Neural Network Based Robust Controller for Trajectory Tracking of Underwater Vehicles

A robust neural network controller （NNC） is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A twolayer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded （UUB） stabilities of tracking errors and NN weights are guaran- teed through the proposed controller. An on-line NN weights tuning algorithm is also propesed. Good performances of the tracking control system are illustrated bv the results of numerical simulations.

Distributed Adaptive Tracking Control for Unknown Nonlinear Networked Systems

在这份报纸，我们为易于一个积极领导人，其仅仅说罐头的非线性的不明确的联网的系统的一个类调查合作追踪问题部分被测量，输入隧道也被扰乱。由神经网络(NN ) 的优点技术，追随者的动力学适当地在某些基础功能上被建模，他们的输入隧道被假定也被扰乱。在这个工作，基于观察员的适应控制为可以有非相同的动力学的非线性的联网的系统被建议。它被适当地在一些图状况下面选择参数经由 Lyapunov 理论(UUB ) 显示出全面系统最终一致地合作地被围住。最后，几数字模拟为建议适应控制器的确认被详细描述。

Design of a Networked Tracking Control System With a Data-based Approach

Tracking control is a very challenging problem in the networked control system(NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the networked tracking control system(NTCS). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of fuzzy cluster modelling(FCM)technology. Then, the deduced error and error change in the future are treated as inputs of a fuzzy sliding mode controller(FSMC) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.

Dynamics Modeling and Robust Trajectory Tracking Control for a Class of Hybrid Humanoid Arm Based on Neural Network

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm（HHA）, which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network（NN）-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function（RBF） NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.

Robust Fuzzy Tracking Control for Nonlinear Networked Control Systems with Integral Quadratic Constraints

This paper investigates the robust tracking control problcm for a class of nonlinear networked control systems （NCSs） using the Takagi-Sugeno （T-S） fuzzy model approach. Based on a time-varying delay system transformed from the NCSs, an augmented Lyapunov function containing more useful information is constructed. A less conservative sufficient condition is established such that the closed-loop systems stability and time-domain integral quadratic constraints （IQCs） are satisfied while both time-varying network- induced delays and packet losses are taken into account. The fuzzy tracking controllers design scheme is derived in terms of linear matrix inequalities （LMIs） and parallel distributed compensation （PDC）. Furthermore, robust stabilization criterion for nonlinear NCSs is given as an extension of the tracking control result. Finally, numerical simulations are provided to illustrate the effectiveness and merits of the proposed method.

An optimal adaptive H-infinity tracking control design via wavelet network

In this paper, an optimal adaptive H-infinity tracking control design method via wavelet network for a class of uncertain nonlinear systems with external disturbances is proposed to achieve H-infinity tracking performance. First, an alternate tracking error and a performance index with respect to the tracking error and the control effort are introduced in order to obtain better performance, especially, in reducing the cost of the control effort in the case of small attenuation levels. Next, H-infinity tracking performance, which attenuates the influence of both wavelet network approximation error and external disturbances on the modified tracking error, is formulated. Our results indicate that a small attenuation level does not lead to a large control signal. The proposed method insures an optimal trade-off between the amplitude of control signals and the performance of tracking errors. An example is given to illustrate the design efficiency.

Adaptive Output Tracking for Nonlinear Network Control Systems with Time-Delay

The problem of adaptive output tracking is researched for a class of nonlinear network control systems with parameter uncertainties and time-delay. In this paper, a new program is proposed to design a state-feedback controller for this system. For time-delay and parameter uncertainties problems in network control systems, applying the backstepping recursive method, and using Young inequality to process the time-delay term of the systems, a robust adaptive output tracking controller is designed to achieve robust control over a class of nonlinear time-delay network control systems. According to Lyapunov stability theory, Barbalat lemma and Gronwall inequality, it is proved that the designed state feedback controller not only guarantees the state of systems is uniformly bounded, but also ensures the tracking error of the systems converges to a small neighborhood of the origin. Finally, a simulation example for nonlinear network control systems with parameter uncertainties and time-delay is given to illustrate the robust effectiveness of the designed state-feedback controller.

Target Tracking and Obstacle Avoidance for Multi-agent Networks with Input Constraints

In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.

Neural adaptive attitude tracking controller for flexible spacecraft

In this paper,a neural network adaptive controller is proposed for attitude tracking of flexible spacecraft in presence of unknown inertial matrix and external disturbance.In this approach,neural network technique is employed to approximate the unknown system dynamics with finite combinations of some basis functions,and a robust controller is also designed to attenuate the effect of approximation error,more specially,the knowledge of angular velocity is not required.In the closed-loop system,Lyapunov stability analysis shows that the attitude trajectories asymptotically follow the reference output trajectories.Finally,simulation results are presented for the attitude tracking of a flexible spacecraft to show the excellent performance of the proposed controller and illustrate its robustness in face of external disturbances and unknown dynamics.

智慧高速铁路运行控制系统发展趋势综述

在保障安全的前提下,高速铁路运行控制系统进一步结合现代感知、控制、通信以及人工智能技术实现智慧化,是未来高速铁路发展的前沿方向。分析目前高速铁路运营中仍存在异物侵限造成安全事故、突发事件导致列车延误、运力与运量不匹配等问题,提出高速铁路运行控制系统的智能化、智慧化是解决上述问题的重要手段,是保持和提升我国高速铁路技术整体竞争力的核心组成部分。新时期的智慧高速铁路运行控制系统以实现列车(群)全天候全无人自主追踪运行控制为目标,主要包括移动闭塞、列车运行净空感知、列车状态智能监测、列车自主追踪、智能调度指挥、车-地动态自组网通信等关键技术。结合新一代使能技术,研究高速铁路运行控制智能技术的应用及其发展趋势,对我国智慧高速铁路的发展具有参考价值。

基于自主漂移的自动驾驶车辆极限工况轨迹规划与控制

为兼顾自动驾驶车辆在极限工况下的稳定性与轨迹跟踪性能,提出了一种基于自主漂移的自动驾驶车辆轨迹规划与控制方法。基于神经网络设计了神经网络轮胎动力学模型,提升了传统魔术轮胎公式的精度。为进一步拓展自动驾驶车辆极限工况下的稳定边界,基于漂移时轮胎饱和及最大侧滑特性结合质心侧偏角-横摆角速度相平面约束设计了漂移稳定边界,采用非线性模型预测控制(NMPC)在更大稳定范围内规划了安全漂移轨迹,并对规划轨迹进行了漂移跟踪控制。Simulink/CarSim联合仿真结果表明,该方法可充分利用漂移运动优势,在极限工况下确保车辆不发生失控,同时准确跟踪期望轨迹。

低附着路况条件下车辆横向稳定性控制

针对在冰雪环境下车辆横向稳定控制,为解决在低附着、分布不均的路面情况下车辆对参考轨迹的稳定跟踪问题,设计了基于神经网络调节的模糊PID(Proportional-Integral-Differential)制器,以及基于线性化车辆模型的模型预测控制(MPC:Model Predictive Controll)。以路面附着系数及车辆速度作为输入构建BP(Back-Propagation)神经网络,输出调节系数优化模糊PID控制器控制性能;设计了十自由度模型表征车辆在冰雪环境下的动力学特性,使用MPC实现车辆横向稳定控制。使用CarSim/Simulink进行联合仿真实验,结果表明该控制器能显著提高车辆轨迹跟踪性能。

基于自适应RBF神经网络具有模型不确定性的四旋翼无人机指定时间预设性能控制方法

四旋翼无人机具有强耦合和欠驱动的特点,在飞行过程中很容易受到外界干扰,进而影响整个无人机系统的稳定性和精度。为此,提出了一种基于RBF神经网络的指定时间预设性能约束控制策略。首先,针对四旋翼无人机的不确定数学模型难以精确建立,并且在执行任务过程中存在外部未知扰动问题,提出了一种基于指定时间预设性能控制方法,将四旋翼无人机的轨迹跟踪问题转换为对位置子系统和姿态子系统的期望指令跟踪问题;其次,在设计控制器过程中,为了解决“微分爆炸”问题产生的滤波器误差,引入一种新型滤波误差补偿方法,通过RBF神经网络逼近外部未知扰动,并将预测结果补偿给控制器以提高轨迹跟踪的鲁棒性。最后,应用仿真模拟方法验证无人机控制系统稳定性和性能优势,通过飞行试验验证,微风聚拢环境下实际飞行轨迹与仿真模拟结果趋于一致,自主轨迹跟踪起降位置偏差小于1 cm,证明了所提出算法的有效性。

汽车EPS伺服变幅加载系统PID建立DNN控制优化

为了进一步提高汽车电动助力转向电液伺服加载系统运动精度,设计了一种PID建立DNN控制系统。通过柔性联接的模式构建电液位置伺服系统,确保在瞬态平衡响应过程中快速完成控制。搭建了柔性电液位置伺服调控测试,并开展调控效果分析。研究结果表明:通过PID建立的DNN控制器在校正柔性联接电液位置伺服参数方面发挥了关键作用,整体响应时间大幅缩短,确保系统频段动态控制效果获得明显改善。采用DNN控制器调节柔性连接电液伺服系统时,能够有效增强系统低频负载位移精度,避免发生幅值衰减的问题,能够以更高精度跟踪柔性连接电液位置伺服参数,促进了系统跟踪性能获得明显改善。该研究对提高汽车运行稳定性以及节能具有很好的理论支撑作用。

Switching Control Method for Optimal State Feedback Controller of Nuclear Reactor Power System

Due to the nonlinearity of the reactor power system, the load tracking situation is closely related to the initial steady-state power and the final steady-state power after the introduction of the state feedback controller. Therefore, when the initial power and the final stable power are determined, the particle swarm optimization algorithm is used to find the optimal controller parameters to minimize the load tracking error. Since there are many combinations of initial stable power and final stable power, it is not possible to find the optimal controller parameters for all combinations, so the neural network is used to take the final stable power and the initial stable power as input, and the optimal controller parameters as the output. This method obtains the optimal state feedback controller switching control method can achieve a very excellent load tracking effect in the case of continuous power change, in the power change time point, the response is fast, in the controller parameter switching time point, the actual power does not fluctuate due to the change of controller parameters. .

基于漏斗函数双电机伺服系统跟踪与同步控制

针对双电机驱动伺服系统具有未知非线性的问题,提出一种基于漏斗函数的跟踪与同步控制方案。首先,利用神经网络逼近和补偿复杂的非线性,在此基础上,引入滤波技术解决传统反步控制的“计算爆炸”问题,同时引入非光滑漏斗误差面确保系统的状态量被约束在预定义的漏斗边界内,结合改进的漏斗函数和反步设计技术设计了一种自适应量化漏斗跟踪控制方案。为了同时保证双电机的同步运行,同步控制器采用了平均偏差耦合策略,实现了双电机伺服系统的跟踪与同步控制。仿真结果表明,该方法可以实现对负载的跟踪以及双电机的同步。

基于机械臂运动轨迹控制双生成对抗网络的设计

为了提高机械臂的运动准确性,以改善机械臂的工作效率,设计了一种用于机械臂运动轨迹控制的双生成对抗网络。首先,对机械臂的结构进行了分析,根据机械臂的结构特点建立了机械臂的坐标系,在该坐标系上,通过机械臂各关节点的变换矩阵,求取了机械臂的运动学方程;然后,将机械臂末端执行器的位置向量、关节速度等信息与编码器相结合,以获取双向生成性对抗网络,并在双向生成性对抗网络的基础上,设计了双生成对抗网络,以对机械臂的运动轨迹进行控制;最后,利用所提方法与干扰观测器方法对机械臂的运动轨迹进行控制测试。测试结果显示,所提方法的控制准确度比干扰观测器方法的控制准确度提高了40.13%,而且所提方法控制机械臂运动过程中,出现的波动也比干扰观测器方法的控制过程要小。说明所提方法不仅能较为准确地控制机械臂运动,而且控制过程较为平稳,有助于提高机械臂的工作效率。

基于事件触发的预设时间航天器轨迹跟踪控制

针对通信受限条件下的航天器轨迹跟踪问题,提出了一种基于事件触发的预设时间控制算法。为了满足不同任务场景下对航天器快速性的要求,本文设计了一种预设时间轨迹跟踪控制算法。结合反步控制理论及预设时间动态增益,最终实现轨迹跟踪系统的预设时间收敛。作为一种典型的网络化控制系统,航天器在执行任务过程中可能会受到自身通信能力的限制,造成通信阻塞、时延和丢包等问题。为此,本文提出了一种基于动态参数的事件触发算法,在保证系统控制精度的同时,一定程度上避免了控制中心与航天器本体间不必要的通信资源消耗。相比于传统的静态事件触发算法,文中基于动态参数的事件触发算法在减少通信频率方面更具优势。通过理论分析和仿真实验验证所提控制算法的有效性。

考虑多关节时滞的液压挖掘机轨迹跟踪研究

为了减小多关节系统的时滞特性对液压挖掘机工作装置轨迹跟踪精度的影响,本文设计了考虑时滞的长短期记忆-广义回归级联网络和一种基于该网络模型的多关节控制系统。利用几何法分析闭式链运动状态,建立以摇臂关节为末端反馈关节的含闭式链的工作装置运动学逆解,通过4-3-3-3-4多项式轨迹规划算法在关节空间上生成轨迹。结合LSTM的时序处理能力和GRNN的强非线性映射能力,以比例阀信号和关节转角的各阶延迟以及目标转角作为网络输入特征,通过多关节联动辨识建立起输出信号和输入特征的映射关系模型,将关节模型作为逆控制器实现多关节轨迹跟踪。经AMESim-Simulink仿真验证:在空载或变载荷作用下,与不考虑时滞的LSTM系统比,考虑时滞的多关节控制系统能更准确、更迅速、更平滑地跟踪规划轨迹,误差小,并且系统具有较强鲁棒性。