基于非线性干扰观测器不确定系统的终端滑模控制

针对一类存在非匹配干扰和建模误差的高阶非线性系统,结合滤波反步控制方法,设计一种基于非线性干扰观测器的自适应反步非奇异终端滑模控制方案。首先,设计一种有限时间稳定的非线性干扰观测器,以此对非匹配干扰进行估计和补偿。采用反步控制处理高阶不确定非线性系统,结合动态面控制设计虚拟控制律,避免传统反步设计中存在的"微分爆炸"问题;第n步结合自适应控制和非线性干扰观测器,设计非奇异终端滑模控制律,消除建模误差和非匹配干扰对系统的影响。基于Lyapunov理论证明跟踪误差一致最终有界。仿真结果验证了所设计控制方案的有效性。     

采用滑模观测器的机载激光通信视轴精度控制

为了提高机载激光通信系统在机体振动和机械摩擦等扰动下的视轴对准精度,提出了一种基于滑模观测器的反步滑模控制方法。首先建立了机载激光通信系统的数学模型,然后通过设计的滑模观测器对扰动值进行估计,同时针对指令转换模块、激光通信模块和电机模块逐步设计了反步滑模控制律,实现对机载激光通信系统视轴的高精度控制。实验结果表明:提出的方法与分数阶PID控制方法相比突出了更优的快速性和准确性,响应时间仅为0.4 s,最大空间对准误差仅为0.3 m,设计的滑模观测器能够快速、准确地估计出扰动值,响应时间仅为0.3 s,最大估计误差分别仅为0.1 m/s、0.06 (°)/s2 和0.07 A/s,大幅提高了机载激光通信系统中视轴的对准精度。     

四旋翼无人机基于图像的抗干扰视觉伺服控制

针对四旋翼无人机视觉伺服过程中易受到干扰的问题,利用积分反步滑模控制方法设计基于图像的四旋翼无人机视觉伺服抗干扰的非线性控制器。选择图像矩作为特征,利用虚拟相机平面改进存在模型不确定和外部干扰的动力学模型。针对难以直接测量的虚拟平面的线速度,通过反步法设计线速度估计器,提高了控制的准确性。通过Lyapunov理论证明了所提出的全局积分反步滑模控制器的稳定性。仿真实验结果证明了设计的控制器的有效性和鲁棒性。     

不确定分数阶多驱动一响应混沌系统同步

针对一类新的多驱动一响应混沌系统同步方式,基于分数阶系统稳定性理论和Lyapunov稳定性理论,运用追踪控制和滑模自适应控制方法设计了同步控制律和参数自适应律.对象模型考虑了不确定因素的影响,首先选取一类稳定的分数阶滑模曲面,然后提出了一种鲁棒同步方案.最后数值仿真验证了方案的正确性和有效性.     

TCP网络的自适应非奇异终端滑模控制

针对TCP网络的拥塞控制问题,采用非奇异终端滑模控制理论提出了一种新的主动队列管理算法。采用非奇异终端滑模面以克服传统终端滑模控制的奇异问题,同时确保系统能在有限时间内收敛至平衡点。考虑到UDP流干扰的情况,用Lyapunov稳定性方法给出了一个自适应律来消除UDP流干扰对系统的影响。仿真结果表明,该算法可以使队列长度快速收敛到设定值,同时维持较小的队列振荡,优于传统的滑模控制。     

基于滑模状态观测器的不确定控制系统鲁棒性诊断

针对不确定控制系统的故障诊断问题,通过对滑模控制系统的鲁棒性分析,提出了利用滑模观测器进行故障诊断的方法。该方法能够稳定地估计不确定控制系统正常扰动下的状态响应,进而对于系统出现的故障扰动实现检测诊断,仿真结果验证了该方法的有效性。     

多延迟多瓶颈网络拥塞鲁棒控制研究

研究了具有多延迟用户源端和多瓶颈链路端的复杂网络的主动队列管理控制器的设计和稳定性分析.建立了具有非线性、输入带有时滞和不确定参数扰动的通用网络TCP/AQM动态模型.在平衡点线性化后,采用还原算法进行延迟补偿和预估,将该模型转化为无时滞的线性模型.在系统状态矩阵和输入矩阵参数扰动不满足匹配但有界的条件下,采用积分滑模变结构控制算法,基于Lyapunov稳定性理论和线性矩阵不等式方法给出了滑模可到达和渐进稳定的可行条件,根据该条件设计鲁棒主动队列管理算法.理论证明了该算法的稳定性.     

非匹配不确定系统的T-S模糊反演滑模控制

对于一类T-S模糊模型描述的非匹配不确定系统,滑模控制鲁棒性难以保证问题,研究并设计了基于T-S模糊模型的模糊反演控制器。首先中间稳定项通过选择合适的Lyapunov函数来确定,在最后一步中确定滑模控制及参数。逐步设计调节器和跟踪控制器,进而实现系统的全局调节和渐进稳定。从仿真结果可以看出,所设计的模糊反演滑模控制器具有良好的跟踪性能和动态品质,在加入干扰项时,系统仍具有良好的性能,表明该设计控制律的有效性和较强的鲁棒性。     

基于TCP、UDP的多路多数据流融合网络系统设计

为节省网络资源,充分利用TCP与UDP两种网络传输协议各自的优势,设计了TCP、UDP的多路多数据流融合网络系统。该系统在数据采集端的多端复杂网路中采用TCP网络协议传输数据,保证了网络传输的可靠性,同时在数据转发端的点对点网络中采用UDP网络协议传输数据,节省了网络资源,解决了网络拥塞问题。该系统在多路TCP数据流融合过程中应用了队列(Queue)融合算法,解决了数据流不同步导致的数据重复转发及丢失问题。经过长时间运行测试,整个系统在网络传输速率、稳定性及可靠性方面都达到了设计要求。     

四旋翼飞行器自适应收缩反步控制

针对四旋翼飞行器轨迹跟踪控制问题,考虑模型参数不确定的情况,提出了一种基于收缩理论与反步法的四旋翼飞行器自适应控制算法。首先,介绍了基于微分几何的收缩理论并给出了四旋翼飞行器的动力学模型;然后,提出了一种自适应收缩反步控制方法应用于飞行器跟踪期望轨迹;最后,分析了系统的增量稳定性,证明了系统是误差状态收敛的。积分反步(IB)与自适应收缩反步(ACB)的对比实验表明,应用此控制算法的飞行器系统鲁棒性更强,能够精确地完成轨迹跟踪任务。     

基于非线性控制方法的AQM算法

首先利用非线性控制方法中的积分反步法设计了一种AQM算法,接着使用非线性控制方法中的模糊滑模变结构控制方法进行AQM算法的设计.仿真结果表明,与积分反步法相比,基于模糊滑模变结构控制的AQM算法性能上优于基于积分反步法的AQM算法.与基于线性控制方法的PID控制方法相比,基于积分反步法的AQM算法性能上优于基于PID控制的AQM算法.     

Adaptive Fuzzy Sliding Mode Control Algorithm for a Non-Affine Nonlinear System

This paper concerns the design of robust controller for a nonlinear system that can be represented or approximated in a non-affine form. The control algorithm is based on sliding mode control that incorporates a fuzzy tuning technique, and it superposes equivalent control, switching control, and fuzzy control. An equivalent control law is firstly designed based on a nominal system model that was obtained by using curve fitting techniques under MATLAB. Switching control is then added to guarantee that the state reaches the sliding surface in the presence of parameter and disturbance uncertainties. Also, fuzzy tuning schemes, which can be supported by learning techniques derived from neural networks, are employed to improve control performance and to reduce chattering in the sliding mode. To verify the performance of this controller, an experimental platform of a pneumatically actuated top-guided single-seated control valve, which belongs to a classical complex nonlinear system, was constructed. Also, the experimental results show that high performance and attenuated chatter are achieved and thus verify the validity of the proposed control approach to dynamic systems characterized by severe uncertainties.     

Sliding mode control for an active magnetic bearing system subject to base motion

This paper describes the application of an active magnetic bearing (AMB) system to levitate the elevation axis of an electro-optical sight mounted on a moving vehicle. In this type of system, it is desirable to retain the elevation axis in an air–gap between magnetic bearing stators while the vehicle is moving. A sliding mode control is applied to increase robustness to model uncertainties and to reduce disturbance responses. To ensure the authority of sliding mode control, model parameter uncertainties of AMB systems are analysed and reachability to sliding surface has been verified. The sliding surfaces are designed such that the poles of the closed-loop system locate at desired locations. The proposed control is applied to a 2-DOF active magnetic bearing system subject to base motion. The feasibility of the proposed technique is verified with experimental results.     

Adaptive backstepping control using recurrent neural network forlinear induction motor drive

An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results     

Development and application of a novel radial basis function sliding mode controller

Generally, physical systems have certain non-linear and time-varying behaviours and various uncertainties. It is difficult to establish an appropriate model for controller design. Adaptive and sliding mode control schemes have been employed to solve some of these problems under certain model-based conditions and limitations. Here a novel adaptive radial basis functions sliding mode control is proposed by combining the advantages of the adaptive, neural network and sliding mode control strategies without precise system model information. It has on-line learning ability to deal with the system time-varying and non-linear uncertainties by adjusting the control parameters. The proposed scheme is implemented on a three degree-of-freedom dynamic absorber system. Only five radial basis functions are required for this control system and their weightings can be established and updated continuously by on-line learning. The experimental results show that this intelligent control approach effectively suppresses the vibration amplitude of the main mass due to external disturbances.     

Sliding mode controller by use of neural networks

This paper proposes a method to design a robust controller by use of a neural network. The trained neural network functions as a sliding mode controller which is robust against uncertainties. From the analysis of the neural network, it is proved that the switching surface is not the same as the sliding surface like conventional sliding mode control theory. The neural network shows that the switching surface should be a nonlinear surface because of a hard limitation on control inputs, even if the designed sliding surface is linear. From the result of estimating the robustness of neural networks, we propose that generalization of neural networks which are used as controllers should be measured by the robustness. Numerical simulations show that the controller is robust against uncertainties and robustness can be improved by the proposed method.     

Model identification and adaptive control of lower limb exoskeleton based on neighborhood field optimization

In lower limb exoskeletons, control performance and system stability of human–robot coordinated movement are often hampered by some model parametric uncertainties. To address this problem, Neighborhood Field Optimization (NFO) is proposed to identify the unknown model parameters of an exoskeleton for the model-based controller design. The excitation trajectory is designed by the NFO algorithm with motion constraints to improve the model identification accuracy. Meanwhile, the Huber fitness function is adopted to suppress the influence of the disturbance points in sampled dataset. Then an adaptive backstepping control scheme is constructed to improve the dynamic tracking performance of human–robot training mode in the presence of the identification error. Via Lyapunov technique and backstepping iteration, all the system state errors of the exoskeleton are bound and converge to zero neighborhood based on the assumption of bounded identified parameter error. Finally, the model identification results and comparative tracking performance of the proposed scheme are verified by an experimental platform of Two-degrees of freedom (DOF) lower limb exoskeleton with human–robot cooperative motion.     

BTT导弹自适应反演控制律设计与仿真

针对倾斜转弯(BTT)导弹控制中的多变量强耦合问题,研究了一种适用于BTT导弹的反演算法,以实现自动驾驶仪的自适应解耦控制。根据BTT导弹控制的基本特性,建立导弹的非线性控制模型,并将其转化为适合于反演设计的反馈块模型。在此模型上,基于反演的非线性控制系统综合设计方法,加入自适应神经网络逼近系统中存在的不确定性,利用Lyapunov稳定性定理推导了自适应调节律,设计了导弹控制律。通过仿真验证了该设计方法的有效性和可行性,该控制器能够实现控制解耦目的,且对指令信号跟踪效果良好。     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.