基于MAS的多UUV编队智能优化控制

针对无人水下航行器(Unmanned Underwater Vehicle,UUV)编队航行的实际背景和限制条件,提出了一种基于多智能体系统(MAS)技术的多UUV编队智能控制方法。利用多Agent之间的交互作用,以灵活便捷的方式进行各UUV之间的协同优化,从而实现多个UUV的自主编队航行。考虑到控制器在编队保持中的重要性,对面向UUV编队航行的控制率和实现算法进行了重点研究,设计了UUV编队控制器,并通过仿真实验验证了该算法的鲁棒性和稳定性,最后对多UUV编队智能控制方法进行了仿真验证和分析。     

Adaptive neural control and learning of affine nonlinear systems

This paper presents deterministic learning from adaptive neural network control of affine nonlinear systems with completely unknown system dynamics. Thanks to the learning capability of radial basis function, neural network (NN), stable adaptive NN controller is designed for the unknown affine nonlinear systems. The designed adaptive NN controller is rigorously shown that learning of the unknown closed-loop system dynamics can be achieved during the stable control process because partial persistent excitation condition of some internal signals in the closed-loop system is satisfied. Subsequently, neural learning controller using the knowledge obtained from deterministic learning is constructed to achieve closed-loop stability and improve control performance. Numerical simulation is provided to show the effectiveness of the proposed control scheme.     

移动USBL测距辅助的UUV协同导航定位方法

针对无人水下航行器(UUV) 导航精度受惯性导航(INS) 影响较大的问题, 本文提出一种基于无人水面船 (USV)携带超短基线(USBL)对UUV进行移动式辅助导航定位的方法. 文中以USV上高精度INS和全球导航卫星系 统(GNSS)组合后的导航结果作为基准, 利用USBL测量得到的USV和UUV相对位置和姿态信息, 结合UUV的INS误 差方程, 建立了UUV协同导航系统的状态方程和观测方程, 并基于自适应卡尔曼滤波方法对UUV状态进行滤波估 计. 仿真和湖上实验结果表明, 文中所提方法可有效提升UUV导航精度.     

Robust adaptive neural sliding mode control of MEMS triaxial gyroscope with angular velocity estimation

In this paper, a robust adaptive sliding mode control strategy of micro electro-mechanical system (MEMS) triaxial gyroscope using radial basis function (RBF) neural network is presented for the system identification of MEMS gyroscope. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network controller is used to learn the unknown upper bound of model uncertainties and external disturbances. The adaptive RBF neural network is incorporated into the adaptive sliding mode control in the Lyapunov sense, and the stability of the proposed adaptive neural sliding mode control can be established. The dynamics and angular velocities of gyroscope can be identified in real time. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme, showing that the designed control system has better robust performance in its insensitivity to system nonlinearities; moreover, system parameters including angular velocity can be consistently estimated and tracking errors converge to zero asymptotically.     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.     

Comparison of RBF and SHL Neural Network Based Adaptive Control

Modern unmanned aerial vehicles (UAVs) are required to perform complex maneuvers while operating in increasingly uncertain environments. To meet these demands and model the system dynamics with a high degree of precision, a control system design known as neural network based model reference adaptive control (MRAC) is employed. There are currently two neural network architectures used by industry and academia as the adaptive element for MRAC; the radial basis function and single hidden layer neural network. While mathematical derivations can identify differences between the two neural networks, there have been no comparative analyses conducted on the performance characteristics for the flight controller to justify the selection of one neural network over the other. While the architecture of both neural networks contain similarities, there are several key distinctions which exhibit a noticeable impact on the control system’s overall performance. In this paper, a detailed comparison of the performance characteristics between both neural network based adaptive control approaches has been conducted in an application highly relevant to UAVs. The results and conclusions drawn from this paper will provide engineers with tangible justification for the selection of the better neural network adaptive element and thus a controller with better performance characteristics.     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

Stable nonlinear adaptive controller for an autonomous underwater vehicle using neural networks

In general, the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions. For this reason, high performance control system for an AUV usually should have the capacities of learning and adaptation to the time-varying dynamics of the vehicle. In this article, we present a robust adaptive nonlinear control scheme for an AUV, where a linearly parameterized neural network (LPNN) is introduced to approximate the uncertainties of the vehicle's dynamics, and the basis function vector of the network is constructed according to the vehicle's physical properties. The proposed control scheme can guarantee that all of the signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.     

Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator’s dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.     

欠驱动无人水下航行器三维轨迹跟踪的反步控制

针对欠驱动无人水下航行器(underactuated unmanned underwater vehicles,UUVs)三维轨迹跟踪控制问题,本文有别于传统反步法中基于视线法设计姿态角误差变量的思路,提出了一种定义虚拟速度误差变量的反步控制器设计方法,能够有效避免传统反步法控制律设计时存在的奇异值问题,简化了传统反步法复杂的计算过程;设计了欠驱动UUV的三维轨迹跟踪控制器,给出了系统的误差方程,基于Lyapunov稳定性理论证明了系统在定常外界扰动下的鲁棒性和稳定性;仿真结果表明本文提出的UUV三维轨迹跟踪反步控制方法收敛、有效,能够实现欠驱动UUV对时变三维轨迹的精确跟踪控制.     

Neural-network-based robust adaptive control for a class of nonlinear systems

In this study, a robust adaptive control (RAC) system is developed for a class of nonlinear systems. The RAC system is comprised of a computation controller and a robust compensator. The computation controller containing a radial basis function (RBF) neural network is the principal controller, and the robust compensator can provide the smooth and chattering-free stability compensation. The RBF neural network is used to approximate the system dynamics, and the adaptive laws are derived to on-line tune the parameters of the neural network so as to achieve favorable estimation performance. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. To investigate the effectiveness of the RAC system, the design methodology is applied to control two nonlinear systems: a wing rock motion system and a Chua’s chaotic circuit system. Simulation results demonstrate that the proposed RAC system can achieve favorable tracking performance with unknown of the system dynamics.     

Adaptive control for uncertain nonlinear systems based on multiple neural networks.

A new adaptive multiple neural network controller (AMNNC) with a supervisory controller for a class of uncertain nonlinear dynamic systems was developed in this paper. The AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks was used to approximate system nonlinearity for the given task. Each neural network represents the system dynamics for each task. For a job where some tasks are repeated but information on the load is not defined and unknown or varying, the proposed controller is effective because of its capability to memorize control skill for each task with each neural network. For a new task, most similar existing control skills may be used as a starting point of adaptation. With the help of a supervisory controller, the resulting closed-loop system is globally stable in the sense that all signals involved are uniformly bounded. Simulation results on a cartpole system for the changing mass of the pole were illustrated to show the effectiveness of the proposed control scheme for the comparison with the conventional adaptive neural network controller (ANNC).     

Adaptive neural control for a class of perturbed strict-feedback nonlinear time-delay systems

This paper proposes a novel adaptive neural control scheme for a class of perturbed strict-feedback nonlinear time-delay systems with unknown virtual control coefficients. Based on the radial basis function neural network online approximation capability, an adaptive neural controller is presented by combining the backstepping approach and Lyapunov-Krasovskii functionals. The proposed controller guarantees the semiglobal boundedness of all the signals in the closed-loop system and contains minimal learning parameters. Finally, three simulation examples are given to demonstrate the effectiveness and applicability of the proposed scheme.     

基于神经网络的无人机姿态自适应控制仿真

针对四旋翼无人机姿态控制中模型不完整、部分参数和扰动不确定的问题,提出了一种基于神经网络的自适应控制方法,采用RBF神经网络对无人机姿态动力学模型中不确定和扰动部分进行学习,设计了以类反步法为基础,包含反馈控制和神经网络控制的自适应控制器,实现了对未知动态的准确逼近,解决了传统控制方法中过于依赖精确模型的问题。同时设计了神经网络的权值自适应律,实现了控制过程中的在线学习和调整,并且通过李雅普诺夫方法证明了闭环系统的稳定性。仿真结果表明,在存在较大扰动的情况下,上述控制器可得到很好的控制效果,可以实现误差的快速收敛,具有较好的鲁棒性和自适应性。     

一类非线性不确定系统的神经网络控制

针对一类非线性不确定系统,提出了一种自适 应神经网络控制方案．被控系统是部分已知的,其中系统已知的动态特性被用来设计保证标 称模型稳定的反馈控制器,而基于神经网络的动态补偿器则用于补偿系统的非线性不确定性 ,从而可以保证系统输出跟踪误差渐近收敛于0．     

不确定机器人的自适应神经网络控制与学习

针对具有未知动态的电驱动机器人,研究其自适应神经网络控制与学习问题.首先,设计了稳定的自适应神经网络控制器,径向基函数(RBF)神经网络被用来逼近电驱动机器人的未知闭环系统动态,并根据李雅普诺夫稳定性理论推导了神经网络权值更新律.在对回归轨迹实现跟踪控制的过程中,闭环系统内部信号的部分持续激励(PE)条件得到满足.随着PE条件的满足,设计的自适应神经网络控制器被证明在稳定的跟踪控制过程中实现了电驱动机器人未知闭环系统动态的准确逼近.接着,使用学过的知识设计了新颖的学习控制器,实现了闭环系统稳定、改进了控制性能.最后,通过数字仿真验证了所提控制方法的正确性和有效性.     

Acoustic motion estimation and guidance for unmanned underwater vehicles

The problem of navigation, guidance and control of Unmanned Underwater Vehicles (UUVs) is addressed in this paper. A task-function based guidance system and an acoustic motion estimation module have been integrated with a conventional UUV autopilot within a two-layered hierarchical architecture for closed-loop control. The design of the guidance system is based on suitable Lyapunov functions that can handle the different manoeuvres involved in approaching a target. Range and bearing information provided by a pencil beam profiling sonar are processed by an Extended Kalman Filter based algorithm for motion estimation in a structured environment. The resulting Navigation Guidance and Control (NGC) system has been tested on Roby2, the UUV testbed developed at the Istituto Automazione Navale of Italy's National Research Council. The experimental set-up as well as modalities and results are discussed.     

Observer-based robust optimal control for helicopter with uncertainties and disturbances

Unknown model uncertainties and external disturbances widely exist in helicopter dynamics and bring adverse effects on control performance. Optimal control techniques have been extensively studied for helicopters, but these methods cannot effectively handle flight control problems since they are sensitive to uncertainties and disturbances. This paper proposes an observer-based robust optimal control scheme that enables a helicopter to fly optimally and reduce the influence of unknown model uncertainties and external disturbances. A control Lyapunov function (CLF) is firstly constructed using the backstepping method, then Sontag's formula is utilized to design an inverse optimal controller to stabilize the nominal system. Furthermore, it is stressed that the radial basis function (RBF) neural network is introduced to establish an observer with adaptive laws, approximating and compensating for the unknown model uncertainties and external disturbances to enhance the robustness of the closed-loop system. The uniform ultimate boundedness of the closed-loop system is ensured using the presented control approach via Lyapunov stability analysis. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control strategy.     

An approach to adaptive neural control of robot manipulators

An adaptive neural control scheme for mechanical manipulators is presented. The neural design has been developed basically following adaptive control design principles and taking into account a number of properties that adaptive schemes and neural controllers have in common. The control loop essentially consists of a neural network for learning the robot's inverse dynamics and online generation of the control signal. Some simulation results are provided to evaluate the design.