基于分散神经网络的多指灵巧手位置控制

针对多指灵巧手钢缆传动系统的非线性，提出一种基于分散神经网络的位置控制方法．通过 对复杂的钢缆传动系统施加不同的输入可以得到特定的相对简单的输入输出数据，利用这种 特定的输入输出数据学习传动系统的非线性关系得到多个分散的神经网络，再根据传动系统 的结构特性用分散的神经网络求取钢缆传动系统的逆模型，用于直接逆控制，从而达到补偿 非线性误差的目的．同时应用在线神经网络的适时补偿使系统长时间保持良好的运行状态． 实验证明这种方法可大大提高位置跟踪精度，取得比较满意的结果．     

级联RTAC系统动态神经网络辨识与分散镇定控制

针对含不确定关联项的级联RTAC系统的镇定控制问题, 提出了一种基于动态神经网络辨识的分散控制方 案. 应用拉格朗日方程建立起了考虑不确定非线性作用力的级联RTAC系统数学模型, 采用动态神经网络实现级 联RTAC系统中不确定关联项的在线辨识, 通过构造含神经网络权值矩阵迹的Lyapunov函数, 证明了辨识误差的一 致有界性. 通过动态神经网络辨识不确定关联项、补偿系统建模误差, 建立级联RTAC系统分层滑模控制算法, 以实 现级联RTAC系统的高精度分散镇定控制. 数值仿真验证了动态神经网络的引入对级联RTAC系统分散镇定控制系 统瞬态幅值抑制、稳态精度提升的效果.     

Decentralized adaptive controller design of large-scale uncertain robotic systems

In this paper, we develop a decentralized neural network control design for robotic systems. Using this design, it is not necessary to derive the robotic dynamical system (robotic model) for the control of each of the robotic components, as in traditional robot control. The advantage of the proposed neural network controller is that, under a mild assumption, unknown nonlinear dynamics such as inertia matrix and Coriolis/centripetal matrix and friction, as well as interconnections with arbitrary nonlinear bounds can be accommodated with on-line learning.     

A neural network model predictive controller

A neural network controller is applied to the optimal model predictive control of constrained nonlinear systems. The control law is represented by a neural network function approximator, which is trained to minimize a control-relevant cost function. The proposed procedure can be applied to construct controllers with arbitrary structures, such as optimal reduced-order controllers and decentralized controllers.     

Decentralized adaptive neural network control of cascaded DC–DC converters with high voltage conversion ratio

Decentralized output voltage tracking of cascaded DC–DC converters is an interesting topic to obtain a high voltage conversion ratio. The control purpose is challenging due to the load resistance changes, renewable energy supply voltage variations and interaction of the individual converters. In this paper, four novel decentralized adaptive neural network controllers are designed on the cascaded DC–DC buck and boost converters under load and DC supply voltage uncertainties. In the beginning, individual buck and boost converter average models that can operate in both continuous and discontinuous conduction modes are derived. Then, the interconnected and decentralized state-space models of cascaded buck and boost converters are extracted. These models are highly nonlinear with unknown uncertainties which can be estimated by neural networks. Further, two decentralized adaptive backstepping neural network voltage controllers are proposed on cascaded buck converters to deal with uncertainties and interactions. However, these control strategies are not applicable to a boost converter due to its non-minimum phase nature. Then, two novel decentralized adaptive neural network with a conventional proportional–integral reference current generator are developed on the cascaded boost converters. Practical stability of the overall system is guaranteed for the proposed controllers using Lyapunov stability theorem. Finally, four control strategies provide good quality of output voltage in the presence of uncertainties and interactions. Comparative simulations are carried out on cascaded buck and boost converters to validate the effectiveness and performance of the designed methods.     

Decentralized adaptive recurrent neural control structure

This paper presents a novel decentralized variable structure neural control approach for large-scale uncertain systems, which is developed using recurrent high-order neural networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnection terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate online the dynamical behavior of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well-known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of the proposed decentralized identification and control algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum.     

Coordinated decentralized adaptive output feedback control of interconnected systems

A decentralized adaptive output feedback control design is proposed for large-scale interconnected systems. It is assumed that all the controllers share prior information about the system reference models. Based on that information, a linearly parameterized neural network is introduced for each subsystem to partially cancel the effect of the interconnections on tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.     

非线性时滞大系统自适应神经网络分散控制

针对一类未知非线性时滞关联大系统,提出一种自适应神经网络分散跟踪控制方案．采用神经网络逼近各子系统内部的非线性函数和关联项中的时滞非线性函数;利用占有方法处理时滞项,采用Backstepping技术设计分散控制律和参数自适应律．基于Lyapunov-Krasoviskii泛函证明了闭环大系统所有信号半全局一致最终有界．通过调节设计参数和增加神经元个数,可以实现任意输出跟踪精度．实例仿真说明了该方案的可行性。     

Stable decentralized adaptive control design of robot manipulators using neural network approximations

In this paper, we present a decentralized neural network (NN) adaptive technique for control of robot manipulators in the presence of unknown non-linear functions. Radial basis function NNs are used to approximate the non-linear functions to include the case of both parametric and dynamic uncertainty in each subsystem. The robustifying terms are added to the controllers to overcome the effects of the interconnections. The stability can be guaranteed by using a rigid proof. Finally, simulation is given to illustrate the effectiveness of the proposed algorithm.     

Neural network control for position tracking of a two-axis inverted pendulum system: experimental studies.

In this paper, experimental studies of a decentralized neural network control scheme of the reference compensation technique applied to control a 2-degrees-of-freedom (2-DOF) inverted pendulum on an x - y plane are presented. Each axis is controlled by two separate neural network controllers to have a decoupled control structure. Neural network controllers are applied not only to balance the angle of pendulum, but also to control the position tracking of the cart. The decoupled control structure can compensate for uncertainties and cancel coupling effects. Especially, a circular trajectory tracking task is tested for position tracking control of the cart while maintaining the angle of the pendulum. Experimental result shows that position control of the inverted pendulum and cart is successful.     

考虑多传感器故障的可重构机械臂主动取代分散容错控制

针对可重构机械臂系统位置传感器和速度传感器多故障, 提出一种主动取代分散容错控制方法. 基于可重构机械臂的模块化属性, 设计正常工作模式下的分散神经网络控制器. 利用微分同胚原理将子系统结构进行非线性变换, 将传感器故障转化成伪执行器故障, 设计分散滑模观测器以对多传感器故障进行实时检测, 并利用其输出信号取代故障传感器信号, 实现了多传感器故障情形下可重构机械臂的主动容错控制. 仿真结果表明了所设计的容错控制方法的有效性.     

Decentralized adaptive output feedback control via input/output inversion

A decentralized adaptive output feedback control design method is presented for control of large-scale interconnected systems. It is assumed that all the controllers share prior information about the subsystem reference models. Based on that information, a linear dynamic output feedback compensator and linearly parameterized neural network (NN) are introduced for each subsystem to partially cancel the effect of the interconnections on the tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.     

Decentralized discrete-time neural control for a Quanser 2-DOF helicopter

Control design for helicopters is a complicated and challenging problem due to the strong inter-couplings and nonlinear uncertainties in the system model. This paper deals with the decentralized control problem for the output trajectory tracking in a Quanser 2 degree of freedom (DOF) helicopter. High order neural network (HONN) is an important technique to approximate non-linearities in the model. Two different discrete-time schemes with a decentralized structure are used. Neural backstepping and neural sliding mode block control techniques are considered in order to control pitch and yaw positions. On one hand, backstepping control divides the whole system into two subsystems which are used to track the pitch and yaw references respectively. Real and virtual controls are approximated by HONNs. On the other hand, block control technique is applied to HONNs which can identify the system helicopter model. Each discrete-time high order neural network is trained on-line with an extended Kalman filter based algorithm. Without the previous knowledge of the plant parameters neither its model, we show via simulations the good performance of both strategies. The block control technique presents slightly better results than backstepping algorithm.     

全局稳定的分散自适应神经网络反推跟踪控制

针对一类不确定大规模系统,研究其全局稳定的分散自适应神经网络反推跟踪控制问题.在假设不匹配的未知关联项满足部分已知的非线性Lipschitz条件下,采用神经网络作为前馈补偿器,逼近参考信号作为输入的未知关联函数;设计者可根据参考信号的界预先确定神经网络逼近域,同时保证了闭环系统的全局稳定性.仿真实例验证了控制算法的有效性.     

Real‐Time Results for High Order Neural Identification and Block Control Transformation Form Using High Order Sliding Modes

In this paper, real‐time results for a novel continuous‐time adaptive tracking controller algorithm for nonlinear multiple input multiple output systems are presented. The control algorithm includes the combination of a recurrent high order neural network with block control transformation using a high order sliding modes technique as control law. A neural network is used to identify the dynamic plant behavior where a filtered error algorithm is used to train the neural identifier. A decentralized high order sliding mode, named the twisting algorithm, is used to design chattering‐reduced independent controllers to solve the trajectory tracking problem for a robot arm with three degrees of freedom. Stability analyses are given via a Lyapunov approach.     

Decentralized adaptive control of nonlinear systems using radialbasis neural networks

Stable direct and indirect decentralized adaptive radial basis neural network controllers are presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Due to the functional approximation capabilities of radial basis neural networks, the dynamics for each subsystem are not required to be linear in a set of unknown coefficients as is typically required in decentralized adaptive schemes. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds     

Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks

This paper introduces a new decentralized adaptive neural network controller for a class of large-scale nonlinear systems with unknown non-affine subsystems and unknown interconnections represented by nonlinear functions. A radial basis function neural network is used to represent the controller’s structure. The stability of the closed loop system is guaranteed through Lyapunov stability analysis. The effectiveness of the proposed decentralized adaptive controller is illustrated by considering two nonlinear systems: a two-inverted pendulum and a turbo generator. The simulation results verify the merits of the proposed controller.     

Decentralized backstepping output-feedback control for stochastic interconnected systems with time-varying delays using neural networks

This paper addresses the decentralized adaptive output-feedback control problem for a class of interconnected stochastic strict-feedback uncertain systems described by It $\hat{\hbox{o}}$ differential equation using neural networks. Compared with the existing literature, this paper removes the commonly used assumption that the interconnections are bounded by known functions multiplying unknown parameters, and all unknown interconnections are lumped in a suitable function which is compensated by only a neural network in each subsystem. So, the controller is simpler even than that for the strict-feedback systems described by the ordinary differential equation. Moreover, the circle criterion is applied to designing nonlinear observers for the estimates of system states. A simulation example is used to illustrate the effectiveness of control scheme proposed in this paper.     

非线性互联系统的分散混合自适应智能控制

针对一类高阶互联MIMO非线性系统，利用TS模糊系统和神经网络的通用逼近能力，在神经网络控制器中引入模糊基函数，提出一种分散混合自适应智能控制器设计的新方案．基于等价控制思想，设计分散自适应控制器，无需计算TS模型．通过对不确定项进行自适应估计，取消了其存在已知上界的假设．通过理论分析，证明了闭环智能控制系统所有信号有界，跟踪误差收敛到零．     

Performance modeling of load-balancing algorithms using neural networks

The paper presents a new approach that uses neural networks to predict the performance of a number of dynamic decentralized load-balancing strategies. A distributed multicomputer system using distributed load-balancing strategies is represented by a unified analytical queuing model. A large simulation data set is used to train a neural network using the back-propagation learning algorithm based on gradient descent The performance model using the predicted data from the neural network produces the average response time of various load balancing algorithms under various system parameters. The validation and comparison with simulation data show that the neural network is very effective in predicting the performance of dynamic load-balancing algorithms. Our work leads to interesting techniques for designing load balancing schemes (for large distributed systems) that are computationally very expensive to simulate. One of the important findings is that performance is affected least by the number of nodes, and most by the number of links at each node in a large distributed system.