四旋翼无人机鲁棒自适应姿态控制

 四旋翼无人机的姿态控制是自主飞行控制的核心,针对四旋翼姿态易受外界环境干扰和内部参数摄动等不确定性影响的问题,设计了一种鲁棒自适应反步控制器,以提高四旋翼的鲁棒性。建立了四旋翼完整的姿态运动模型,并将其转化为含有广义不确定性的多输入多输出非线性系统。根据该系统满足严格反馈的结构特点,设计了反步控制器; 针对系统中存在的外部干扰和内部参数摄动等不确定性,引入了一类鲁棒自适应函数来抵消该不确定性对系统的影响; 采用非线性跟踪微分器估计虚拟控制量的微分信号,减小了反步控制器设计中普遍存在的“计算膨胀”问题; 通过构造Lyapunov 函数证明闭环系统是稳定且指数收敛的。仿真结果表明,所设计控制器具有良好的控制效果和鲁棒性。     

Multivariable extension of global finite‐time HOSM based differentiator for output‐feedback unit vector and smooth binary control

In this paper, we propose a unit vector control law by output feedback to solve the problem of global exact output tracking for a class of multivariable uncertain plants with nonlinear disturbances. In order to face the nonuniform arbitrary relative degree obstacle, we extend our earlier estimation scheme based on global finite‐time differentiators using dynamic gains to a multivariable architecture. A diagonally stable condition over the system high‐frequency gain (HFG) matrix has to be assumed. Preserving the simplicity of its mono variable framework, variable gain super‐twisting algorithm (STA) is employed to obtain the robust and exact multivariable differentiator. Moreover, state‐norm observers for the unmeasured state variables are constructed to upper bound the disturbances as well as to update the differentiator gains, being both state dependent. Uniform global exponential stability and ultimate exact tracking are proved. As an alternative to chattering alleviation, we appeal to the Emelyanov's concept of binary control in order to obtain a continuous control signal replacing the unit vector function in the controller by a high‐gain gradient adaptive law with parameter projection. As shown in the existing literature for mono variable systems, the proposed multiparameter binary‐adaptive formulation tends to the unit vector control as the adaptation gain increases to infinity, also smoothing the transition from adaptive to sliding mode control. A numerical example is portrayed to illustrate the potentialities of the developed multivariable techniques.     

基于非线性参数的电液伺服系统滑模控制

针对在电液伺服系统跟踪控制中存在非线性不确定参数和外界扰动的问题,提出了一种基于积分微分器的滑模Lyapunov函数的控制方法。首先,在只有位移信号测量输出的情况下,采用高阶积分链式微分器对其速度和加速度信息进行预估。系统存在非线性不确定参数,利用微分器对状态和不确定项的实时估计,设计出积分滑模控制器,实现自适应规律以及对电液伺服系统中不确定扰动的抑制。搭建电液伺服系统AMESim模型并与Matlab构成联合仿真平台,对控制器进行仿真。仿真表明,该控制器具有良好的对非线性不确定参数变化的补偿能力和跟踪性能。     

垂直面欠驱动自治水下机器人定深问题的自适应输出反馈控制

针对垂直面欠驱动自治水下机器人(AUV)定深控制问题,本文仅使用可测量的深度和纵摇角信息,基于反步法设计自适应输出反馈控制器.为此首先设计观测器,实现不可测纵摇角速度反馈;再利用径向基神经网络对不确定水动力系数和纵荡、垂荡及纵摇角速度耦合产生的非线性结构进行补偿;采用自适应策略对纵荡和垂荡速度形成的有界干扰进行抑制.本文采用AUV一阶非完整模型,不以线性化为目的,放宽了纵摇角只能在小范围内变化的限制.最后通过理论证明和仿真实验表明该方法能够实现AUV深度和姿态控制,对未建模非线性动态和有界扰动具有很强的自适应性和鲁棒性.     

Finite-Time Sideslip Differentiator-Based LOS Guidance for Robust Path Following of Snake Robots

This paper presents a finite-time sideslip differentiator-based line-of-sight(LOS) guidance method for robust path following of snake robots. Firstly, finite-time stable sideslip differentiator and adaptive LOS guidance method are proposed to counteract sideslip drift caused by cross-track velocity. The proposed differentiator can accurately observe the cross-track error and sideslip angle for snake robots to avoid errors caused by calculating sideslip angle approximately. In our method, the des...     

基于系统浸入和流形不变自适应方法的静止无功补偿器非线性鲁棒自适应控制方法

本文提出一种将系统浸入和流形不变(I&I)自适应控制方法与L2-增益抑制鲁棒控制方法相结合的静止无功补偿器(SVC)的非线性鲁棒自适应控制方法.所提方法首先通过参数估计误差和鲁棒控制律的设计,使得所构造的表示参数估计误差函数的流形不变且吸引,从而使参数估计误差在这一流形上收敛于零.然后,通过所设计的可调参数对参数估计误差的收敛性能进行控制,以此来保证参数估计器对不确定参数的自适应估计能力.最后,采用自适应逆推算法推导鲁棒控制律,并通过使不确定外部扰动满足从输入到输出的耗散性来保证系统对不确定扰动的鲁棒性.仿真结果表明,利用所提方法设计的SVC控制器和参数替换律在参数估计、发电机功角动态响应方面优于传统自适应逆推算法,从而提高了输电系统的稳定水平.     

Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: (1) an NN observer to estimate the system states and (2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem encountered during the control design is overcome by using a dynamic NN which is constructed through a feedforward NN with a novel weight tuning law. The separation principle is relaxed, persistency of excitation condition (PE) is not needed and certainty equivalence principle is not used. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is demonstrated. Though the proposed work is applicable for second order nonlinear discrete-time systems expressed in non-strict feedback form, the proposed controller design can be easily extendable to an nth order nonlinear discrete-time system.     

Improving velocity feedback for position control by using a discrete-time sliding mode filtering with adaptive windowing

In position control of mechatronic devices, velocity feedback is important for injecting additional damping to avoid low-frequency fluctuation around desired trajectories. In practice, velocity signal is often obtained by finite difference of position signal from an optical encoder. However, such a numerical differentiation produces high-frequency noise by magnifying quantization error contained in the position signal. As a result, the controller may produce high-frequency vibration. This paper presents a new noise-reduction discrete-time filter based on sliding mode and adaptive windowing. The presented filter is an improved version of a sliding mode filter by Jin et al. (2012), with including adaptive windowing of which the window size is determined in a similar way to that of a discrete-time adaptive windowing differentiator by Janabi-Sharifi et al. (2000). The presented filter is then applied to a position control of a mechatronic device for improving velocity feedback. Experimental results show that the presented filter provides better velocity feedback than its previous version, Janabi-Sharifi et al.’s differentiator, and combinations of these two filters.     

Adaptive backstepping output feedback control of DC motor actuator with friction and load uncertainty compensation

In this paper, we present an output feedback backstepping controller for mechatronic actuators with dynamic adaptive parameters for friction and load compensation. The targeted application is angular position control of automotive mechatronic valves, which possess nonlinear dynamics due to friction. The proposed controller requires only position measurement. The velocity, current, and friction dynamics are obtained by estimation and observation. The adaptive control law compensates the variations in friction behavior and load torque variation, which are common in real life applications. Lyapunov analysis has been used to show the asymptotic convergence of the closed‐loop system to zero. Simulation and laboratory experimental results illustrate the effectiveness and robustness of the controller. Further experiments on an engine test bench demonstrate the applicability of this controller in commercial engines, as well as its effectiveness as compared with conventional PI controllers.Copyright © 2014 John Wiley & Sons, Ltd.     

Robust adaptive tracking of uncertain nonlinear systems by output feedback

We investigate the problem of robust adaptive tracking by output feedback for a class of uncertain nonlinear systems. Based on the high‐gain scaling technique and a new adaptive law, a linear‐like output feedback controller is constructed. Only one dynamic gain is designed, which makes the controller easier to implement. Furthermore, by modifying the update law, the adaptive controller is robust to bounded external disturbance and is able to guarantee the convergence of the output tracking error to an arbitrarily small residual set. A numerical example is used to illustrate the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.     

含未建模动态的非线性系统输出反馈DSC设计

针对一类含有未建模动态和未知控制增益符号的非线性系统,提出了一种输出反馈自适应跟踪控制方案.首先利用Kreisselmeier观测器实现了不可测状态的估计,在此基础上以回归设计方式设计了输出反馈动态面控制系统,通过引入Nussbaum函数解决了控制方向未知问题.该方案解克服了传统反推控制方法中的微分爆炸现象,并且所有未...     

Adaptive backstepping impact angle control with autopilot dynamics and acceleration saturation consideration

This paper presents a robust impact angle constraint guidance law for maneuvering target interception in the presence of autopilot dynamics and input saturation. The presented guidance law is designed on the basis of a combination of adaptive backstepping control technique and higher‐order sliding mode differentiator. Different from existing impact angle constraint guidance law using sliding mode control, the line‐of‐sight angular rate and impact angle tracking error are regulated by two different virtual control laws. Because the future course of action of the target, an independent entity, cannot be predicted beforehand, adaptive laws are introduced in guidance law derivation for disturbance rejection. Unlike dynamic surface control approach, higher‐order sliding mode differentiator is adopted here as an alternative way to obtain the derivatives of the virtual control laws, thus leading to the exact tracking performance of backstepping control. Detailed stability analysis shows that both the line‐of‐sight angular rate and impact angle error can be stabilized in a small region around zero asymptotically. Simulation results explicitly show that accurate interception is achieved with a wide range of impact angles. Copyright © 2017 John Wiley & Sons, Ltd.     

非仿射纯反馈系统的间接自适应神经网络控制

针对非仿射纯反馈系统,提出了一种新的设计方案.与现有文献中方法不同,该方案不是直接利用逼近技巧构建理想的反馈控制器.首先通过自抗扰思想将非仿射纯反馈系统转化成含有未知控制系数以及未知非线性的仿射系统,并且证明了可行性.然后结合微分器和全调节径向基函数神经网络,利用自适应反演技巧设计了自抗扰控制器,微分器的引入避免了传统反演的计算复杂性.最后,从理论上证明了所设计的控制器能够保证闭环系统所有信号半全局一致有界,并且证明了系统状态渐进收敛到零点的残集内.仿真例子验证了算法的有效性.     

Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation

In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.     

An adaptive actuator failure compensation controller using outputfeedback

An adaptive control scheme using output feedback for output tracking is developed for systems with unknown actuator failures. Such actuator failures are characterized by some unknown inputs stuck at some unknown fixed values at unknown time instants. An effective output feedback controller structure is proposed for actuator failure compensation. When implemented with true matching parameters, the controller achieves desired plant-model output matching. When implemented with adaptive parameter estimates, the controller achieves asymptotic output tracking. A stable adaptive law is derived for parameter adaptation in the presence of parameter uncertainties. Closed-loop signal boundedness and asymptotic output tracking, despite the uncertainties in actuator failures and plant parameters, are ensured analytically and verified by simulation results     

Adaptive Output Feedback Control for a Class of Uncertain Stochastic Nonlinear Systems With Unknown Time‐Delays

This paper investigates the problem of output feedback control for a class of stochastic nonlinear systems with time‐delays. Using dynamic gain scaling technique, an adaptive update law is introduced to the observer and controller to deal with the unknown parameters. Based on the Lyapunov‐Krasovskii functional and stochastic Barbalat's lemma, it is proved that the proposed universal‐type adaptive output feedback controller can regulate all the states of the closed‐loop system almost surely. A simulation example is presented to illustrate the effectiveness of the proposed design procedure.     

Adaptive Non‐Backstepping Fuzzy Control for a Class of Uncertain Nonlinear Systems with Unknown Dead‐Zone Input

Based on the approximation property of fuzzy logic systems, we propose a novel non‐backstepping adaptive tracking control algorithm for a class of single input single output (SISO) strict‐feedback nonlinear systems with unknown dead‐zone input. In this algorithm, we introduce some novel state variables and coordinate transforms to convert the strict‐feedback form into a normal one, and it is not necessary to consider the traditional approximation‐based the backstepping scheme. Due to new states variables being unavailable, the tracking control is changed from a state‐feedback one to an output‐feedback one. So, observers need to be designed to estimate the indirect nonmeasurable states. According to Lyapunov stability analysis method, the developed controller can guarantee that all of the signals in the closed‐loop system will be ultimately uniformly bounded (UUB), and the output can track the reference signal very well. Simulation results are presented to show the effectiveness of the proposed approach.     

Output tracking control of a parallel-flow heat exchange process

In this paper, we consider an output tracking problem of a parallel-flow heat exchange process with distributed and boundary inputs. As the distributed inputs to the system, the output feedback control is first applied. Under zero boundary inputs, it is shown that the C₀-semigroup describing the closed-loop system satisfies the spectrum determined growth condition. Next, we apply a backstepping method to the design of the boundary inputs for output tracking. Our main result shows that the output of the system reaches a reference signal in finite time under both the output feedback control and the boundary control law derived by the backstepping method.     

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.     

Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks.

In this paper, dynamic output feedback control problem is investigated for a class of nonlinear interconnected systems with time delays. Decentralized observer independent of the time delays is first designed. Then, we employ the bounds information of uncertain interconnections to construct the decentralized output feedback controller via backstepping design method. Based on Lyapunov stability theory, we show that the designed controller can render the closed-loop system asymptotically stable with the help of the changing supplying function idea. Furthermore, the corresponding decentralized control problem is considered under the case that the bounds of uncertain interconnections are not precisely known. By employing the neural network approximation theory, we construct the neural network output feedback controller with corresponding adaptive law. The resulting closed-loop system is stable in the sense of semiglobal boundedness. The observers and controllers constructed in this paper are independent of the time delays. Finally, simulations are done to verify the effectiveness of the theoretic results obtained.