基于观测器的四旋翼飞行器自适应滑模姿态控制

针对存在建模不确定性和外部干扰时四旋翼飞行器的姿态控制问题,提出一种基于观测器的自适应滑模控制算法。在建立四旋翼飞行器姿态误差动力学模型的基础上,通过全局渐近收敛观测器获取系统的未知状态反馈量,利用自适应滑模控制抑制系统的不确定性和干扰,构建一种基于观测器的自适应滑模姿态控制器。基于Lyapunov的稳定性分析表明,该方法的跟踪误差是一致最终有界的。数值仿真实验结果表明,与现有滑模控制方法相比,所提方法具有更好的姿态跟踪性能和较高的抗干扰鲁棒性,能有效保证飞行器的姿态跟踪控制性能。     

非线性奇异摄动系统的自适应模糊控制器设计

为解决模型未知情况下多输入多输出非线性奇异摄动系统的跟踪问题,提出一种新型的自适应模糊控制器。首先将被控系统分解为快慢子系统,对慢子系统,设计后件参数可调的直接型自适应模糊控制器,能保证系统的慢状态跟踪预定轨迹;对快子系统,则设计模糊控制器,能够保证快子系统的稳定,最终控制器为二者的合成。Lyapunov方法证明,只要摄动参数足够小,就能保证整个系统的稳定。仿真验证了所提方法的有效性。     

考虑约束的卫星抗扰反步姿态控制

针对具有不确定性、外干扰及饱和约束的卫星非线性姿态跟踪问题, 将约束反步法与状态观测器相结合, 提出分块自适应抗扰反步控制器。卫星模型由修正罗德里格参数进行描述。利用带参数投影的非线性扩张状态观测器对时变的“总干扰”项进行在线估计补偿, 以提高反步控制器的鲁棒性。在设计反步控制器时, 引入指令滤波器和修正跟踪误差信号以施加系统状态和执行器的饱和限制, 同时较容易获得虚拟控制导数, 并且放宽了干扰估计律投影算子的投影集范围。Lyapunov理论证明了闭环系统在非线性阻尼的作用下输入-状态稳定。对比仿真表明, 与传统自适应反步法相比, 所提出的控制器具有更高的姿态跟踪性能和干扰估计精度。     

基于跟踪微分器的模型参考自适应控制

为了处理二阶系统模型参数的大范围不确定性,提出了基于跟踪微分器的模型参考自适应控制,利用两个非线性跟踪微分器分别得到系统输出的微分信号和误差的微分信号,同时抑制了高频噪声放大效应。根据被控对象的数学模型,自适应调节律能自动实时调节控制律中的参数。实验结果表明,当雷达伺服系统被控对象模型的参数在较大范围内变化时,该新型控制器有效补偿了二阶系统参数的不确定性,提高了伺服系统稳态和动态跟踪精度,保证了系统的全局渐近稳定。     

参数不确定高超声速飞行器自适应模糊H_∞控制

针对具有参数不确定性特点的高超声速飞行器输出跟踪问题,提出了一种自适应模糊H∞控制器设计方法。考虑系统存在的参数不确定性,利用自适应模糊系统在线逼近动态逆控制器中的非线性项,同时引入鲁棒补偿项,减轻模糊系统逼近误差和系统外部干扰对控制系统稳定性造成的影响,提高控制器的H∞性能。利用Lyapunov理论对整个系统的稳定性进行证明。对比仿真结果表明该方法能够保证高超声速飞行器具有良好的跟踪性能和很强的鲁棒性。     

重装空投过程的有限时间收敛自适应滑模控制

针对带有不确定性且不确定性边界未知的重装空投过程飞机运动系统,提出了一种有限时间收敛增益自适应滑模控制律。该控制律由标称控制和补偿控制两部分构成,前者用于获得有限时间的收敛性能,后者用于克服不确定性,提高系统鲁棒性。采用自适应方法在线近似补偿控制器的增益,解决了控制增益选取依赖于不确定性边界的问题,并有效抑制了滑模控制的抖振现象。理论分析表明,该方法能够保证飞机速度和俯仰角的跟踪误差有限时间收敛。仿真验证了控制方法的有效性和优越性。     

一类MIMO非线性系统的自适应模糊输出反馈控制

针对一类MIMO非线性状态不可测系统,提出了一种稳定的基于观测器的自适应模糊控制方法.该方法不需要系统状态可测的条件,而是通过设计模糊观测器来估计系统的状态.证明了所提出的控制方法可保证闭环系统的稳定性和跟踪误差的收敛性.仿真结果进一步验证了该控制方法的实用性和有效性.     

自适应模糊奇异摄动控制在航天器中的应用

带挠性附件航天器可以用含小摄动参数的多时标非线性系统进行描述,而目前的控制方法大多针对多时标线性系统,要求模型精确已知.为解决存在不确定性的带挠性附件航天器的跟踪问题,提出一种基于模糊奇异摄动模型的自适应控制器,采用鲁棒控制和自适应控制理论相结合的方法进行设计,由反馈项、自适应项和鲁棒控制项组成,反馈项的增益采用线性矩阵不等式(LMI)方法求解,自适应项用于减小系统的跟踪误差,鲁棒项用于保证系统的闭环稳定性.稳定性证明采用Lyapunov合成方法完成.该控制器适用于模型带有不确定性的多时标非线性系统的跟踪控制.由于采用了自适应方法消除系统不确定性,能够减少鲁棒控制方法带来的保守性.在带挠性附件航天器的跟踪控制中的应用验证了其有效性.     

基于超扭曲算法的鲁棒自适应四旋翼控制器

针对四旋翼在存在外部未知干扰及具有模型不确定性情况下的姿态控制问题,设计一种基于超扭曲算法的鲁棒自适应四旋翼控制器.该设计方法将超扭曲算法与鲁棒自适应控制结合,使用超扭曲算法抑制系统抖振现象,鲁棒自适应算法能有效补偿系统模型的不确定性,增强系统的抗干扰性能;构造Lyapunov函数,证明四旋翼飞行器闭环系统的稳定性.对所设计的控制器进行仿真,搭建四旋翼飞行器平台进行飞行实验,以验证设计的控制器.仿真结果表明,基于超扭曲算法控制的四旋翼系统具有较快的收敛速度和较强的鲁棒性,飞行实验验证了所提控制策略的可行性,可实现四旋翼的稳定控制.     

一种不依赖状态估计的自适应输出反馈控制方法

研究了一种不依赖状态估计的自适应输出反馈控制方法。首先引入伪控制信号使系统反馈线性化，然后设计一个线性动态补偿器和在线神经网络，自适应消除不确定性和建模所引起的误差。对一含有末建模动态的非线性系统的仿真结果表明，该控制方法能够消除系统的稳态误差，提高系统动态性能，并且具有较好的鲁棒性。     

Adaptive control techniques forfrictioncompensation

In this paper, we design two adaptive controllers for a second-ordermechanicalsystem which incorporates frictional effects such as Coulomb, static, Stribeck, andviscousfriction. First, we design a modular position tracking controller that can accommodate avarietyof adaptive update laws. The proposed controller is shown to compensate foruncertaintyassociated with the friction parameters which appear linearly in the model. In thesecond controlscheme, we show how a Lyapunov-based adaptive position setpoint controller canbe designed tocompensate for parametric uncertainty throughout the mechanical systemincluding the Stribeckeffect related constant which does not appear linearly in the model.Experimental results areprovided to illustrate the performance of the proposed controllers.     

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.     

A new technique for robust control of servo systems

The robust controller has very simple structures and can be divided into two separate parts: a servo controller and an auxiliary controller. The two controllers are designed independently. The function of the auxiliary controller is to cancel out the plant uncertainties directly without the use of the high loop gain principle. Interpretation of robot controller as a signal-synthesis adaptive controller is given. Practical implementation issues of the auxiliary controller are discussed. Simulations of a design example with large parameter uncertainty, nonlinearity, and external disturbance are presented to demonstrate the effectiveness of the design technique. This technique is further tested with success in an experimental study of joint position control of a PUMA 560 robot arm     

Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive

A robust wavelet neural network control (RWNNC) system is proposed to control the rotor position of an induction servo motor drive in this paper. In the proposed RWNNC system, a wavelet neural network controller is the main tracking controller that is used to mimic a computed torque control law, and a robust controller is designed to recover the residual approximation for ensuring the stable control performance. Moreover, to relax the requirement for a known bound on lumped uncertainty, which comprises a minimum approximation error, optimal network parameters and higher order terms in a Taylor series expansion of the wavelet functions, an RWNNC system with adaptive bound estimation was investigated for the control of an induction servo motor drive. In this control system, a simple adaptive algorithm was utilized to estimate the bound on lumped uncertainty. In addition, numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to parameter variations and external load disturbance.     

Vision-based nonlinear tracking controllers with uncertainrobot-camera parameters

This paper considers the problem of position tracking control of planar robot manipulators via visual servoing in the presence of parametric uncertainty associated with the robot mechanical dynamics and/or the camera system. Specifically, by assuming exact knowledge of the mechanical parameters, we design an adaptive camera calibration controller that compensates for uncertain camera parameters and ensures global asymptotic position tracking. We then develop an adaptive robot controller that accounts for parametric uncertainty throughout the entire robot-camera system while producing global asymptotic position tracking. Experimental results illustrating the viability of the adaptive controllers and extensions regarding robust control and redundant robot manipulators are also included     

Vision-based optimization of the generalized predictive active disturbance rejection controller

The batching system of the integrated mixing and spreading equipment for MOH material is a nonlinear system with large uncertainty. It is difficult for conventional control strategies to meet the requirements for system performance. This research combines generalized predictive control and active disturbance rejection technique to propose a new generalized predictive active disturbance rejection controller (GPADRC) used in the batching system of MOH material. For the nonlinearity and uncertainty of the batching system, the extended state observer in the active disturbance rejection technique is used for estimation and compensation. The batching system model is converted into an integrator form, based on which the use of generalized predictive control can greatly reduce the impact of nonlinear models and uncertainties on the controller. Aiming at the problem that the parameters of the proposed new controller are numerous and difficult to tune, the adaptive genetic algorithm is used to realize the automatic tuning of the parameters. The simulation experiment shows that the designed GPADRC can well adapt to the working conditions of the batching system and can meet the requirements for various control indicators. At the same time, the adaptive genetic algorithm can realize the rapid tuning of the controller parameters, which reduce the difficulty and time consumption of the tuning process, and improve the applicability and achievability of the designed controller.     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

An optimal fuzzy PID controller

This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytical formulae. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization     

A Genetic Fuzzy Controller for Vehicle Automatic Steering Control

This paper presents the design and experimental implementation of a genetic fuzzy controller for automatic steering of a small-scaled vehicle. We first derive a dynamic model of the vehicle via system identification and show that the model exhibits similar characteristics to full-sized vehicles. Subsequently, a stable fuzzy proportional-derivative controller is designed and optimized by genetic algorithms. The control system is transformed into a Lureacute system, and Lyapunov's direct method is used to guarantee the stability of the control system. Experimental studies suggest that the control system is insensitive to parametric uncertainty, load, and disturbances. The performance of the proposed controller is also compared against a conventional proportional derivative (PD) controller. Experimental results confirm that it outperforms the conventional PD controller, particularly in terms of robustness     

Design and Implementation of a Robust Current-Control Scheme for a PMSM Vector Drive With a Simple Adaptive Disturbance Observer

This paper introduces a robust current-control scheme for a permanent-magnet synchronous motor (PMSM) with a simple adaptive disturbance observer. The robust controller is realized by including an adaptive element in the reference-voltage-generation stage using the feedforward control. Due to the time-varying nature and the high-bandwidth property of the uncertainties in a practical PMSM drive system, the adaptive element is simply chosen as the estimated uncertainty function, which adaptively varies with different operating conditions. Subsequently, the frequency modes of the uncertainty function are embedded in the control effort, and a robust current-control performance is yielded. Furthermore, the inclusion of the estimated uncertainty function provides an efficient solution for torque-ripple minimization in PMSM drives. This is because the frequency modes of the disturbances to be eliminated, i.e., the flux harmonics, are included in the stable closed-loop system. To provide a high-bandwidth estimate of the uncertainty function, a simple adaptation law is derived using the nominal current dynamics and the steepest descent method. To guarantee the system's convergence and to properly tune the proposed observer, a stability analysis based on a discrete-time Lyapunov function has been used. Comparative evaluation experiments are presented to demonstrate the effectiveness of the proposed control scheme under different operating conditions.