无角速度测量的姿态跟踪动态PD控制

针对太阳帆板展开过程中存在系统切换和外部未知干扰的航天器姿态跟踪控制问题,提出了角速度可测和角速度不可测的姿态跟踪自适应动态比例微分(PD)控制器。本文先设计自适应更新律补偿外部复合干扰,提出了姿态角及角速度全状态可测的自适应PD控制器;接着针对角速度不可量测,基于无源控制理论给出估算角速度的动态观测器,提出一种仅有姿态角跟踪误差信息的变增益动态PD控制器,并理论证明了闭环系统的全局渐近稳定性;数值仿真验证了变增益动态PD控制器对太阳帆板展开过程中,航天器的姿态跟踪控制的鲁棒性和有效性。     

Model free adaptive control design for a tilt trirotor unmanned aerial vehicle with quaternion feedback: Theory and implementation

This article focuses on the attitude and altitude tracking control of the tilt trirotor unmanned aerial vehicle (UAV) which is subject to modeling uncertainties and unknown external disturbances. A novel model free adaptive controller is designed to achieve asymptotic tracking control of the UAV attitude and altitude channels. The control scheme is based on the data driven strategy, and relies on the input/output data to estimate the system dynamics online. Furthermore, the discrete sliding mode algorithm is combined to enhance the system robustness, and the quaternion feedback is employed to avoid the singularity associated with the attitude control design. The stability of closed-loop system and the convergence of the tracking errors are proved. And real time flight experiments are preformed on a tilt trirotor UAV control testbed. The experimental results verify the effectiveness of the proposed control scheme and achieve a strong robustness with respect to the modeling uncertainties and unknown external disturbances.     

Tracking control of vertical taking-off and landing vehicles with parametric uncertainty

This paper considers the position and attitude tracking control of a quadrotor with inertia parameter uncertainty. With the aid of the cascade structure of the dynamics of the system, an immersion and invariance observer is proposed to estimate the unknown mass of the system first. Secondly, a saturation controller is designed for the thrust force. Thirdly, a virtual controller is proposed for the angular velocity of the quadrotor with the aid of a nonlinear transform of the Euler angles. Finally, adaptive and robust controllers are proposed for the torque input of the quadrotor to deal with the unknown inertia moment of the system. Simulation results show the effectiveness of the proposed algorithms.     

Discrete time adaptive control for a MEMS gyroscope

This paper presents a discrete time version of the observer‐based adaptive control system for micro‐electro‐mechanical systems gyroscopes, which can be implemented using digital processors. A stochastic analysis of this control algorithm is developed and it shows that the estimates of the angular rate and the fabrication imperfections are biased due to the signal discretization errors in the feedforward control path introduced by the sampler and holder. Thus, a two‐rate discrete time control is proposed as a compromise between the measurement biases and the computational burden imposed on the controller. The convergence analysis of this algorithm is also conducted and an analysis method is developed for determining the trade‐off between the controller sampling frequency and the magnitude of the angular rate estimate biased errors. All convergence and stochastic properties of a continuous time adaptive control are preserved, and this analysis is verified with computer simulations. Copyright © 2005 John Wiley & Sons, Ltd.     

Sensorless vector controller for a synchronous reluctance motor

A new high-performance sensorless speed vector controller that implements the maximum torque per ampere control strategy for the inverter-driven synchronous reluctance machine is presented in this paper. It is based on a parameter-dependent technique for online estimation of rotor position and angular velocity at the control rate. The current ripple principle is used to estimate position. The estimates are fed to a conventional closed-loop observer to predict the new position and angular velocity. The very high accuracy of the sensorless control algorithm at both low and high speeds is confirmed by experimental results     

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are 1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control; 2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller–observer design; and 3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties, as well as severe and adverse driving conditions.      

Adaptive finite-time output feedback control for Markov jumping nonlinear systems

In this article, the problem of output feedback tracking control for uncertain Markov jumping nonlinear systems is studied. A finite-time control scheme based on command filtered backstepping and adaptive neural network (NN) technique is given. The finite-time command filter solves the problem of differential explosions for virtual control signals, the NN is utilized to approximate the uncertain nonlinear dynamics and the adaptive NN observer is applied to restructure the state of system. The finite-time error compensation mechanism is established to compensate the errors brought by filtering process. The proposed finite-time tracking control algorithm can ensure that the solution of the closed-loop system is practically finite-time stable in mean square. Two simulation examples are employed to demonstrate the effectiveness of the proposed control algorithm.     

基于变截止频率的梯形离散磁链观测控制系统

为了解决直接转矩控制系统中永磁同步电机抗干扰能力差的问题,速度环采用自抗扰控制器取代传统PI控制器,移除自抗扰控制器中的跟踪微分器以提高系统信号的跟踪速率,并且通过引入负载观测器对速度环进行前馈补偿,减少自抗扰控制器的负担;同时考虑到电流采样时零漂产生的干扰,引入二阶高通滤波器对传统电压模型进行滤波操作,采用梯形离散法对二阶高通滤波器进行离散处理,提高观测器的观测精度,并设置滤波器截止频率跟随电角速度变化,提高系统的动态性能。最后仿真结果表明系统输出转速超调小,输出磁链、转矩精度高,具有良好的抗干扰能力。     

Small-gain technique-based adaptive fuzzy command filtered control for uncertain nonlinear systems with unmodeled dynamics and disturbances

This article studies the adaptive tracking control problem for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances. First, a fuzzy state observer is established to estimate unmeasurable states. To overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, the command filter with a compensation mechanism is applied to the controller design procedure. Meanwhile, with the help of the fuzzy logic systems and the backstepping technique, an adaptive fuzzy control scheme is proposed, which guarantees that all signals in the closed-loop systems are bounded, and the tracking error can converge to a small region around the origin. Furthermore, the stability of the systems is proven to be input-to-state practically stable based on the small-gain theorem. Finally, a simulation example verifies the effectiveness of the proposed control approach.     

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non-affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non-affine nonlinearities with the help of Butterworth low-pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first-order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back-stepping approach. The boundedness and convergence of the closed-loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.     

风力发电系统最大功率跟踪自适应鲁棒控制

为了提高风力发电系统最大功率跟踪(MPPT)运行的工作性能,针对系统未知建模误差和外部扰动等不确定问题,提出了一种MPPT自适应鲁棒控制方法。该方法建立在基于广义扰动的风力发电系统角速度跟踪动态模型基础上,不依赖于系统模型参数和外部扰动辨识。利用MPPT跟踪偏差的非线性状态反馈和扰动边界值的在线实时估计,自适应地调整切换控制项增益,以加快系统收敛的速度。实际控制律经过一阶积分输出,进一步削弱控制输出信号幅值的抖振,平滑发电转矩,提高跟踪精度。通过构造Lyapunov函数,验证了闭环系统的全局稳定性。通过与常规线性PID控制和非线性动态状态反馈控制(SFC)进行仿真比较,验证了该控制器实现最大功率跟踪控制的良好效果,具有较强的鲁棒性和自适应性。     

多星发射上面级的姿态解耦控制

针对具有非线性和惯量积耦合的多星发射上面级姿态控制系统,提出了两种控制器综合方法.首先,基于奇异摄动理论,把姿态动力学分解成姿态角和姿态角速度跟踪内外两回路;将非线性动力学模型伪线性化,在每个跟踪回路用State-Dependent-Riccati Equation(SDRE)获得自适应非线性控制律,通过SDRE局部渐近稳定的特点保证系统闭环稳定.其次,基于分段仿射切换理论对原始非线性模型建模,设计了简单的分段仿射控制器,具有明确的航天物理意义,便于工程应用;最后,对两种控制方案进行了仿真验证和对比,结果两种方法都满足控制要求,其中分段仿射控制器便于工程应用.     

Adaptive robust output feedback control for attitude tracking of quadrotor unmanned aerial vehicles

This work proposes a new adaptive robust output feedback control method for attitude reference tracking of a quadrotor unmanned aerial vehicle without using the angular velocity measurements. By using the K-filters well known in the adaptive control community, the necessity of velocity measurements or estimating is avoided. The attitude system model is transformed into a second-order model where the angular velocity measurements are not involved. However, the model includes mismatched uncertainties which should be estimated and compensated by the disturbance observers (DOBs). The controller is designed in a backstepping manner, and the dynamic surface technique is adopted to avoid the explosion of the controller complexity. For each Euler angle axis, the prescribed performance control technique is adopted to ensure a prescribed performance, the lumped disturbance is compensated by a DOB, and furthermore an adaptive law is introduced to adaptively update the corresponding uncertain inertia parameter which affects the control performance significantly. The control performance of the overall control system is analyzed rigorously from the viewpoint of input-to-state practical stability. In addition, it is shown how the adaptive laws contribute to improving the control performance. And simulation examples are provided to demonstrate the performance of the proposed method.     

Saturated observer-based adaptive neural network leader-following control of N tractors with n-trailers with a guaranteed performance

This article addresses the leader-following neural network adaptive observer-based control of N tractors connected to n trailers with the prescribed performance specifications. To propose the controller, a change of coordinates and a nonlinear error transformation are used to transform the constrained error dynamics to a new second-order Euler-Lagrange unconstrained error dynamics which inherits all structural properties of ith vehicle dynamic model. By combining a projection-type neural network and an adaptive robust technique, a novel leader-following saturated output-feedback controller is proposed to force that ith vehicle tracks a virtual leader trajectory with the prescribed transient and steady-state characteristics while reducing the actuator saturation risk and compensating all unknown dynamic model parameters, external disturbances, unmolded dynamics, and NN approximation errors. A saturated velocity observer is heuristically proposed to obviate the requirement for the velocity measurements of ith vehicle without any unwanted peaking. A Lyapunov-based stability analysis is utilized to prove that all the tracking and state observation errors are semi-globally uniformly ultimately bounded (SGUUB) and they converge to small bounds including the origin with a prescribed performance. At the end, computer simulations will be shown to validate the efficacy of the proposed controller in practice.     

针对时变输入的永磁同步电机改进型自抗扰控制器

在永磁同步电机(PMSM)调速系统中,提出了对时变输入具有更高跟踪精度的改进型模型补偿自抗扰控制(MMC-ADRC)策略。传统的自抗扰控制中,扩张状态观测器(ESO)的观测扰动项较大,通过模型补偿可以大幅减小观测扰动项,提高跟踪精度。然而当输入时变时,由于常规的模型补偿自抗扰控制(MC-ADRC)存在建模误差,系统对输入的跟踪误差仍然较大。尽管可通过增大控制器增益来减小跟踪误差,但这同时会使系统中噪声被放大,降低系统性能。在MC-ADRC基础上引入输入微分前馈(IDF)得到的MMC-ADRC,可从理论上消除建模误差,在相同增益下获得更高的跟踪精度。仿真和实验结果证明了所提方法的有效性。     

Adaptive interconnection and damping assignment passivity-based control for linearly parameterized discrete-time port controlled Hamiltonian systems via I&I approach

In this paper, discrete-time adaptive control of linearly parameterized fully actuated Port-controlled Hamiltonian systems with parameter uncertainties in energy function is considered. A discrete-time adaptive interconnection and damping assignment passivity-based control (IDA-PBC) method, utilizing the immersion and invariance (I&I) approach, for the considered uncertain Hamiltonian system, is presented. A discrete-time parameter estimator based on the immersion and invariance approach is derived to obtain an automatic tuning mechanism for the IDA-PBC controller. The stability analysis for the estimator and the closed-loop system is done using the Lyapunov theory. The proposed method is applied to two fully actuated physical systems and its performance is tested by simulations. Simulation results show that the proposed I&I-based adaptive IDA-PBC controller successfully preserves the performance of the IDA-PBC controller designed with true parameters under a large amount of uncertainty.     

Immersion and invariance adaptive tracking control for robot manipulators with a novel modified scaling factor design

The robot manipulators' tracking control problem in the presence of inertia uncertainties is addressed in this paper, and a novel dynamic scaling–based immersion and invariance (I&I) adaptive tracking controller is utilized to stabilize the proposed system. By virtue of the reconstruction method of the parameter regression matrix, this paper provides a new perspective on how to overcome the integrability obstacle typically arising in the I&I controller design through dynamic scaling and presents a new controller design method. What is more, a novel modified scaling factor is proposed as well so that the controller can be implemented without the prior knowledge of the inertia matrix's lower bound, and only the saturation function involving the scaling factor is included in the feedback gains. Finally, the numerical simulations show the validity of the proposed controller.     

基于迭代学习的矿井提升系统跟踪控制

矿井提升机在煤矿开采和生产过程中发挥着至关重要的作用。针对矿料提升过程具有较强重复性的特点,提出迭代学习的提升机速度和位置跟踪控制方法。设计了提升机的D型迭代学习控制器,同时考虑运行过程中出现的非重复性干扰设计了带有滤波器型迭代学习控制器。并采用λ范数证明了系统的收敛性,理论结果表明矿井提升机位置与速度跟踪误差可以收敛到0。同时仿真结果表明,经过30次运行后,跟踪误差几乎收敛到0,迭代学习控制算法可利用矿井作业的重复运行特性可以有效提高提升机的跟踪性能,带有滤波器的迭代学习控制算法可较好地抑制了非重复扰动的影响。     

Composite adaptive finite-time fuzzy control for switched nonlinear systems with preassigned performance

This article investigates the composite adaptive fuzzy finite-time prescribed performance control issue of switched nonlinear systems subject to the unknown external disturbance and performance requirement. First, by utilizing the compensation and prediction errors, the piecewise switched composite parameter update law is employed to improve the approximation accuracy of the unknown nonlinearity. Then, the improved fractional-order filter and error compensation signal are introduced to cope with the influences caused by the explosive calculation and filter error, respectively. Meanwhile, the effect of the compound disturbances consisting of the unknown disturbances and approximation errors is reduced appropriately by designing the piecewise switched nonlinear disturbance observer. Moreover, stability analysis results prove that the proposed preassigned performance control scheme not only ensures that all states of the closed-loop system are practical finite-time bounded, but also that the tracking error converges to a preassigned area with a finite time. Ultimately, the simulation examples are given to demonstrate the effectiveness of the proposed control strategy.     

基于终端滑模控制的移动机器人轨迹跟踪

针对移动机器人受到外部不规则扰动时易出现速度和位姿误差跳变的问题,提出了一种基于扰动观测器的动态终端滑模控制方法。结合运动学模型,利用李雅普诺夫法设计虚拟控制器,进一步设计非奇异动态终端滑模轨迹跟踪控制器。为减小外部扰动对系统的影响,设计非线性扰动观测器对控制器进行扰动补偿。最后,将本文所提方法与自适应滑模控制方法进行仿真比较。结果显示,在第15 s扰动发生阶跃变化时,自适应滑模控制方法的线速度和角速度分别发生1.4 m/s和1.24 rad/s的跳变,而本文所提方法速度跳变幅度小于自适应滑模控制方法的1/10。仿真结果表明,本文所提方法可有效地抑制扰动对系统的影响,减小移动机器人速度和位姿误差的跳变幅度。