基于干扰观测器的一类不确定非线性系统自适应二阶动态terminal滑模控制

针对一类不确定非线性系统的跟踪控制问题,在考虑建模误差、参数不确定和外部干扰情况下,以其拥有良好的跟踪性能以及强鲁棒性为目标,提出基于回归扰动模糊神经网络干扰观测器(recurrent perturbation fuzzy neural networks disturbance observer,RPFNNDO)的鲁棒自适应二阶动态terminal滑模控制策略.将回归网络、模糊神经网络和sine-cosine扰动函数各自优势相结合,给出一种回归扰动模糊神经网络结构,提出RPFNNDO设计方法,保证干扰估计准确性;构造基于带有指数函数滑模面的二阶快速terminal滑模面,给出其控制器设计过程,避免了滑模到达阶段、传统滑模的抖振问题,采用具有指数收敛的鲁棒项抑制干扰估计误差对系统跟踪性能的影响,利用Lyapunov理论证明闭环系统的稳定性;将该方法应用于混沌陀螺系统同步控制仿真实验,结果表明所提方法的有效性.     

基于非奇异快速终端滑模的轧机液压伺服位置系统反步控制

针对具有非线性、参数不确定性和未知负载扰动的非对称缸轧机液压伺服位置系统,提出一种基于模糊自适应观测器和非奇异快速终端滑模面的反步控制方法.首先,基于非奇异快速终端滑模面和双幂次趋近律完成非对称缸轧机液压伺服位置系统反步控制器的设计,并通过构造二阶滑模滤波器对虚拟控制量的微分信号进行估计,有效地避免了反步控制中的微分爆炸现象;然后,选用模糊自适应观测器对系统的不确定项进行逼近估计,并将输出的估计值引入到设计的控制器中进行补偿,有效地提高了系统的跟踪控制精度,且分析表明,所提出的控制方法能够保证闭环系统全局渐近稳定;最后,基于某650mm可逆冷带轧机液压伺服位置系统的实际参数进行仿真研究,并与常规线性滑模控制方法相比较,结果验证了所提出方法能够有效提高系统在整个全局过程的收敛速度和鲁棒稳定性.     

基于滤波器的浸入与不变自适应控制

针对机电伺服系统存在参数不确定、未建模动态及时变扰动这一问题,提出一种基于滤波器的浸入与不变自适应算法,该算法能够准确估计伺服系统中的未知参数.首先,构造系统状态及回归函数的滤波器,再根据滤波后的辅助变量构造参数估计器;然后,依据浸入与不变理论设计参数估计器中的辅助函数,从而保证参数估计误差的收敛性.此外,为了进一步降低集总扰动对系统闭环性能的影响,提出一种扰动观测器,这种扰动观测器结构简单,并且能保证估计误差的渐近稳定,从而有效地补偿系统中的未建模动态和外部扰动.最后,利用Lyapunov理论分别证明了参数估计器、扰动观测器及闭环系统的稳定性,仿真与实验结果验证了所提出的自适应方法及扰动观测器的有效性.     

一类高阶非线性系统的终端滑模复合控制

研究干扰观测器的控制与自适应终端滑模控制思想相结合,针对调整非线性系统跟踪误差,为了提高控制性能,提出一种适用于一类岛阶非线性系统的复合控制策略.通过引入干扰观测器对系统中存在的不确定进行估计,并利用估计结果对滑模控制输出进行补偿.根据干扰观测器的估计值设计自适应律,实现终端滑模控制器中的切换增益,根据系统扰动的大小进行自适应调节,从而改善由于固定切换增益所造成的滑模控制器输出量过大以及抖振等现象.基于Lyapunov理论证明了复合控制的稳定性,最后通过仿真进一步验证了控制策略的可行性及有效性.     

基于学习观测器的航天器指定时间跟踪控制

针对一类含有外部扰动和执行器故障的刚体航天器姿态控制系统,提出一种基于自适应学习观测器的指定时间容错控制器的设计方案.首先,系统性地给出一种改进型自适应学习观测器设计方案,基于自适应学习观测器框架,设计航天器姿态系统的学习观测器实现对系统的综合扰动值估计;然后,利用综合扰动的估计信息和滑模控制理论设计指定时间容错跟踪控制器,使得系统的姿态角能够在指定时间跟踪指令信号,系统的收敛时间可通过容错控制器的参数预先设置,且与系统的初始状态值无关;接着,基于Lyapunov稳定性理论验证含有故障的姿态控制系统能够在指定时间内稳定;最后,通过数值仿真,与已有的观测器和有限时间控制方案进行对比,表明所提出方案的有效性和可行性.     

基于模糊扰动观测器的PMSM积分滑模控制研究

为了克服传统永磁同步电机(Permanent magnet synchronous motor,PMSM)的滑模控制增益大容易产生抖振的问题,提出基于模糊观测器的PMSM积分滑模控制策略。采用新型趋近律设计积分滑模控制器取代传统的滑模控制器,提高系统的动态响应性能。结合模糊控制与自适应控制的特点,设计模糊扰动观测器,能够迅速有效地观测系统内部参数变化和外部扰动,并对积分滑模速度控制器进行前馈补偿,削弱系统抖振的同时提高了系统的鲁棒性。通过李雅普诺夫理论证明了该控制系统的稳定性。仿真及实验结果验证了该方法具有较强的鲁棒性,可以实现良好的跟踪效果并且无抖动。     

具有饱和死区非线性输入的自适应滑模跟踪控制

针对一类具有非对称饱和死区的扇区非线性输入系统,研究了自适应滑模输出反馈跟踪控制问题．在假设系统数学模型已知或未知的情形下,分别基于自适应观测器和模糊观测器,提出了滑模控制器的综合设计方案,并通过定义一类李亚普诺夫函数给出了自适应控制器和模糊逻辑系统的参数调整律．所提方法不仅具有对系统模型依赖性小的优点,而且增强了对扰动的鲁棒性,并保证了整个闭环跟踪误差系统的稳定性．最后,仿真结果验证了本方法的有效性．     

基于神经网络观测器的船舶轨迹跟踪递归滑模 动态面输出反馈控制

针对三自由度全驱动船舶速度向量不可测问题,考虑船舶模型参数和外部环境扰动均未知的情况,提出一种基于神经网络观测器的船舶轨迹跟踪递归滑模动态面输出反馈控制方法.该方法设计神经网络自适应观测器估计船舶速度向量,且利用神经网络逼近模型参数不确定项,综合考虑船舶位置和速度误差之间关系构造递归滑模面,再采用动态面控制技术设计轨迹跟踪控制律和参数自适应律,并引入低频增益学习方法消除外界扰动导致的高频振荡控制信号.选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

自适应比例–积分H2滑模观测器设计

传统的龙伯格观测器的观测精度极易受到未知外部扰动的影响.为了解决这个问题,本文设计了一种基于径向基神经网络的自适应比例–积分H₂滑模观测器,实现了参数不确定性和外部扰动下非线性系统的鲁棒确切估计.首先,利用径向基神经网络自适应逼近系统模型的复杂非线性项;其次,设计基于误差的线性滑模面,将比例–积分滑模项注入观测器中,使得滑模动态在有限时间内收敛于滑模面,实现对外部扰动和系统模型非线性的完全补偿;最后,基于H₂次优控制和区域极点配置,提出观测器参数自整定方法.通过对单连杆机器人的仿真结果表明,该方法能够保证非线性系统具有较好的鲁棒性和自适应性.     

直流降压变换器的降阶扩张状态观测器与滑模控制设计与实现

本文针对直流降压变换器的负载电阻扰动和输入电压变化等系统不确定因素对输出电压的影响,提出了基于降阶扩张状态观测器的滑模控制方法(SMC+RESO).首先设计降阶扩张状态观测器对系统状态,负载电阻扰动和输入电压变化进行估计,然后基于估计值利用滑模控制技术设计控制器,实现对直流降压变换器系统给定电压跟踪的快速性和准确性.值得注意的是,不同于文[1]所提出的基于扩张状态观测器的滑模控制方法(SMC+ESO),本文所提出的方法采用降阶扩张状态观测器,实现简单,且无需电流传感器,减小了实际应用的成本.利用Lyapunov稳定性定理从理论上证明了所设计的控制器可以保证闭环系统的稳定性.仿真和实验结果表明,与已有的基于扩张状态观测器的滑模控制方法相比,所提出的控制方法更好地改善了系统的跟踪性能和对干扰和不确定性的鲁棒性能,且减少了成本,但是牺牲了系统稳态性能.     

基于干扰观测器的非线性不确定系统自适应滑模控制

本文研究了一类基于非线性干扰观测器的多输入多输出非线性不确定系统的边界层自适应滑模控制方法并应用于近空间飞行器高精度姿态控制.考虑系统存在不确定性和外部干扰上界未知的情况,设计了基于干扰观测器的边界层自适应滑模控制器,以消除传统滑模控制中的"抖振"现象,使跟踪误差趋近于零.同时,利用李雅普洛夫方法严格证明了闭环系统的稳定性.最后将所研究的自适应滑模控制方法,应用于某近空间飞行器的姿态控制中,仿真结果表明在不确定性和外部干扰作用下能保证姿态控制的稳定性,对参数不确定具有较好的鲁棒性.     

Adaptive complementary sliding-mode control for thrust active magnetic bearing system

An adaptive complementary sliding-mode control (ACSMC) system with a multi-input-multi-output (MIMO) recurrent Hermite neural network (RHNN) estimator is proposed to control the position of the rotor in the axial direction of a thrust active magnetic bearing (TAMB) system for the tracking of various reference trajectories in this study. First, the operating principles and dynamic model of the TAMB system using a linearized electromagnetic force model is derived. Then, a conventional sliding-mode control (SMC) system is designed for the tracking of various reference trajectories. Moreover, a complementary sliding-mode control (CSMC) system is adopted to reduce the guaranteed ultimate bound of the tracking error by half while using the saturation function as compared with the SMC. Furthermore, since the system parameters and the external disturbance are highly nonlinear and time-varying, the ACSMC is proposed to further improve the control performance and increase the robustness of the TAMB system. In the ACSMC, the MIMO RHNN estimator with estimation laws is proposed to estimate two complicated dynamic functions of the system on-line. In addition, a robust compensator is proposed to confront the minimum approximated errors and achieve the robustness. Finally, some experimental results for the tracking of various reference trajectories show that the control performance of the ACSMC is significantly improved comparing with the SMC and CSMC.     

Output tracking of constrained nonlinear processes with offset-free input-to-state stable fuzzy predictive control

This paper develops an efficient offset-free output feedback predictive control approach to nonlinear processes based on their approximate fuzzy models as well as an integrating disturbance model. The estimated disturbance signals account for all the plant-model mismatch and unmodeled plant disturbances. An augmented piecewise observer, constructed by solving some linear matrix inequalities, is used to estimate the system states and the lumped disturbances. Based on the reference from an online constrained target generator, the fuzzy model predictive control law can be easily obtained by solving a convex semi-definite programming optimization problem subject to several linear matrix inequalities. The resulting closed-loop system is guaranteed to be input-to-state stable even in the presence of observer estimation error. The zero offset output tracking property of the proposed control approach is proved, and subsequently demonstrated by the simulation results on a strongly nonlinear benchmark plant.     

Finite time state and disturbance estimation for robust performance of motion control systems using sliding modes

In this paper, simultaneous state and disturbance estimation of a drive system composed of motor connected to a load is proposed. Such a system is represented by a two mass model realising in a fourth-order plant. Backlash is introduced as the nonlinear disturbance in gears which is proposed to be estimated and in turn compensated. For this motion control system, a two-stage higher order sliding-mode observer is proposed for state and backlash estimation. The novelty lies in the fact that for this fourth-order system, output is considered from the motor end only, i.e. its angular displacement. The unmeasured states consisting of output derivative, load-side angular displacement and its derivative along with backlash are estimated in finite time. This disturbance due to backlash is unmatched in nature. The estimated states and disturbance are used to devise a robust sliding-mode control. This proposed scheme is validated in simulation and experimentation.     

基于惯量辨识的测试转台自适应内模控制

为了降低系统模型参数变化对测试转台控制系统的影响,提出了基于惯量辨识技术的自适应内模控制方案.首先建立了某型号测试转台数学模型,设计了测试转台内模控制器,在此基础上采用基于扰动转矩观测器的惯量辨识算法来辨识测试转台转动惯量的变化情况,然后通过模糊控制器根据转动惯量的变化自动调整内模控制器的参数,从而确保了控制器的控制性能.仿真和实验结果表明,该控制方案对惯量的变化有着很强的自适应性,提高了测试转台控制系统的抗干扰性能和系统的鲁棒性,取得了较好的控制效果.     

Observer-based variable universe adaptive fuzzy controller without additional dynamic order

A high-precision fuzzy controller, based on a state observer, is developed for a class of nonlinear single-input-single-output (SISO) systems with system uncertainties and external disturbances. The state observer is introduced to resolve the problem of the unavailability of state variables. Assisted by the observer, a variable universe fuzzy system is designed to approximate the ideal control law. Being auxiliary components, a robust control term and a state feedback control term are designed to suppress the influence of the lumped uncertainties and remove the observation error, respectively. Different from the existing results, no additional dynamic order is required for the control design. All the adaptive laws and the control law are built based on the Lyapunov synthesis approach, and the signals involved in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results performed on Duffing forced oscillation demonstrate the advantages of the proposed control scheme.     

Straight‐Line Tracking Control of an Agricultural Vehicle with Finite‐Time Control Technique

For the agricultural vehicle straight‐line tracking system, three control algorithms based upon the finite‐time control technique have been proposed to force the vehicle to track a straight line. Without considering the lumped disturbance, a backstepping‐like finite‐time state‐feedback controller is first developed. On this basis, an adaptive state‐feedback controller in conjunction with integral sliding mode is further developed in the presence of the lumped disturbance. Finally, a sliding mode disturbance observer is given to estimate the lumped disturbance, and the composite control scheme is presented. Under the composite controller, the lumped disturbance can be compensated and thus the disturbance rejection property has been significantly improved. Simulation results verify the proposed control algorithms.     

Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems

This paper proposes a novel dynamic structure neural fuzzy network (DSNFN) to address the adaptive tracking problems of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control scheme uses a four-layer neural fuzzy network (NFN) to estimate system uncertainties online. The main feature of this DSNFN is that it can either increase or decrease the number of fuzzy rules over time based on tracking errors. Projection-type adaptation laws for the network parameters are derived from the Lyapunov synthesis approach to ensure network convergence and stable control. A hybrid control scheme that combines the sliding-mode control and the adaptive bound estimation control with different weights improves system performance by suppressing the influence of external disturbances and approximation errors. As the employment of the DSNFN, high-quality tracking performance could be achieved in the system. Furthermore, the trained network avoids the problems of overfitting and underfitting. Simulations performed on a two-link robot manipulator demonstrate the effectiveness of the proposed control scheme.