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.     

Distributed robust adaptive control of high order nonlinear multi agent systems

In this paper, a robust adaptive neural network based controller is presented for multi agent high order nonlinear systems with unknown nonlinear functions, unknown control gains and unknown actuator failures. At first, Neural Network (NN) is used to approximate the nonlinear uncertainty terms derived from the controller design procedure for the followers. Then, a novel distributed robust adaptive controller is developed by combining the backstepping method and the Dynamic Surface Control (DSC) approach. The proposed controllers are distributed in the sense that the designed controller for each follower agent only requires relative state information between itself and its neighbors. By using the Young's inequality, only few parameters need to be tuned regardless of NN nodes number. Accordingly, the problems of dimensionality curse and explosion of complexity are counteracted, simultaneously. New adaptive laws are designed by choosing the appropriate Lyapunov-Krasovskii functionals. The proposed approach proves the boundedness of all the closed-loop signals in addition to the convergence of the distributed tracking errors to a small neighborhood of the origin. Simulation results indicate that the proposed controller is effective and robust.     

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations.     

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies.     

Optimal control for earth pressure balance of shield machine based on action-dependent heuristic dynamic programming

Earth pressure balance (EPB) shield has been widely used in underground construction. The excavation face stability is crucial to avoid the accidents caused by EPB shield tunneling, so that it is very important to propose an effective control method for the earth pressure balance in sealed cabin. Considering the problem that stable automatic control of the earth pressure in shield’s sealed cabin is difficult, an optimal control method of the earth pressure is proposed based on action-dependent heuristic dynamic programming (ADHDP), which can realize online autonomous learning and adaptive control in tunneling process. According to Bellman’s principle of optimality, the cost function with respect to the sealed cabin’s earth pressure is given. In addition, the action network and critic network of ADHDP controller are constructed. The critic network approximates the cost function and feeds error back into the action network. With the goal of minimizing the cost function, the action network utilizes the critic network’s error to optimize screw conveyor speed. The simulation results show that the earth pressure controller based on ADHDP can realize the earth pressure balance control, and the control process is steadier. Moreover, ADHDP controller has good dynamic performance and anti-interference ability.     

Distributed coordinated attitude tracking control for spacecraft formation with communication delays

This paper investigates the distributed coordinated attitude tracking control problem for spacecraft formation with time-varying communication delays under the condition that the dynamic leader spacecraft is a neighbor of only a subset of follower spacecrafts. We consider two cases for the leader spacecraft: i) the attitude derivative is constant, and ii) the attitude derivative is time-varying. In the first case, a distributed estimator is proposed for each follower spacecraft by using its neighbors’ information with communication delays. In the second case, to express the dynamic leader’s attitude, an improved distributed observer is developed to estimate the leader’s information. Based on the estimated values, adaptive coordinated attitude tracking control laws are designed to compensate for parametric uncertainties and unknown disturbances. By employing the Lyapunov–Krasovskii functional approach, the attitude tracking errors and estimation errors are proven to converge to zero asymptotically. Numerical simulations are presented to illustrate the effectiveness of theoretical results.     

Adaptive sensor fault-tolerant control for a class of multi-input multi-output nonlinear systems: Adaptive first-order filter-based dynamic surface control approach

This paper is concerned with the adaptive fault-tolerant control (FTC) problem for a class of multivariable nonlinear systems with external disturbances, modeling errors and time-varying sensor faults. The bias, drift, loss of accuracy and loss of effectiveness faults can be effectively accommodated by this scheme. The dynamic surface control (DSC) technique and adaptive first-order filters are brought together to design an adaptive FTC scheme which can reduce significantly the computational burden and improve further the control performance. The adaptation laws are constructed using novel low-pass filter based modification terms which enable under high learning or modification gains to achieve robust, fast and high-accuracy estimation without incurring undesired high-frequency oscillations. It is proved that all signals in the closed-loop system are uniformly ultimately bounded and the tracking-errors can be made arbitrary close to zero. Simulation results are provided to verify the effectiveness and superiority of the proposed FTC method.     

Event-triggered distributed receding horizon control for tracking and formation of homogeneous multi-agent systems

This paper proposes an event-triggered distributed receding horizon control (DRHC) approach for the formation and tracking problems of homogeneous multi-agent systems. For each agent, an event-triggering condition, based on assumed predictive information of the neighbours, is derived from stability analysis. Considering the uncertain deviation between the assumed and true predictive information, we design a time-varying compatibility constraint for the individual optimization problem. In the event-triggered DRHC algorithm, each agent solves the optimization problem and communicates with its neighbours only when the event-triggering condition is satisfied, so the communication and computation burden are reduced. Moreover, guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. A simulation example is provided to illustrate effectiveness of the proposed approach.     

采用自适应一致性UKF的分布式目标跟踪

由于传统分布式跟踪方法在先验噪声协方差与其实际值不相匹配时跟踪误差较大,提出了一种采用自适应一致性无迹卡尔曼滤波的分布式目标跟踪方法,该方法首先执行分布式UKF算法得到对当前移动目标状态的估计值,然后通过一个系统错误检测机制,确定是否需要对噪声协方差值进行更新。如需要,则根据当前获得的测量信息去估计当前噪声协方差,并联合该估计值和先前的噪声协方差值获得一个新的先验噪声协方差值。最后根据新获得的噪声协方差值对获得的目标状态估计值进行修正。实验结果表明该方法具有较好的准确性和鲁棒性：在噪声未知环境下,基于ACUKF的分布式跟踪方法相比于基于容积信息滤波和基于分布式无迹卡尔曼滤波的跟踪方法,最大跟踪误差值分别减少了49.93%和 51.46%;在目标过程噪声发生动态变化的情况下,提出的方法相比于上述两种传统跟踪方法,跟踪误差值分别减少了40.67%和40.06%。     

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.     

Distributed model reference adaptive containment control of heterogeneous uncertain multi-agent systems

In this paper, a distributed model reference adaptive control (MRAC) design framework is proposed for containment control of heterogeneous uncertain multi-agent systems (MAS). Both groups of leaders and followers are considered to have general linear dynamics with the leaders subject to bounded external inputs and the followers subject to uncertain system dynamics. Two distributed adaptive control protocols are developed under this framework. The first protocol assumes measurable leaders’ input signals for a subset of the followers, and employs distributed observers with state-feedback adaptive controllers to achieve exact containment control performance. The second protocol incorporates robust adaptive control with nonlinear compensator techniques to handle a more challenging scenario of unmeasurable bounded leaders’ inputs. Convergence of the containment control errors to an arbitrarily adjustable neighborhood of the origin is guaranteed with the second protocol. The proposed MRAC framework provides a promising alternative solution over the prevailing cooperative output regulation framework for heterogeneous linear MAS containment control. It enables us to handle more general system settings under more stringent control environments with limited accessibility of leaders’ information and uncertain follower dynamics. Effectiveness and usefulness of the proposed approaches are demonstrated through extensive simulation studies, including an application to containment control of multiple nonholonomic mobile robots.     

基于动态规划的分层立体匹配算法研究

动态规划是双目立体匹配的经典算法,针对控制点动态规划算法易产生横向条纹以及立体匹配普遍的边缘性和弱纹理区域问题,提出一种基于金字塔分层双向动态规划的改进立体匹配算法。该算法将分层模型加入传统动态规划方法,以低像素层级为高像素层级提供控制点集,并在匹配代价计算中采用一种自适应相关性测度函数,加以匹配代价滤波,提高算法精确度及实时性并获取高精度视差图。以Middlebury标准库中的图片以及实拍图片作为实验对象,实验表明所提出的方法具有较好的性能。     

混沌光学系统之快速神经网络自适应控制研究

提出一种用于光动力学系统控制之快速神经网络自适应控制技术。该技术以一前向神经网络作为光动力学系统之系统辨识器,由其与光动力学系统之输出差值对系统控制参数进行调整以达到控制目的,由于神经网络系统辨识器在混沌加速BP算法的支持下可从光动力学系统输出时间序列进行快速动力学模型重构,因而此控制技术特别适用于对未知动力学表述的光动力学系统进行快速控制。文中成功地将此神经网络自适应控制技术应用于布喇格声光混沌系统进行的快速控制仿真实验中     

Adaptive spectral band identification method in noncontact temperature measurement based on dynamic programming

An adaptive method based on dynamic programming is proposed to identify the spectral band for noncontact measurement of surface temperature of heatshield materials when Fourier transform infrared (FTIR) spectrometer is used to collect the radiation spectrum in the dynamic heating environment of a high-frequency plasma wind tunnel. First, the radiation spectrum is converted to a time series. Then, high-frequency parts of the measurement spectral signal are obtained by multi-resolution analysis of one-dimensional discrete wavelet and then the suitable spectral band required by a noncontact temperature measurement is adaptively identified based on dynamic programming. Eventually, surface temperature and its corresponding emissivity can be determined. Results of the experiment conducted on a benchmark material (graphite) in the dynamic heating environment of high frequency plasma wind tunnel show the proposed method to be practical.     

H∞ non-fragile observer-based dynamic event-triggered sliding mode control for nonlinear networked systems with sensor saturation and dead-zone input

This paper considers the problem of robust non-fragile observer-based dynamic event-triggered sliding mode control (SMC) for a class of discrete-time Lipschitz nonlinear networked control systems subject to sensor saturation and dead-zone input nonlinearity. First, an improved dynamic event-triggered scheme (DETS) in consideration of sensor saturation is proposed to reduce the number of data transmission. Next, a non-fragile observer is designed to estimate the system state, which facilitates the construction of the discrete sliding surface. By using a reformulated Lipschitz property, the error dynamics and sliding mode dynamics are modeled as a unified linear parameter varying (LPV) networked system with time-varying delays. Then, based on this model, sufficient conditions are established to guarantee the resulting closed-loop system to be asymptotically stable with a given disturbance attenuation level. Furthermore, an observer-based event-triggered SMC law is designed to drive the trajectories of the observer system onto a region near equilibrium point in a finite time in the presence of dead-zone input nonlinearity. Finally, two practical examples are employed to demonstrate the effectiveness of the proposed method.     

基于自适应模糊神经网络辨识的电液伺服系统L2增益设计

为克服电液伺服系统不确定性、非线性、估计误差和干扰等因素对系统稳定性和精度的影响，提出了基于自适应模糊神经网络辨识的电液伺服系统L2增益设计方法。用自适应模糊神经网络在线估计包括系统不确定性和非线性在内的未知动态特性，同时用增益自适应变结构补偿自适应模糊神经网络的估计误差，用系统L2增益设计方法抑制干扰对系统的影响，以期使系统对不确定性和非线性具有鲁棒性，而且从干扰到描述系统跟踪误差的评价函数的L2增益小于指定值。     

Neural network adaptive sliding mode control for omnidirectional vehicle with uncertainties

This paper presents a novel neural network adaptive sliding mode control (NNASMC) method to design the dynamic control system for an omnidirectional vehicle. The omnidirectional vehicle is equipped with four Mecanum wheels that are actuated by separate motors, and thus has the omnidirectional mobility and excellent athletic ability in a narrow space. Considering various uncertainties and unknown external disturbances, kinematic and dynamic models of the omnidirectional vehicle are established. The inner-loop controller is designed based the sliding mode control (SMC) method, while the out-loop controller uses the proportion integral derivative (PID) method. In order to achieve the stable and robust performance, the artificial neural network (ANN) based adaptive law is introduced to model and estimated the various uncertainties disturbances. Stability and robustness of the proposed control method are analyzed using the Lyapunov theory. The performance of the proposed NNASMC method is verified and compared with the classical PID controller and SMC controller through both the computer simulation and the platform experiment. Results validate the effectiveness and robustness of the NNASMC method in presence of uncertainties and unknown external disturbances.     

自适应控制在热连轧轧机的负荷优化分配中的应用

本文在简要介绍热连轧轧机负荷优化分配的意义后,提出了一种利用模糊自适应理论的方法把多目标函数处理成同等约束条件下的单目标函数;再以改进的动态规划算法,以不同的步长对所求的解进行多重动态规划模式的寻优,基本上克服了因步长选取不当而带来的非最优解的问题。实验结果表明,这种综合模糊自适应和变步长的动态规划算法节能效果良好,具有很好的工程应用价值。     

Learning from adaptive neural network output feedback control of a unicycle-type mobile robot

This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.