自适应控制新方法，仿真研究及现场实际运行均获得满意的控制效果。     

基于神经网络的卷烟共线分拣系统自适应控制

针对目前方法自适应控制卷烟共线系统时,由于未能依据Lyapunov函数确定系统的控制规律,导致在实施系统自适应控制时,存在控制效果差、控制误差高和控制性能低的问题,提出基于神经网络的卷烟共线分拣系统自适应控制方法。首先依据Lyapunov函数确定系统的控制规律,建立对象控制模型并使用前馈神经网络训练模型,优化控制器参数,完成控制器的设计;再利用控制器的参数建立线性和非线性2种自适应控制方法;最后通过制定的切换规则,完成自适应算法的平滑转换,实现系统的自适应控制。实验结果表明,运用该方法控制系统时,控制效果好、控制误差低以及控制性能高。     

船用天然气发动机电子节气门自适应高阶滑模控制

针对船用天然气发动机电子节气门非线性控制问题以及高阶滑模控制存在的边界难以估计问题,提出了一种基于高阶 滑模理论的节气门自适应控制算法,设计了基于系统相平面轨迹收敛过程的自适应策略,为了增加控制算法的实用性,在自适 应策略的基础上设计了检测区域,通过判断系统状态与该区域的相对位置双向调节控制增益,以防止增益过大而导致控制精度 降低、控制能量浪费的问题;同时,采用鲁棒微分估计器,对不可观测量进行估计;最后,设计 3 种测试方案,将该算法与传统高 阶滑模算法进行实验对比。 实验结果表明:在阶跃信号下,该算法使系统响应速度提高 35% ,稳态误差均方根减小 37. 5% ;在正 弦信号下,系统最大稳态误差和稳态误差均方根分别减小 30% 和 22% 。     

自适应同步控制策略在3-PRR平面并联机构中的应用

本文以三自由度P—R—R型平面并联机构为研究对象,建立系统的动力学模型,结合同步控制和自适应控制两种控制方法的优缺点,提出了一种新颖的控制方法,称为自适应同步控制,用来提高并联机构不确定参数的跟踪精度。通过用自适应同步控制策略对被控对象进行控制仿真,其结果表明,自适应同步控制方法的跟踪效果好,精度高,系统误差小,可以满足并联机构控制的要求,能够解决机构的轨迹跟踪问题,证实了自适应同步控制策略的正确性和有效性。     

使用低阶参考模型的HMRACS设计方法

利用高阶被控制对象跟随低价参考模型的方法来设计ＭＰＡＣＳ是自适应控制领域的研究课题之一。本文对文献［１］进行了改进，采用混合自适应控制器，从而提高了使用低阶参考模型的ＭＲＡＣＳ的鲁棒性。     

一类不确定性时滞系统的鲁棒控制器设计

本文讨论了一类非匹配时滞系统的鲁棒自适应控制和非线性控制问题.所讨论系统包含时滞及不确定性扰动项,且扰动项关于具有未知增益的高阶多项式函数有界而不是关于线性函数有界.利用Lyapunov-Krasovskii方法提出了保证系统渐近稳定的自适应控制器设计方法,相应的控制器是时滞依赖的.并利用Razumikhin引理给出了能使系统一致最终有界稳定的时滞独立的控制器设计方法.     

多关节机械臂的高阶滑模神经网络自适应控制

本文针对多关节机械臂提出了 一种高阶滑模神经网络自适应控制策略.在机械臂的动力学方程的基础之上,设置了滑模面,并对该滑模面求二阶导数,利用高阶滑模控制理论设计了机械臂的控制方案;高阶滑模控制分两步实施,针对标称系统采用了齐次连续控制项,对系统中存在外部干扰的情况添加了补偿项,并对系统中存在的不确定性采用RBF神经网络进行逼近.最后,应用李雅普诺夫稳定性理论证明了系统的稳定性,并通过MATLAB/Simulink仿真与传统滑模控制比较,表明了该控制算法有效地提高了轨迹的跟踪速度和精度,降低了系统中存在的抖颤.     

基于Backstepping的电液伺服系统多级自适应滑模控制

针对电液伺服系统的非线性特性、系统参数及外部负载的非匹配不确定性,在电液伺服系统的位置跟踪控制中,提出了基于Backstepping逆向递推技术的多级自适应滑模控制方法,应用Backstepping的逆向递推方法有效地解决了高阶系统的控制问题,并结合了自适应方法和滑模控制方法各自优良的抗干扰特点。仿真结果显示,该控制方法具有较强的鲁棒性及良好的跟踪性能。     

基于神经网络的刮板输送机链动态自动张紧控制策略研究

以刮板输送机链自动张紧系统为研究对象,通过控制机尾电机转速和伸缩液压缸的位移,使得机尾链轮上下链张紧力始终恒定,实现刮板链动态自动张紧控制,这是一个时变的两输入两输出耦合系统。本文根据解耦和神经网络的思想,在线系统辨识和调整两个PID控制器的参数,实现了不依赖于对象模型的自适应PID解耦控制。计算机仿真结果表明,该系统具有较强的抗干扰能力和自适应能力,系统鲁棒性增强,为今后进一步研究奠定了基础。     

基于神经网络的刮板输送机链动态自动张紧控制策略研究

以刮板输送机链自动张紧系统为研究对象,通过控制机尾电机转速和伸缩液压缸的位移,使得机尾链轮上下链张紧力始终恒定,实现刮板链动态自动张紧控制,这是一个时变的两输入两输出耦合系统。本文根据解耦和神经网络的思想,在线系统辨识和调整两个PID控制器的参数,实现了不依赖于对象模型的自适应PID解耦控制。计算机仿真结果表明,该系统具有较强的抗干扰能力和自适应能力,系统鲁棒性增强,为今后进一步研究奠定了基础。     

State and output feedback fuzzy variable structure controllers for multivariable nonlinear systems subject to input nonlinearities

This paper presents three fuzzy adaptive controllers for a class of uncertain multivariable nonlinear systems with both sector nonlinearities and dead zones: two first controllers are state feedbacks and the last controller is an output feedback. The design of the first controller concerns systems with symmetric and positive definite control–gain matrix, while the second control design is extended to the case of nonsymmetric control–gain matrix thanks to an appropriate decomposition, namely the product of a symmetric positive definite matrix, a diagonal matrix with diagonal entries +1 or ?1, and a unity upper triangular matrix. The third controller is an output feedback extension of the second controller. In this controller, a high-gain observer is incorporated to estimate the unmeasurable states. An appropriate adaptive fuzzy logic system is used to reasonably approximate the uncertain functions. A Lyapunov approach is adopted to derive the parameter adaptation laws and prove the stability of those control systems as well as the exponential convergence of their underlying tracking errors within an adjustable region. The effectiveness of the proposed fuzzy adaptive controllers is illustrated through simulation results.     

Adaptive control of uncertain continuously stirred tank reactors with unknown actuator nonlinearities

Adaptive nonlinear control is investigated for continuously stirred tank reactor (CSTR) systems using neural networks. The CSTR plant under study belongs to a class of nonaffine nonlinear systems, and contains an unknown parameter that enters the model nonlinearly. Using adaptive backstepping and neural network (NN) approximation techniques, an alternative adaptive NN controller is developed that achieves asymptotic output tracking control. A novel integral-type Lyapunov function, which includes both system states and control input as its arguments, is constructed to solve the difficulty associated with the nonaffine control problem. Numerical simulation is performed to show the feasibility of the proposed approach for chemical process control.     

基于模型参考自适应控制的直线电机位置控制

针对直线电机的强非线性和时变特性,采用模型参考自适应控制(MRAC)方法对SISO直线电机闭环位置控制器进行了研究。利用偶极子对消建立了简化的永磁直线电机二阶数学模型,提出了基于局部参数最优化MIT(梯度)方案和全局稳定性理论的Lyapunov方案下的二阶直线电机位置模型参考自适应控制器,并对自适应控制器下的直线电机闭环系统稳定性进行了分析研究。在相同的前馈加反馈的控制器下,对这两种方案下的实验结果进行了对比分析。研究结果表明,基于Lyapunov第二方法设计的二阶控制器比MIT方法下的二阶控制器更能实现对三阶点到点轨迹输入信号的快速响应和跟踪,证实了直线电机位置自适应控制的有效性。     

Model-free adaptive fractional order control of stable linear time-varying systems

This paper presents a new model-free adaptive fractional order control approach for linear time-varying systems. An online algorithm is proposed to determine some frequency characteristics using a selective filtering and to design a fractional PID controller based on the numerical optimization of the frequency-domain criterion. When the system parameters are time-varying, the controller is updated to keep the same desired performances. The main advantage of the proposed approach is that the controller design depends only on the measured input and output signals of the process. The effectiveness of the proposed method is assessed through a numerical example.     

电液位置伺服系统的最佳——自适应控制设计

本文研究了电液位置伺服系统的参数自适应与系统响应最优化的组合控制设计问题。文章基于优化理论提出一种形式简单易于在线实现的最优自适应控制器,并通过仿真试验证明这种控制方法的有效性与合理性。     

Command filter based adaptive control of asymmetric output-constrained switched stochastic nonlinear systems

In this paper, adaptive tracking control problem is investigated for a class of switched stochastic nonlinear systems with an asymmetric output constraint. By introducing a nonlinear mapping (NM), the asymmetric output-constrained switched stochastic system is first transformed into a new system without any constraint, which achieves the equivalent control objective. The command filter technique is employed to handle the “explosion of complexity” in traditional backstepping design, and neural networks (NNs) are directly utilized to cope with the completely unknown nonlinear functions and stochastic disturbances existing in systems. At last, on the basis of stochastic Lyapunov function method, an adaptive neural controller is developed for the considered system. It is shown that the designed adaptive controller can guarantee that all the signals remain semi-globally uniformly ultimately bounded (SGUUB), while the output constraint is satisfied and the desired signal can be tracked with a small domain of the origin. Simulation results are offered to illustrate the feasibility of the newly designed control scheme.     

Discrete-time direct adaptive control for robotic systems based on model-free and if–then rules operation

An adaptive controller for a class of nonlinear discrete-time systems is proposed for robotic systems under the assumption that the parameters and structure of system dynamics are all unknown. This controller is designed with the concept of model-free adaptive control requiring only the input–output of the unknown plant. The robotic system has been generalized to be a nonaffine discrete-time system under reasonable assumptions. The adaptive scheme called fuzzy rules emulated network (FREN) is implemented as a direct controller. The IF–THEN rules for FREN have been defined by the knowledge according to the relation between input and output of the robotic system without any compensator for the unknown mathematical model or nonlinearities. The underlying physical specifications of robotic system such as the operating range, maximum joint velocity, and so on have been considered to initialize the membership functions and adjustable parameters of FREN. The adaptation scheme is developed according to convergence analysis established for both adjustable parameters and the tracking error. The performance of the proposed controller is validated by the experimental system with a 7-degrees-of-freedom robotic arm operated in velocity-mode control.     

Adaptive output feedback tracking of nonlinear systems with uncertain nonsymmetric dead-zone input

In this paper, the problem of adaptive practical tracking is investigated by output feedback for a class of uncertain nonlinear systems subject to nonsymmetric dead-zone input nonlinearity with parameters of dead-zone being unknown. Instead of constructing the inverse of dead-zone nonlinearity, an adaptive robust control scheme is developed by designing an output compensator including two dynamic gains based respectively on identification and non-identification mechanism. With the aid of dynamic high-gain scaling approach and Backstepping method, stability analysis of the closed-loop system is proceeded using non-separation principle, which shows that the proposed controller guarantees that all closed-loop signal is bounded while the output of system tracks a broad class of bounded reference trajectories by arbitrarily small error prescribed previously. Finally, two examples are given to illustrate our controller effective.     

基于自适应反演的超细金刚线张力控制系统

针对多线切割机张力控制系统存在库仑摩擦和摆角耦合等较强非线性特征,以及收放卷轮直径变化引起的参数不确定问题,提出了一种基于自适应反演的非线性补偿控制方法。该方法结合实验辨识和自适应参数控制器设计方法分别对多线切割机的多轴电机同步运动控制系统和不确定系统参数进行简化分析和在线估计,使用李雅普诺夫稳定性理论保证了系统全局渐近稳定性以及系统状态的有界性。仿真和实验结果表明,所设计的自适应控制器可以实现多轴同步运动,并将张力摆角控制在较小的范围内,获得更高的张力控制精度。     

Fuzzy time-optimal controller (FTOC) for second order nonlinear systems

The motivation behind this paper is to seek alternative techniques to achieve a near optimal controller for non-linear systems without solving the analytical problem. In classical optimal control systems, the system states and optimization co-state parameters generate a two-point boundary value problem (TPBVP) using Pontryagin’s minimum principle (PMP). The paper contributes a new fuzzy time-optimal controller to the existing fuzzy controllers which has two regular inputs and one bang-bang output. The proposed controller closely approximates the output of the classical time-optimal controller. Further, input membership function are tuned on-line to improve the time-optimal output. The new controller exhibits optimal behaviour for second order non-linear systems. The rules are selected to satisfy the stability and optimality conditions of the new fuzzy time-optimal controller. The paper describes a systematic procedure to design the controller and how to achieve the desired result. To benchmark the new controller performance, a sliding mode controller is used for guidance and comparison purpose. Simulation of three non-linear examples shows promising results. The work described here is expected to incite researcher’s interest in fuzzy time-optimal controller design.