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。     

非仿射纯反馈不确定系统预设性能鲁棒自适应控制

针对一类结构和参数及控制方向均未知的非仿射纯反馈非线性不确定系统,提出了一种保预设性能鲁棒自适应控制方案。首先引入性能函数和误差转换函数,通过误差转换将原始的输出误差存在性能约束的受限系统转换为等价的非受限系统;其次,基于中值定理将非仿射型系统转化为具有线性结构形式的时变系统,并同时利用自适应投影算法对有界时变参数进行辨识,参数辨识误差和外界干扰采用非线性阻技术项进行补偿;随后综合运用反演技术和Nussbaum函数设计控制器并进行稳定性分析。所设计的控制器不仅能够保证闭环系统所有信号有界且输出误差满足预设的瞬态及稳态性能要求;最后,仿真结果验证了所设计控制方案的可行性与有效性。     

Robust Sliding Mode Speed Controller-based Model Reference Adaptive System (MRAS) and Load Torque Estimator for Interior Permanent Magnet Synchronous Motor (IPMSM) Drives

This paper presents a robust sliding mode control (SMC)-based model reference adaptive system (MRAS) aimed at improving the dynamic performance of interior permanent magnet synchronous motor (IPMSM) drives. MRAS following a speed controller for IPMSM drives is developed. The error signal between the plant speed and MRAS speed is augmented to permit the prescribed specifications be maintained using SMC. The load disturbance is detected using a load torque estimator and is compensated through the q-axis current reference value. The load torque estimator is used to provide a feedforward value in the speed controller in order to decouple the load torque from the speed control. This method can improve IPMSM dynamic performance against the disturbance torque without increasing SMC gain due to both chattering and stability limitations. The complete field-oriented control of an IPMSM drive with the proposed controller is successfully implemented in real time using the DSP-DS1102 control board for a laboratory 1-hp motor. A performance comparison of the proposed controller with the conventional PI controller is also provided. The efficacy of the proposed controller is verified by simulation and experimentation under different operating conditions. It is found that the proposed controller provides an excellent speed response under load torque disturbance and parameter uncertainty.     

Neural network‐based adaptive attitude tracking control for flexible spacecraft with unknown high‐frequency gain

Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state‐dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high‐frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L₂ ‐gain performance. The resulting closed‐loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.     

自适应通用模型控制在液压弯辊系统中的应用

针对非线性的电液伺服系统,提出一种利用基于输入等价干扰的通用模型控制算法(ICMC)来设计控制器新策略.该控制器用估计得到的输入等价干扰及时进行自适应前馈补偿.控制器参数整定方便,物理意义明确.ICMC控制器能有效地跟踪液压伺服系统的时变参数和不可测扰动,通过仿真实验表明,该控制方法对非线性的液压弯辊力系统实现自适应控制是正确有效的.     

Prescribed performance adaptive fuzzy dynamic surface control of nonaffine time‐varying delayed systems with unknown control directions and dead‐zone input

In this paper, an adaptive prescribed performance control method is presented for a class of uncertain strict feedback nonaffine nonlinear systems with the coupling effect of time‐varying delays, dead‐zone input, and unknown control directions. Owing to the universal approximation property, fuzzy logic systems are used to approximate the uncertain terms in the system. Since there is no systematic approach to determine the required upper bounds of errors in control systems, the prior selection of control parameters to have a satisfactory performance is somehow impossible. Therefore, the prescribed performance technique as a solution is applied in this study to bring satisfactory performance indices to the system such as overshoot and steady state performance within a predetermined bound. Dynamic surface control strategy is also introduced to the proposed control scheme to address the “explosion of complexity” behavior existing in conventional backstepping methods. To ease the control design, the mean‐value theorem is utilized to transform the nonaffine system into the affine one. Moreover, with the help of this theorem, the unknown dead‐zone nonlinearity is separated into the linear and nonlinear disturbance‐like bounded term. The proposed method relaxes a prior knowledge of control direction by employing Nussbaum‐type functions, and the effect of time‐varying delays are compensated by constructing the proper Lyapunov‐Krasovskii functions. The proposed controller guarantees that all the closed‐loop signals are semiglobally uniformly ultimately bounded and the error evolves within the decaying prescribed bounds. In the end, in order to demonstrate the superiority of this method, simulation examples are given.     

PSS与HVDC调制器的在线协调控制策略

提出了模糊神经网络电力系统稳定器与高压直流调制器的自适应在线协调控制策略,改善了系统在大扰动后的动态品质。该协调控制策略采用辨识器预测各个发电机在下一个采样点的角速度变化,并以其为变量构造目标函数对模糊控制器进行在线训练,使功角摇摆过程中各发电机间角速度差值的平方和最小。算法用于三区域双馈入直流系统的在线协调控制。仿真结果表明,自适应在线协调策略使系统在不同的运行状态和不同的故障下均具有良好的暂态稳定性,抑制了故障后系统的振荡。     

基于频域分析的挠性卫星姿态高稳定度控制

在考虑外界干扰和挠性振动的影响下,针对卫星姿态的高稳定度控制需求提出了一种控制方案,并给出控制参数的整定方法。采用传统的PD控制结合干扰补偿器的控制方法完成姿态稳定任务。干扰补偿器的设计运用了鲁棒模型匹配的原理,并通过减小干扰输入到角速度输出的幅频响应以提高姿态控制的稳定度。分别给出了挠性影响化作广义干扰和未化作广义干扰时的传递函数模型,然后用频域分析的方法分析了引入干扰补偿器后系统的开环特性以及干扰抑制的性能,并证明了系统的鲁棒稳定性。仿真表明,所采用的姿态控制方法有效抑制了挠性振动,并提高了姿态控制的精确度与稳定度。方法设计简单,容易实现,适于工程应用。     

不确定性电力系统鲁棒自适应励磁控制

为降低电力系统不确定参数和外部干扰在建模和控制设计中对系统品质的影响,利用Backstepping方法,递归构造闭环系统耗散不等式的能量存储函数,进而得到具有干扰抑制的反馈控制.推证了存储函数又是Lyapunov函数,保证闭环系统渐进稳定.设计过程用估计值代替不确定参数,故系统对不确定参数具有适应能力.仿真结果表明,该...     

基于复合控制的圆筒形永磁直线同步电机位置控制

针对圆筒形永磁直线同步电机,设计了一种位置控制系统。考虑到直线电机的推力脉动、摩擦力和系统模型的不确定性,采用干扰观测器(DOB)对其进行在线估算并进行补偿,经过补偿,系统模型近似等价其标称模型;基于标称模型设计速度前馈控制器,基于速度控制系统的模型设计位置前馈控制器,使得永磁直线同步电机控制系统的输出能够无误差地跟踪期望的速度和位置响应曲线;采用综合校正方法设计反馈控制器,保证系统的稳定性。最后给出了仿真结果。     

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。     

Adaptive multi-dimensional Taylor network funnel control of a class of nonlinear systems with asymmetric input saturation

In view of the result and performance of control are affected by the existence of input constraints and requirements, adaptive multi-dimensional Taylor network (MTN) funnel control problem is studied for a class of nonlinear systems with asymmetric input saturation in this paper. Firstly, the effect of asymmetric input saturation can overcome by introducing the Gaussian error function, namely, the asymmetric saturation model is represented as a simple linear model with a bounded disturbance. Secondly, MTNs are employed to approximate the unknown functions in the controller design. Then, an adaptive MTN tracking controller is developed by blends the idea of funnel control into backstepping, which can guarantee that the tracking error always meets the given prescribed performance regarding the transient and steady state responses as well as the output of system tracks the give continuous reference signal. Finally, the effectiveness of the proposed control is demonstrated using two examples.     

Compound conditions event-triggered tracking control for nonlinear multiagent systems with nonlinear output faults

This article is devoted to investigating the compound conditions event-triggered prescribed performance tracking problem for strict-feedback nonlinear multiagent systems with nonlinear output faults and unknown disturbances. A simplified event-triggered sampling condition is designed to successfully reduce the number of state triggered design parameters. Besides, a compound conditions event-triggered mechanism is constructed, which integrates state triggered and controller triggered simultaneously to reduce communication burden. Furthermore, by using disturbance observers and prescribed performance functions, unknown external disturbances are compensated and the tracking errors can converge into the prescribed boundary, respectively. Moreover, all the signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the availability of the proposed control strategy is testified via simulation results.     

三相Boost型PWM整流器输出误差无源性控制

提出将无源性控制与频域理论相结合来控制三相Boost型PWM整流器,得到无源性电流控制器、输出误差补偿控制器和闭环系统的频域模型.仿真结果表明,在电路参数变化和负载扰动时,无源性电流控制器和输出误差补偿控制器可以实现输入单位功率因数和输出直流电压恒定,采用频域方法设计参数的输出误差补偿控制器提高了系统的动态性能.     

基于RBF神经网络的伺服系统自适应自抗扰控制

针对伺服系统中存在的非线性,提出了一种基于RBF神经网络的自适应自抗扰控制（ADRC）方法,设计了基于RBF神经网络的自适应自抗扰ADRC控制器。通过仿真和实验验证了该方法能有效地克服采用PD控制时系统的超速超回现象和爬行现象,在参数变化时具有较好的稳态性能和较强的鲁棒性。     

Output feedback tracking control for rigid body attitude via immersion and invariance angular velocity observers

A novel immersion and invariance (I&I) angular velocity observer is presented for the attitude tracking control of a rigid body with the lack of angular rate. Global exponential convergence of angular velocity estimate errors are guaranteed by an innovative filter design for the estimates' Euclidean norm. The proposed method requires fewer filter states compared with existing I&I angular velocity observer designs, which achieves a simpler closed-loop structure (dynamic reduction). The observer synthesis and convergence are independent of the control torque, which leads to much convenience in establishing “separation property” when combining a proportional-derivative attitude tracking controller driven by angular velocity estimates. A rigorous stability analysis is provided to ensure the (almost) global asymptotic convergence of the overall closed-loop tracking errors, and several numerical simulations are carried out to demonstrate the effectiveness of the combined implementation of proposed angular velocity observer and full-state feedback attitude tracking controller.     

Precision motion control of a small launching platform with disturbance compensation using neural networks

A kind of launching platform driven by two permanent magnet synchronous motors which is used to launch kinetic load to hit the target always faces strong parameter uncertainties and strong external disturbance such as the air current impulsion which would degrade their tracking accuracy greatly. In this paper, a practical method which combines adaptive robust control with neural network‐based disturbance observer is proposed for high‐accuracy motion control of the launching platform. The proposed controller not only accounts for the parametric uncertainties but also takes the external disturbances into account. Adaptive control is designed to compensate the former, while neural network‐based disturbance observer is designed to compensate the latter respectively and both of them are integrated together via a feedforward cancellation technique. A new kind of parametric adaptation and weight adaptation strategy is designed by using the linear combination of the system's tracking error and the weight estimation error as a driving signal for parametric adaptation and disturbance approximation. The stability of the novel control scheme is analyzed via a Lyapunov method and this method presents a prescribed output tracking performance in the presence of both parameter uncertainties and unmodeled nonlinearities. Extensive comparative simulation and experimental results are obtained to verify the high‐performance of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.     

Prescribed performance adaptive fuzzy control for nonstrict-feedback nonlinear systems with dead zone outputs

This paper presents an adaptive fuzzy control scheme for a class of nonstrict-feedback nonlinear systems with dead zone outputs and prescribed performance. By utilizing the monotonically increasing property of system bounding functions and the Nussbaum function, the design difficulties caused by the nonstrict-feedback structure and dead zone output are overcome. Combining backstepping technique with prescribed performance algorithm, a feasible adaptive fuzzy controller is designed to guarantee the boundedness of all signals of the closed-loop system and the prescribed tracking performance of the system. Finally, simulation results are depicted to illustrate the effectiveness of the proposed control approach.     

基于观测器的异步电机随机系统模糊反步位置跟踪控制

提出了一种基于观测器的异步电机随机系统模糊反步位置跟踪控制方法:通过构造降维观测器估计转子角速度;采用模糊逻辑系统逼近系统模型中的未知随机非线性函数。利用动态面控制技术解决传统反步设计中存在的"计算爆炸"问题。仿真结果表明:所提出的控制方法可以克服随机扰动的影响,并且确保跟踪误差收敛到足够小的原点邻域内。     

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.