Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines

Wind power plants have nonlinear dynamics and contain many uncertainties such as unknown nonlinear disturbances and parameter uncertainties. Thus, it is a difficult task to design a robust reliable controller for this system. This paper proposes a novel robust fractional-order sliding mode (FOSM) controller for maximum power point tracking (MPPT) control of doubly fed induction generator (DFIG)-based wind energy conversion system. In order to enhance the robustness of the control system, uncertainties and disturbances are estimated using a fractional order uncertainty estimator. In the proposed method a continuous control strategy is developed to achieve the chattering free fractional order sliding-mode control, and also no knowledge of the uncertainties and disturbances or their bound is assumed. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov׳s stability theory. Simulation results in the presence of various uncertainties were carried out to evaluate the effectiveness and robustness of the proposed control scheme.     

电液力伺服系统自适应抗扰控制研究

考虑到电液伺服系统中存有各种非线性因素、不确定干扰以及参数时变,为了提高干扰下电液力伺服系统的控制精度,以电液伺服振动实验台作为控制对象,构建其非线性模型,同时使用参数自适应率对不定参数进行补偿,并在反演控制器中引入滑模控制以降低系统的干扰敏感性,利用Lyapunov理论保证闭环系统的全局稳定。对设计的控制器进行实验,模拟在有未知外部位置干扰下的力控制,提升系统的稳定性。实验结果证明,此控制方法能够有效地提升电液力伺服系统的抗干扰跟踪性能。     

Memoryless disturbance-observer-based adaptive tracking of uncertain pure-feedback nonlinear time-delay systems with unmatched disturbances

This paper presents a delay-independent nonlinear disturbance observer (NDO) design methodology for adaptive tracking of uncertain pure-feedback nonlinear systems in the presence of unknown time delays and unmatched external disturbances. Compared with all existing NDO-based control results for uncertain lower-triangular nonlinear systems where unknown time delays have been not considered, the main contribution of this paper is to develop a delay-independent design strategy to construct an NDO-based adaptive tracking scheme in the presence of unknown time-delayed nonlinearities and non-affine nonlinearities unmatched in the control input. The proposed delay-independent scheme is constructed by employing the appropriate Lyapunov-Krasovskii functionals and the same function approximators for the NDO and the controller. It is shown that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin.     

Nonlinear robust dual-loop control for electro-hydraulic load simulator

This paper investigates on the high performance torque control of electro-hydraulic load simulator (EHLS). In order to suppress actuator׳s motion disturbance, a nonlinear robust dual-loop control scheme is developed, which consists of a open-loop nonlinear velocity feed-forward compensator and a closed-loop nonlinear deterministic robust torque controller. The main function of the open-loop compensator is to decouple actuator׳s active motion disturbance, whereas the torque loop controller aims at guaranteeing the dynamics performance of tracking torque reference. Besides actuator׳s motion disturbance, both the nonlinearity characteristics and friction problem of the EHLS system are taken into consideration in this paper. The effectiveness of the developed method are verified through comparative co-simulations and experiments.     

Adaptive backstepping slide mode control of pneumatic position servo system

With the price decreasing of the pneumatic proportional valve and the high performance micro controller, the simple structure and high tracking performance pneumatic servo system demonstrates more application potential in many fields. However, most existing control methods with high tracking performance need to know the model information and to use pressure sensor. This limits the application of the pneumatic servo system. An adaptive backstepping slide mode control method is proposed for pneumatic position servo system. The proposed method designs adaptive slide mode controller using backstepping design technique. The controller parameter adaptive law is derived from Lyapunov analysis to guarantee the stability of the system. A theorem is testified to show that the state of closed-loop system is uniformly bounded, and the closed-loop system is stable. The advantages of the proposed method include that system dynamic model parameters are not required for the controller design, uncertain parameters bounds are not need, and the bulk and expensive pressure sensor is not needed as well. Experimental results show that the designed controller can achieve better tracking performance, as compared with some existing methods.     

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.     

Trajectory tracking for two-degree of freedom helicopter system using a controller-disturbance observer integrated design

Trajectory tracking control for helicopters, which are widely used in severe situations such as military and rescue missions, is a challenging field of research. In helicopter system, the stability problem and predefined trajectories tracking are main challenges, especially in the presence of external disturbances and dynamic model uncertainties. Hence, a robust control design is needed for tracking the desired references. There has been a lot of motivation for solving these problems with simpler methods and also reducing the couplings in the helicopter system to achieve better performance, as the presented paper attempts to fill these gaps. This paper focuses on designing control laws for two-degree of freedom helicopter system while assuring the closed-loop stability. A nonlinear disturbance observer-based control (NDOBC) is designed for attenuating the effects of exogenous disturbances. Trajectory tracking controller and nonlinear disturbance observer are formulated in the form of two linear matrix inequality (LMI) problems. The closed-loop system stability, including controller and observer, is investigated by Lyapunov theorem. The effectiveness of the proposed design for tracking the trajectories (vertical flight and pitch angle rotor blade) and disturbance estimation is verified by simulation results.     

利用模拟退火优化快速反射镜控制策略

由于快速反射镜(FSM)系统在不同应用场合下需要不同有效带宽和闭环带宽,本文基于压电FSM控制系统建立系统模型,通过分析系统光轴抖动情况,对FSM控制算法进行了优化。首先,测得系统闭环Bode图,利用模拟退火算法求取系统传递函数;然后,结合辨识模型与模拟退火算法,提出了一种满足不同应用场合的全局最优PID控制器。最后,通过阶跃响应测试验证辨识模型的正确性,通过闭环实验测试验证最优控制器的有效性。结果表明,辨识模型与实际系统在中低频段符合得很好,阶跃响应曲线基本一致。采用最优控制器控制的系统有效带宽为35 Hz,闭环带宽为70Hz,跟踪精度提高了47%,基本满足当前实验环境下对FSM性能的要求。提出的系统显示良好的低频跟随能力和高频干扰抑制能力,跟踪精度高,器件损耗小。     

Combined feedforward and model-assisted active disturbance rejection control for non-minimum phase system

Control of the non-minimum phase (NMP) system is challenging, especially in the presence of modelling uncertainties and external disturbances. To this end, this paper presents a combined feedforward and model-assisted Active Disturbance Rejection Control (MADRC) strategy. Based on the nominal model, the feedforward controller is used to produce a tracking performance that has minimum settling time subject to a prescribed undershoot constraint. On the other hand, the unknown disturbances and uncertain dynamics beyond the nominal model are compensated by MADRC. Since the conventional Extended State Observer (ESO) is not suitable for the NMP system, a model-assisted ESO (MESO) is proposed based on the nominal observable canonical form. The convergence of MESO is proved in time domain. The stability, steady-state characteristics and robustness of the closed-loop system are analyzed in frequency domain. The proposed strategy has only one tuning parameter, i.e., the bandwidth of MESO, which can be readily determined with a prescribed robustness level. Some comparative examples are given to show the efficacy of the proposed method. This paper depicts a promising prospect of the model-assisted ADRC in dealing with complex systems.     

PD plus error-dependent integral nonlinear controllers for robot manipulators with an uncertain Jacobian matrix

In framework of traditional PID controllers, there are only three parameters available to tune, as a result, performance of the resulting system is always limited. As for Cartesian regulation of robot manipulators with uncertain Jacobian matrix, a scheme of PID controllers with error-dependent integral action is proposed. Compare with traditional PID controllers, the error-dependent integration is employed in the proposed PID controller, in which more parameters are available to be tuned. It provides additional flexibility for controller characteristics and tuning as well, and hence makes better transient performance. In addition, asymptotic stability of the resulting closed-loop system is guaranteed. All signals in the system are bounded when exogenous disturbances and measurement noises are bounded. Numerical example demonstrates the superior transient performance of the proposed controller over the traditional one via Cartesian space set-point manipulation of two-link robotic manipulator.     

Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems

In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems. Second, an adaptive radial basis function neural network estimator-based continuous second order sliding mode control algorithm (CSOSMC-ANNE) is adopted. In CSOSMC-ANNE control methodology, a radial basis function neural network with adaptive parameters is exploited to approximate the unknown system parameters and improve performance against perturbations. Also, the discontinuous switching control of SOSMC is supplanted with a smooth continuous control action to completely eliminate the chattering phenomenon. The convergence and global stability of the closed-loop system are proved using Lyapunov stability method. Numerical computer simulations, with dynamical model of the nonlinear inverted pendulum system, are presented to demonstrate the effectiveness and advantages of the presented control scheme.     

Neural network adaptive position tracking control of underactuated autonomous surface vehicle

The present study investigates the position tracking control of the underactuated autonomous surface vehicle, which is subjected to parameters uncertainties and external disturbances. In this regard, the backstepping method, neural network, dynamic surface control and the sliding mode method are employed to design an adaptive robust controller. Moreover, a Lyapunov synthesis is utilized to verify the stability of the closed-loop control system. Following innovations are highlighted in this study: (i) The derivatives of the virtual control signals are obtained through the dynamic surface control, which overcomes the computational complexities of the conventional backstepping method. (ii) The designed controller can be easily applied in practical applications with no requirement to employ the neural network and state predictors to obtain model parameters. (iii) The prediction errors are combined with position tracking errors to construct the neural network updating laws, which improves the adaptation and the tracking performance. The simulation results demonstrate the effectiveness of the proposed position tracking controller.

     

四旋翼微型飞行器的区间二型模糊神经网络自适应控制

针对四旋翼微型飞行器控制系统中存在不确定性、外界干扰等影响控制精度的问题,提出了基于区间二型模糊神经网络(IT_IIFNN)的四旋翼微型飞行器自适应控制方案.首先,根据四旋翼微型飞行器的动力学模型,设计了基于IT_IIFNN的四旋翼微型飞行器自适应控制器,该控制器由两部分构成,其中IT_IIFNN用来在线逼近系统不确定性;鲁棒补偿器用来实时补偿IT_IIFNN的逼近误差以及外界干扰.其次,利用Lyapunov稳定理论证明此飞行器控制系统闭环稳定性.最后,通过四旋翼微型飞行器样机来验证IT_IIFNN自适应控制器的优越性.验证结果显示,在加入风速为1.5 m/s的外界干扰条件下,跟踪误差可近似达到10-2.结果表明,IT_IIFNN自适应控制器具有良好的跟踪精度、稳定性及鲁棒性.     

Active steering wheel shimmy control for electric vehicle by sampled-data output feedback

In this paper, a new two Degree of Freedom (DOF) shimmy dynamic model of an Electric Vehicle (EV) with independent suspension subject to uncertain disturbances is built via Lagrange’s theorem firstly. Secondly, Based on the built model, an active control method is proposed to solve the shimmy problem of the steering system via a sampled-data output feedback controller. The output feedback domination approach is also used to dominate uncertain disturbances by using a scaling gain. With the help of these tools, the sampling period and scaling gain are calculated to guarantee global stability and disturbance attenuation for the closed-loop control system. Finally, simulations and tests are conducted to verify the effectiveness of the sampled-data output feedback controller for the EV’s shimmy problem under different conditions.     

预测函数控制及其在工业电加热炉中的应用

首先介绍并分析了预测函数控制方法的主要思想和特点,讨论了相应的控制器参数设计方法。进而针对典型的一阶时滞系统,给出了具体的预测函数控制器设计方法,并应用于一工业电加热炉对象的温度控制系统,取得了很好的控制效果。结果表明预测函数控制是一种具有较好控制精度,较快跟踪速度,良好动态特性的控制方法     

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties

This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy.     

Adaptive sampling rate control for networked systems based on statistical characteristics of packet disordering

This paper investigates an adaptive sampling rate control scheme for networked control systems (NCSs) subject to packet disordering. The main objectives of the proposed scheme are (a) to avoid heavy packet disordering existing in communication networks and (b) to stabilize NCSs with packet disordering, transmission delay and packet loss. First, a novel sampling rate control algorithm based on statistical characteristics of disordering entropy is proposed; secondly, an augmented closed-loop NCS that consists of a plant, a sampler and a state-feedback controller is transformed into an uncertain and stochastic system, which facilitates the controller design. Then, a sufficient condition for stochastic stability in terms of Linear Matrix Inequalities (LMIs) is given. Moreover, an adaptive tracking controller is designed such that the sampling period tracks a desired sampling period, which represents a significant contribution. Finally, experimental results are given to illustrate the effectiveness and advantages of the proposed scheme.     

Nonlinear gearshifts control of dual-clutch transmissions during inertia phase

In this paper, a model-based nonlinear gearshift controller is designed by the backstepping method to improve the shift quality of vehicles with a dual-clutch transmission (DCT). Considering easy-implementation, the controller is rearranged into a concise structure which contains a feedforward control and a feedback control. Then, robustness of the closed-loop error system is discussed in the framework of the input to state stability (ISS) theory, where model uncertainties are considered as the additive disturbance inputs. Furthermore, due to the application of the backstepping method, the closed-loop error system is ordered as a linear system. Using the linear system theory, a guideline for selecting the controller parameters is deduced which could reduce the workload of parameters tuning. Finally, simulation results and Hardware in the Loop (HiL) simulation are presented to validate the effectiveness of the designed controller.     

Adaptive output feedback tracking of nonlinear systems with uncertain nonsymmetric dead-zone input

In this paper, the problem of adaptive practical tracking is investigated by output feedback for a class of uncertain nonlinear systems subject to nonsymmetric dead-zone input nonlinearity with parameters of dead-zone being unknown. Instead of constructing the inverse of dead-zone nonlinearity, an adaptive robust control scheme is developed by designing an output compensator including two dynamic gains based respectively on identification and non-identification mechanism. With the aid of dynamic high-gain scaling approach and Backstepping method, stability analysis of the closed-loop system is proceeded using non-separation principle, which shows that the proposed controller guarantees that all closed-loop signal is bounded while the output of system tracks a broad class of bounded reference trajectories by arbitrarily small error prescribed previously. Finally, two examples are given to illustrate our controller effective.