Robust adaptive control for greenhouse climate using neural networks

This paper presents a general framework for robust adaptive neural network (NN)‐based feedback linearization controller design for greenhouse climate system. The controller is based on the well‐known feedback linearization, combined with radial basis functions NNs, which allows the feedback linearization technique to be used in an adaptive way. In addition, a robust sliding mode control is incorporated to deal with the bounded disturbances and the approximation errors of NNs. As a result, an inherently nonlinear robust adaptive control law is obtained, which not only provides fast and accurate tracking of varying set‐points, but also guarantees asymptotic tracking even if there are inherent approximation errors. Copyright © 2010 John Wiley & Sons, Ltd.     

基于遗传算法学习的复合神经网络自适应温度控制系统

针对一类温度控制系统中存在的非线性和参数不确定等问题,提出一种复合神经网络自适应控制结构.在控制系统中构造了神经网络正模型来再现被控对象的动态特性,用神经网络控制器实现优化控制律的非线性映射.文中选用了被控对象80组历史数据作为样本集,并利用遗传算法的全局搜索能力及高效率来训练多层前向神经网络的权系数.最后用升降温工艺曲线作为输入对温度控制系统进行仿真.仿真结果表明,应用遗传算法能够提高神经网络的学习效率.保证神经网络全局快速收敛,从而克服了传统的误差反传学习算法的一些缺点.证明了采用这种神经网络自适应控制结构.使神经网络控制器的输出可以适应对象参数和环境的变化.使温度控制系统具有很好的学习和自适应控制能力,取得了良好的控制效果.     

非线性系统神经网络自适应控制的发展现状及展望

简要回顾了神经网络控制及其应用的发展历程,重点论述了人们在连续、离散时间非线性系统的神经网络以及神经模糊稳定自适应控制研究方面所取得的主要进展,探讨了神经网络自适应控制研究方面存在的主要问题及解决问题的基本途径.作为当前解决神经网络自适应控制问题的途径之一,介绍了近来人们对二阶模糊神经网络以及量子神经网络的研究.最后,总结并指出了这一领域下一步的发展方向和有待解决的新课题.     

The adaptive control using BP neural networks for a nonlinear servo-motor

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification （NNI） and control （NNC）, respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.     

采用自适应自回归小波神经网络的单步预测控制

针对非线性系统的控制问题,提出一种基于神经网络辨识的单步预测控制算法。算法在自回归小波神经网络的基础上,利用混沌机制消除了神经网络易陷入局部极值的缺点．采用自适应性学习率,提高神经网络的收敛能力和速度．以该神经网络为预测模型,引入输出反馈和偏差校正克服预测误差,以此构造一步加权预测控制性能指标。然后采用Brent一维搜索方法求取控制律,Brent法无需任何相关的导数信息,需调整的参数少,使得Brent法适合实时控制．仿真研究说明了该非线性预测控制器的有效性。     

Dissipative-based adaptive neural control for nonlinear systems

A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.     

分数阶细胞神经网络自适应同步控制设计及电路仿真

构建一个新的分数阶细胞神经网络系统,设计驱动系统非线性参数已知而响应系统非线性参数值未知的驱动–响应系统,运用自适应同步控制器及参数自适应调整律实现该驱动–响应系统同步.数值仿真和动力学分析结果表明新的分数阶细胞神经网络系统具有混沌特性.结合分数阶电路理论设计出新的分数阶细胞神经网络系统同步控制的电路原理图.本方案实际可实现4096种多元组合电路,为简洁起见,选取分数阶qi(i=1,2,3)相同值(即q1=q2=q3=0.95)的组合电路进行电路仿真.仿真结果表明,多元电路仿真和数值仿真实验结果具有很高的吻合度.从而证实了该自适应同步控制方法在物理上的可实现性,在工程领域中具有现实的应用价值.     

Robust adaptive control of uncertain non-linear systems using neural networks

This paper presents a robust adaptive output feedback control design method for uncertain non-affine non-linear systems, which does not rely on state estimation. The approach is applicable to systems with unknown but bounded dimensions and with known relative degree. A neural network is employed to approximate the unknown modelling error. In fact, a neural network is considered to approximate and adaptively make ineffective unknown plant non-linearities. An adaptive law for the weights in the hidden layer and the output layer of the neural network are also established so that the entire closed-loop system is stable in the sense of Lyapunov. Moreover, the robustness of the system against the approximation error of neural network is achieved with the aid of an additional adaptive robustifying control term. In addition, the tracking error is guaranteed to be uniformly and asymptotically stable, rather than uniformly ultimately bounded, by using this additional control term. The proposed control algorithm is relatively straightforward and no restrictive conditions on the design parameters for achieving the systems stability are required. The effectiveness of the proposed scheme is shown through simulations of a non-affine non-linear system with unmodelled dynamics, and is compared with a second-sliding mode controller.     

Nonlinear multivariable adaptive control using multiple models and neural networks

In this paper, a multivariable adaptive control approach is proposed for a class of unknown nonlinear multivariable discrete-time dynamical systems. By introducing a k-difference operator, the nonlinear terms of the system are not required to be globally bounded. The proposed adaptive control scheme is composed of a linear adaptive controller, a neural-network-based nonlinear adaptive controller and a switching mechanism. The linear controller can assure boundedness of the input and output signals, and the neural network nonlinear controller can improve performance of the system. By using the switching scheme between the linear and nonlinear controllers, it is demonstrated that improved performance and stability can be achieved simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.     

A review of neural networks for statistical process control

This paper aims to take stock of the recent research literature on application of Neural Networks (NNs) to the analysis of Shewhart's traditional Statistical Process Control (SPC) charts. First appearing in the late 1980s, most of the literature claims success, great or small, in applying NNs for SPC (NNSPC). These efforts are viewed in this paper as useful steps towards automatic on-line SPC for continuous improvement of quality and for real-time manufacturing process control. A standard NN approach that can parallel the universality of the traditional Shewhart charts has not yet been developed or adopted, although knowledge in this area is rapidly increasing. This paper attempts to provide a practical insight into the issues involved in application of NNs to SPC with the hope of advancing the use of NN techniques and facilitating their adoption as a new and useful aspect of SPC. First, a brief review of control chart analysis prior to the introduction of NN technology is presented. This is followed by an examination and classification of the NNSPC existing literature. Next, an extensive discussion of implementation issues with reference to significant research papers is presented. Finally, after summarising the survey, a set of general guidelines for future applications of NNs to SPC is outlined.     

Robot discrete adaptive control based on dynamic inversion using dynamical neural networks

A stable discrete time adaptive control approach using dynamic neural networks (DNNs) is developed in this paper for the trajectory tracking of a robotic manipulator with unknown nonlinear dynamics. By using dynamic inversion constructed by a DNN, the assumption under which the system state should be on a compact set can be removed. This assumption is usually required in neuro-adaptive control. The NN-based variable structure control is designed to guarantee the stability and improve the dynamic performance of the closed-loop system. The proposed control scheme ensures the global stability and desired tracking as well.     

A neural network based adaptive robot controller

Neural network based adaptive controllers have been shown to achieve much improved accuracy compared with traditional adaptive controllers when applied to trajectory tracking in robot manipulators. This paper describes a new Recursive Prediction Error technique for estimating network parameters which is more computationally efficient. Results show that this neural controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.     

Robust adaptive control for a class of uncertain non-affine nonlinear systems using affine-type neural networks

A robust adaptive control is proposed for a class of single-input single-output non-affine nonlinear systems. In order to approximate the unknown nonlinear function, a novel affine-type neural network is used, and then to compensate the approximation error and external disturbance a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proved that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given out based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method.     

Engineering applications of neural networks

This paper describes several prototypical applications of neural network technology to engineering problems. The applications were developed by the authors as part of a graduate-level course taught at the University of Illinois at Urbana-Champaign by the first author (now at Carnegie Mellon University). The applications are: an adaptive controller for building thermal mass storage; an adaptive controller for a combine harvester; an interpretation system for non-destructive evaluation of masonry walls; a machining feature recognition system for use in process planning; an image classification system for classifying land coverage from satellite or high-altitude images; and a system for designing the pumping strategy for contaminated groundwater remediation. These applications are representative of many of the engineering problems for which neural networks are applicable: adaptive control, feature recognition, and design.     

基于RENN 的柔性关节机械臂自适应动态面控制

针对面贴式永磁同步电机驱动的柔性关节机械臂动力学模型具有非线性、不确定性和未知外部扰动等特点,提出一种自适应动态面控制方法来实现其关节轨迹跟踪控制．控制律由动态面技术得到,降低了反推控制器的复杂性．模型不确定因素由递归Elman神经网络在线补偿,神经网络权值自适应律通过Lyapunov稳定性分析推导得到．仿真研究表明,该方法对于载荷不确定和外界扰动具有较强的鲁棒性,与传统动态面法相比,大大提高了柔性关节的位置跟踪精度．     

Applications of neural networks for coordinate transformations in robotics

The use of artificial neural networks is investigated for application to trajectory control problems in robotics. The relative merits of position versus velocity control is considered and a control scheme is proposed in which neural networks are used as static maps (trained off-line) to compute the inverse of the manipulator Jacobian matrix. A proof of the stability of this approach is offered, assuming bounded errors in the static map. A representative two-link robot is investigated using an artificial neural network which has been trained to compute the components of the inverse of the Jacobian matrix. The controller is implemented in the laboratory and its performance compared to a similar controller with the analytical inverse Jacobian matrix.     

机械臂轨迹跟踪控制--基于EC-RBF神经网络的机械臂模型参考自适应控制

针对机械臂运动轨迹控制中存在的跟踪精度不高的问题,采用了一种基于EC-RBF神经网络的模型参考自适应控制方案对机械臂进行模型辨识与轨迹跟踪控制。该方案采用了两个RBF神经网络,运用EC-RBF学习算法,采用离线与在线相结合的方法来训练神经网络,一个用来实现对机械臂进行模型辨识,一个用来实现对机械臂轨迹跟踪控制。对二自由度机械臂进行仿真,结果表明,使用该控制方案对机械臂进行轨迹跟踪控制具有较高的控制精度,且因采用EC-RBF学习算法使网络具有更快的训练速度,从而使得控制过程较迅速。     

Diagnostic neural adaptive control of drifting systems

A hierarchical network of neural network planning and control is employed to successfully accomplish a task such as grasping in a cluttered real world environment. In order for the individual robot joint controllers to follow their specific reference commands, information is shared with other neural network controllers and planners within the hierarchy. Each joint controller is initialized with weights that will acceptably control given a change in any of several crucial parameters across a broad operating range. When increased accuracy is needed as parameters drift, the diagnostic node fuzzy supervisor interprets the controller network's diagnostic outputs and transitions the weights to a closest fit specificchild controller. Future reference commands are in turn influenced by the diagnostic outputs of every robot joint neural network controller. The neural network controller and diagnostics are demonstrated for linear and nonlinear plants.     

The adaptive control using BP networks for a nonlinear servo-motor

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltageload-torque and environmental operating conditions.So it is rather diffcult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics.A neural network-based adaptive control strategy is proposed in this paper.In this method,two neural networks have been adopted for system identification(NNI)and control(NNC),respectively.Then,the commonly-used specialized learning has been modified,by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information.Moreover,the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability.Finally,an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.