神经网络自适应控制的研究进展及展望

关于人工神经网络与自适应结合的研究，近年来已成为智能控制学科的热点之一。自适应具有强鲁棒性，神经网络则具有自学习功能和良好的容错能力，神经网络自适应控制由于较好地结合了二者的优点而具有强大的优势。本文系统地综述了神经网络自适应控制的进展，讨论了神经网络自适应的主要模型和算法，并就其存在的一些问题、应用与发展趋势进行了探讨。     

Nonlinear model identification and adaptive model predictive control using neural networks

This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.     

On-line lower-order modeling via neural networks

This paper presents a novel method to determine the parameters of a first-order plus dead-time model using neural networks. The outputs of the neural networks are the gain, dominant time constant, and apparent time delay. By combining this algorithm with a conventional PI or PID controller, we also present an adaptive controller which requires very little a priori knowledge about the plant under control. The simplicity of the scheme for real-time control provides a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation and experimental results demonstrate the feasibility and adaptive property of the proposed scheme.     

Adaptive nonlinear control using input normalized neural networks

An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small.     

Adaptive control of nonlinear system using online error minimum neural networks

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly.     

基于神经元网络参数自调整的PID控制器

本文介绍一种基于神经元网络自学习的ＰＩＤ控制器。该控制器不仅具有自学习自适应能力,而且具有自调整比例因子功能。实验表明,该控制器能够改善温度控制系统的动态特性和对环境的鲁棒性     

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.     

压电陶瓷执行器的神经网络实时自适应逆控制

目的：为了提高压电陶瓷执行器执行精度,提出消除压电陶瓷的非线性、非光滑的迟滞特性的方法。 方法：提出了基于内积的压电陶瓷动态神经网络非线性、非光滑的迟滞逆模型,采用反馈误差学习方法,避免了求取压电陶瓷的Jacobian信息,快速地在线得到压电陶瓷的逆模型,并结合PID反馈控制,在dSPACE系统平台上,实现压电陶瓷的神经网络自适应逆控制,为了提高实时性,程序采用效率高、速度快的C-MEX S Function编程。结果：实验结果表明：神经网络自适应逆控制的控制精度为：0.13μm,而PID控制精度为：0.32μm 。结论：所提出方法有效地消除了迟滞的影响,控制精度高。     

基于递归型小波神经网络的感应电动机伺服驱动系统自适应控制

针对感应电动机伺服驱动系统具有的多变、强耦合、慢时变等非线性特性和不确定性扰动,传统的位置速 度PID控制策略不能保证轨迹跟踪的精度和良好的动态品质的问题;保证系统对系统内部参数波动和外界不确定 性扰动具有较好的鲁棒性,在矢量控制策略的基础上,提出了基于递归型小波神经网络的自适应控制方案。神经 网络参数的在线学习机制采用delta自适应律并结合了BP算法和梯度下降法,算法简单,计算量大大减少。仿真 的结果验证了方案的有效性。     

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.     

Distributed adaptive neural networks leader-following formation control for quadrotors with directed switching topologies

The leader-following formation problem is discussed for a team of quadrotors under directed switching topologies. To obtain a more general dynamic model, we describe the quadrotor system in a non-affine pure-feedback form with mismatched unknown nonlinearities. By employing an adaptive neural networks state observer to approximate the unknown nonlinear functions and to reconstruct the immeasurable inner states, we propose a novel distributed output feedback formation control protocol with the backstepping method combining with the dynamic surface control technique. From the Lyapunov stability theorem, all signals in the closed-loop formation system are proven to be cooperatively semiglobally uniformly ultimately bounded for any given bounded initial conditions. Finally, we proved that we verify the performance of the proposed formation control approach by a simulation study.     

Control of chemical processes using neural networks: implementation in a plant for xylose production

This work demonstrates the use of artificial intelligence for control of xylose reactor performance in a paper factory. Two types of neural networks are used, a perceptron for the temperature controller and an adaptive formulation for the noise filter. The results show an improvement in the temperature stabilization time with respect to a classic PID control.     

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes.     

用神经网络实现微型电机的群控

本文提出了采用神经网络实现微型电机群控的方案。给出了群控方案的结构图，通过对ＡＮＮ和ＦＮＮ的学习和训练，使其记忆所控规则的样本，实现快速、准确地完成控制的目的。     

Nonlinear adaptive control for high angle of attack flight

This paper presents a nonlinear methodology for the control of a high angle of attack aircraft, in particular, a modified F-18 aircraft. As a modern combat aircraft demands better maneuverability and performance over domains which include high angles of attack, research in high angle of attack is presently at an advanced stage. An adaptive controller is developed to maneuver an aircraft at a high angle of attack even if the aircraft is required to fly over a highly nonlinear flight regime. The adaptive, controller presented in this paper is based on nonlinear prediction models, and can be constructed to minimize the given cost function or the difference of a described Lyapunov function with respect to the control input at each step. A controller uses system identification parameters to calculate a command signal so that the output of system follows the reference trajectory. The control is calculated to let system follow the reference trajectory under some constraints. This paper shows that nonlinear adaptive control can be utilized effectively to control high performance aircraft such as the F-18 aircraft for rapid maneuvers with large changes in angle of attack.     

Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation

This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme.     

神经网络PID控制器在高精度空调系统中的应用

针对中央空调系统被控对象具有大滞后、慢时变、非线性特点及不确定干扰因素多的实际情况，将具有自学习、自适应功能的神经网络PID控制器应用于高精度空调系统中，通过MATLAB环境下的计算机仿真．证明了其在高精度空调控制中的实用性和有效性。     

A comparison of traditional and adaptive control strategies for systems with time delay

A recently developed tuning method is compared to an adaptive Smith Predictor control strategy. The robustness of each method is considered for time-varying plant parameters. Examples with simulations are provided to compare the methods and present conclusions on the advantages and disadvantages of each.     

智能自适应逆控制在无压式淬火机上的应用

将智能控制方法与自适应逆控制思想相结合用于无压式淬火机的综合自动控制．研究建立了基于智能自适应逆控制的终冷温度预测和控制模型，经现场实际验证表明，预测控制模型具有较高的预测精度和良好的控制效果。     

Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate the unknown nonlinear function. Because it is assumed that the system states are unmeasured, an observer needs to be designed to estimate those unmeasured states. In the previous works with the observer design based on the universal approximator, when the dead-zone input appears it is ignored and the stability of the closed-loop system will be affected. In this paper, the proposed algorithm overcomes the affections of dead-zone input for the stability of the systems. Moreover, the dead-zone parameters are assumed to be unknown and will be adjusted adaptively as well as the sign function being introduced to compensate the dead-zone. With the aid of the Lyapunov analysis method, the stability of the closed-loop system is proven. A simulation example is provided to illustrate the feasibility of the control algorithm presented in this paper.