High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints.     

Continuous high order sliding mode controller design for a flexible air-breathing hypersonic vehicle

This paper investigates the problem of tracking control with uncertainties for a flexible air-breathing hypersonic vehicle (FAHV). In order to overcome the analytical intractability of this model, an Input–Output linearization model is constructed for the purpose of feedback control design. Then, the continuous finite time convergence high order sliding mode controller is designed for the Input–Output linearization model without uncertainties. In addition, a nonlinear disturbance observer is applied to estimate the uncertainties in order to compensate the controller and disturbance suppression, where disturbance observer and controller synthesis design is obtained. Finally, the synthesis of controller and disturbance observer is used to achieve the tracking for the velocity and altitude of the FAHV and simulations are presented to illustrate the effectiveness of the control strategies.     

Improved prescribed performance control for air-breathing hypersonic vehicles with unknown deadzone input nonlinearity

An improved prescribed performance controller is proposed for the longitudinal model of an air-breathing hypersonic vehicle (AHV) subject to uncertain dynamics and input nonlinearity. Different from the traditional non-affine model requiring non-affine functions to be differentiable, this paper utilizes a semi-decomposed non-affine model with non-affine functions being locally semi-bounded and possibly in-differentiable. A new error transformation combined with novel prescribed performance functions is proposed to bypass complex deductions caused by conventional error constraint approaches and circumvent high frequency chattering in control inputs. On the basis of backstepping technique, the improved prescribed performance controller with low structural and computational complexity is designed. The methodology guarantees the altitude and velocity tracking error within transient and steady state performance envelopes and presents excellent robustness against uncertain dynamics and deadzone input nonlinearity. Simulation results demonstrate the efficacy of the proposed method.     

Robust inertia-free attitude takeover control of postcapture combined spacecraft with guaranteed prescribed performance

In this paper, a robust inertia-free attitude takeover control scheme with guaranteed prescribed performance is investigated for postcapture combined spacecraft with consideration of unmeasurable states, unknown inertial property and external disturbance torque. Firstly, to estimate the unavailable angular velocity of combination accurately, a novel finite-time-convergent tracking differentiator is developed with a quite computationally achievable structure free from the unknown nonlinear dynamics of combined spacecraft. Then, a robust inertia-free prescribed performance control scheme is proposed, wherein, the transient and steady-state performance of combined spacecraft is first quantitatively studied by stabilizing the filtered attitude tracking errors. Compared with the existing works, the prominent advantage is that no parameter identifications and no neural or fuzzy nonlinear approximations are needed, which decreases the complexity of robust controller design dramatically. Moreover, the prescribed performance of combined spacecraft is guaranteed a priori without resorting to repeated regulations of the controller parameters. Finally, four illustrative examples are employed to validate the effectiveness of the proposed control scheme and tracking differentiator.     

Robust internal model control of servo motor based on sliding mode control approach

This paper proposes a robust internal model control (IMC) based on sliding mode control (SMC) approach for high-performance motion control of a servo motor subject to uncertainties and/or disturbances. The proposed control strategy considers not only the simplicity and intuition of the IMC-based controller for a prescribed tracking performance but also the effectiveness of the SMC scheme to guarantee the robustness of the servo system. Since the performance of the IMC-based controller can be analyzed via a SMC structure, a robust control law based on the SMC technique is introduced into the IMC scheme to decrease the sensitivity to uncertainties and enhance the resistance to disturbances. Moreover, the 2-degree-of-freedom IMC integrating the robust SMC scheme is developed to further improve the control performance. The stability is analyzed based on Lyapunov theory, and the theoretical results show that a prescribed transient tracking performance and a final tracking accuracy of the servo system can be guaranteed. Comparative simulations and experiments are investigated to verify the high performance nature of the proposed control strategy.     

Decentralized adaptive neural prescribed performance control for high-order stochastic switched nonlinear interconnected systems with unknown system dynamics

In this paper, the problem of decentralized adaptive neural backstepping control is investigated for high-order stochastic nonlinear systems with unknown interconnected nonlinearity and prescribed performance under arbitrary switchings. For the control of high-order nonlinear interconnected systems, it is assumed that unknown system dynamics and arbitrary switching signals are unknown. First, by utilizing the prescribed performance control (PPC), the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, at each recursive step, only one adaptive parameter is constructed to overcome the over-parameterization, and RBF neural networks are employed to tackle the difficulties caused by completely unknown system dynamics. At last, based on the common Lyapunov stability method, the decentralized adaptive neural control method is proposed, which decreases the number of learning parameters. It is shown that the designed common controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the prescribed tracking control performance is guaranteed under arbitrary switchings. The simulation results are presented to further illustrate the effectiveness of the proposed control scheme.     

含磁滞补偿的电液伺服系统预设性能跟踪控制

针对电液伺服系统普遍存在的参数不确定性、不确定非线性(磁滞、摩擦、外干扰等),提出一种基于自适应鲁棒控制的含磁滞补偿的预设性能跟踪控制策略。以阀控单出杆液压缸位置伺服系统为例,首先建立了含磁滞非线性的系统数学模型,然后通过定义预设性能函数,实现了对跟踪误差收敛速率、最大超调量和稳态精度的预先规划,基于规划后的转换误差设计了自适应鲁棒控制器,并提高了稳态和瞬态跟踪性能。仿真对比结果表明：该控制策略可以减小磁滞对系统跟踪精度的影响,提高跟踪误差的收敛速度,减小最大超调量,最终实现优良的跟踪性能。     

匹配和不匹配干扰共存时电液伺服系统预设性能渐近跟踪控制

针对电液伺服系统匹配和不匹配干扰共存的特点,兼顾瞬态性能和稳态性能需求,提出一种新型高精度跟踪控制策略。以阀控电液位置伺服系统为例,建立了包含匹配和不匹配干扰的系统非线性数学模型,定义预设性能函数规划控制误差,基于规划后的转换误差设计反步控制器,并融合采用干扰上界估计的连续渐近控制技术处理匹配和不匹配干扰,获得了可预设的瞬态性能和渐近稳态性能,通过Lyapunov分析证明了稳定性。两种位置指令跟踪试验的结果表明,相比PID控制和反馈线性化控制,初始阶段跟踪精度提高超22.9%,相比仅引入预设性能函数的控制器,稳态跟踪精度提高超42.9%。     

Fixed-time output feedback trajectory tracking control of marine surface vessels subject to unknown external disturbances and uncertainties

This paper proposes a novel fixed-time output feedback control scheme for trajectory tracking of marine surface vessels (MSVs) subject to unknown external disturbances and uncertainties. A fixed-time extended state observer (FESO) is proposed to estimate unknown lumped disturbances and unmeasured velocities, and the observation errors will converge to zero in fixed time. Based on the estimated values, a novel fixed-time trajectory tracking controller is designed for an MSV to track a time-varying reference trajectory by the extension of an adding a power integrator (API), and the tracking errors can converge to zero in fixed time as well. Additionally, the convergence time of the controller and the FESO is independent of initial state values. Finally, simulation results and comparisons illustrate the superiority of the proposed control scheme.     

Tracking control of air-breathing hypersonic vehicles with non-affine dynamics via improved neural back-stepping design

This study considers the design of a new back-stepping control approach for air-breathing hypersonic vehicle (AHV) non-affine models via neural approximation. The AHV's non-affine dynamics is decomposed into velocity subsystem and altitude subsystem to be controlled separately, and robust adaptive tracking control laws are developed using improved back-stepping designs. Neural networks are applied to estimate the unknown non-affine dynamics, which guarantees the addressed controllers with satisfactory robustness against uncertainties. In comparison with the existing control methodologies, the special contributions are that the non-affine issue is handled by constructing two low-pass filters based on model transformations, and virtual controllers are treated as intermediate variables such that they aren't needed for back-stepping designs any more. Lyapunov techniques are employed to show the uniformly ultimately boundedness of all closed-loop signals. Finally, simulation results are presented to verify the tracking performance and superiorities of the investigated control strategy.     

ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

0 INTRODUCTION(The satisfied control of the overall weld process is not easily accomplished, largely due to the inadequacies of the available process models. Without exceptions, most welding control methods are based upon the analytical welding models. Although these models are derived directly from the physical laws that govern the main features of the weld pool, a number of assumptions are made to obtain the mathematical solutions and some variables are ignored due to the complexity of t…     

Robust integral of neural network and precision motion control of electrical–optical gyro-stabilized platform with unknown input dead-zones

Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.     

Adaptive neural network based position tracking control for Dual-master/Single-slave teleoperation system under communication constant time delays

The novel trajectory tracking control strategies for trilateral teleoperation systems with Dual-master/Single-slave robot manipulators under communication constant time delays are proposed in this article. By incorporating this design technique into the neural network (NN) based adaptive control framework, two controllers are designed for the trilateral teleoperation systems in free motion. First, with acceleration measurements, an adaptive controller under the synchronization variables containing the position and velocity error is constructed to guarantee the position and velocity tracking errors between the trilateral teleoperation systems asymptotically converge to zero. Second, without acceleration measurements, an adaptive controller under the new synchronization variables is presented such that the trilateral teleoperation systems can obtain the same trajectory tracking performance as the first controller. Third, in term of establishing suitable Lyapunov–Krasovskii functionals, the asymptotic tracking performances of the trilateral teleoperation systems can be derived independent of the communication constant time delays. Moreover, these two controllers are obtained without the knowledge of upper bounds of the NN approximation errors, respectively. Finally, simulation results are presented to demonstrate the validity of the proposed methods.     

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.

     

Adaptive output control of a mobile manipulator hanging from a quadcopter unmanned vehicle

This article summarizes the comprehensive design of an adaptive control that solves the trajectory tracking of a four-rotor unmanned aerial vehicle supplied by an own-designed grasping device. The controller design uses the regular proportional–derivative (PD) structure which is common to solve regulation, as well as tracking problems for robotic systems. The PD structure needs the estimation of the time-derivative to be exerted on-line. The super-twisting algorithm served to operate as a decentralized estimator of the derivatives for the tracking errors. The controller gains were adjusted by the differential laws aimed to track attainable reference trajectories. The adaptive strategy adjusts the controller gains enforcing the convergence of both the estimation and the tracking errors. A numerical example showed the observer/controller performance. A comparison with a classic non-adaptive PD controller confirms the effectiveness on the tracking task by the proposed design. In addition, a set of virtual numerical evaluations using the parameters of a real quadcopter system confirmed the performance benefits of the adaptive controller based on the PD structure aided with the estimation of the velocity obtained by the observer.     

永磁同步电机调速系统的自抗扰控制

提出了一种利用反双曲正弦函数的一阶自抗扰控制器,以提高永磁同步电机正弦波脉宽调制(SPWM)调速系统的跟踪精度。研究了永磁同步电机转速环的数学模型;分别设计了一阶跟踪微分器和二阶扩张状态观测器,利用李雅普诺夫函数分析了它们的收敛性;构造了转速环的一阶自抗扰控制器,同时证明了一阶自抗扰控制误差系统的渐近稳定性。最后,将该新型一阶自抗扰控制器作为永磁同步电机的转速调节器,分析了自抗扰控制永磁同步电机的SPWM调速系统。仿真实验表明:自抗扰控制调速系统速度阶跃跟踪的调整时间约为0.15 s,稳态误差小于0.28 r/min;同一调速系统正弦响应的最大跟踪误差约为17 r/min。与PI控制调速系统相比,自抗扰控制永磁同步电机调速系统阶跃响应快速而平稳,无超调,稳态误差小;另外,系统正弦响应的跟踪性能好,跟踪误差小。     

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.     

Sliding mode control of two-wheeled welding mobile robot for tracking smooth curved welding path

In this paper, a nonlinear controller based on sliding mode control is applied to a two-wheeled Welding Mobile Robot (WMR) to track a smooth curved welding path at a constant velocity of the welding point. The mobile robot is considered in terms of dynamics model in Cartesian coordinates under the presence of external disturbance, and its parameters are exactly known. It is assumed that the disturbance satisfies the matching condition with a known boundary. To obtain the controller, the tracking errors are defined, and the two sliding surfaces are chosen to guarantee that the errors converge to zero asymptotically. Two cases are to be considered : fixed torch and controllable torch. In addition, a simple way of measuring the errors is introduced using two potentiometers. The simulation and experiment on a two-wheeled welding mobile robot are provided to show the effectiveness of the proposed controller.     

Adaptive output-feedback control with prescribed performance for trajectory tracking of underactuated surface vessels

In this paper, we address the problem of trajectory tracking control of underactuated surface vessels in a quantitative method with only position and attitude available. Combined with high-gain observer, parameter compression algorithm and performance function, an adaptive control scheme with prescribed performance is proposed. The high-gain observer is constructed to estimate the velocities, and the parameter compression algorithm is adopted to address persistent perturbations and model uncertainties in a more concise way. By prescribed performance function, the controller can be designed with prescribed performance. The results about system stability is given and proved by using the Lyapunov direct method. The signals concerning with all the errors converge to a bounded set. Compared with the existing methods, the developed scheme can reduce the number of tuning parameters, and guarantee the tracking errors bounded within the prescribed performance constraints in the transformed coordinate, which means the steady errors, convergence rates and maximum overshoots can be guaranteed by the performance function. Comparison and numerical simulations are given to demonstrate the effectiveness of the proposed scheme.     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.