Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory

We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that the neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. It is well known that chaotic and unstable behavior may occur for nonlinear systems. Normally, we will need to control this type of behavior to avoid structural problems with the system. We illustrate in this paper our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the aircraft dynamic system.     

基于偏格式线性化的MIMO系统无模型自适应控制

针对常规PID控制器不能很好兼顾抗干扰性与鲁棒性以及基于模型的控制算法过于依赖受控系统数学模型的缺点,提出一种适用于离散时间多输入多输出(MIMO)非线性系统的无模型自适应控制算法.该算法首先通过偏格式线性化方法将非线性系统线性化,再利用一种新型的投影算法在线辨识受控系统参数,根据辨识得到的受控系统参数直接递推计算无模...     

二维直线电机的并联型无模型自适应复合控制

针对二维直线电机平台系统的XY轴协同控制问题,将PI与并联型无模型自适应相结合的复合控制方法应用于控制系统。该控制方法通过在每个子系统加入PI控制方法保证其稳定性,再通过并联型的无模型自适应控制方法来提高整个系统的跟踪性能,以减小系统位置误差。基于数据驱动的无模型自适应控制方法设计二维直线电机系统控制器,其优势在于无需被控系统精确数学模型,仅根据被控系统输入输出数据对系统进行控制。相比基于模型的控制方法,该方法能够减小未建模动态产生的干扰,有效提高控制精度。仿真及实物控制效果对比结果表明,这种复合控制方法明显提高了整个系统的稳定性和位置跟踪精度。     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

Decoupled control using neural network-based sliding-mode controller for nonlinear systems

In this paper, an adaptive neural network sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. The adaptive neural sliding-mode control system is comprised of neural network (NN) and a compensation controller. The NN is the main regulator controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the neural controller. An adaptive methodology is derived to update weight parts of the NN. Using this approach, the response of system will converge faster than that of previous reports. The simulation results for the cart–pole systems and the ball–beam system are presented to demonstrate the effectiveness and robustness of the method. In addition, the experimental results for seesaw system are given to assure the robustness and stability of system.     

Adaptive Neural Dynamic Surface Control For Nonlinear Pure‐Feedback Systems With Multiple Time‐Varying Delays: A Lyapunov‐Razumikhin Method

This paper focuses on the problem of adaptive neural control for a class of uncertain nonlinear pure‐feedback systems with multiple unknown time‐varying delays. The considered problem is challenging due to the non‐affine pure‐feedback form and the unknown system functions with multiple unknown time‐varying delays. Based on a novel combination of mean value theorem, Razumikhin functional method, dynamic surface control (DSC) technique and neural network (NN) parameterization, a new adaptive neural controller which contains only one parameter is developed for such systems. Moreover, The DSC technique can overcome the problem of ‘explosion of complexity’ in the traditional backstepping design procedure. All closed‐loop signals are shown to be semi‐globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Two simulation examples are given to verify the effectiveness of the proposed design.     

Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics

This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an $n$-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi–Sugeno (T–S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an $n$-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional–differential control, fuzzy-model-based control, T–S-type FNN control, and robust neural fuzzy network control systems.      

An ISS-modular approach for adaptive neural control of pure-feedback systems

Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback system using radial basis function (RBF) neural networks (NN). An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem. The difficulty in controlling the non-affine pure-feedback system is overcome by achieving the so-called “ISS-modularity” of the controller-estimator. Specifically, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors, and a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. The stability of the entire closed-loop system is guaranteed by the small-gain theorem. The ISS-modular approach provides an effective way for controlling non-affine non-linear systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach.     

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.      

Adaptive neural control with fast approximation for uncertain nonlinear systems: A novel composite learning approach

In existing adaptive neural control approaches, only when the regressor satisfies the persistent excitation (PE) or interval excitation (IE) conditions, the constant optimal weights of neural network (NN) can be identified, which can be used to establish uncertainties in nonlinear systems. This paper proposes a novel composite learning approach based on adaptive neural control. The focus of this approach is to make the NN approximate uncertainties in nonlinear systems quickly and accurately without identifying the constant optimal weights of the NN. Hence, the regressor does not need to satisfy the PE or IE conditions. In this paper, regressor filtering scheme is adopted to generate prediction error, and then the prediction error and tracking error simultaneously drive the update of NN weights. Under the framework of Lyapulov theory, the proposed composite learning approach can ensure that approximation error of the uncertainty and tracking error of the system states converge to an arbitrarily small neighborhood of zero exponentially. The simulation results verify the effectiveness and advantages of the proposed approach in terms of fast approximation.     

无模型自适应控制的现状与展望

给出了无模型控制的定义,并对已存在的无模型控制方法进行了分类．综述了无模型自适应控制理论和方法的现状和进展．讨论了无模型自适应控制与其他控制方法的主要区别,提出了两种无模型自适应控制方法与已有基于模型的控制方法优势互补的模块化设计方案,提出了有待于进一步研究的问题．     

Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol

In this paper, an adaptive dynamic programming (ADP) strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation. To save the communication resources between the controller and the actuators, stochastic communication protocols (SCPs) are adopted to schedule the control signal, and therefore the closed-loop system is essentially a protocol-induced switching system. A neural network (NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system, and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent. By virtue of a novel Lyapunov function, a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights. Then, a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints, and the convergence is profoundly discussed in light of mathematical induction. Furthermore, an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP, and the stability of the closed-loop system is analyzed in view of the Lyapunov theory. Finally, the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.      

Intelligent fault-tolerant system of vibration control for flexible structures

This paper describes an intelligent fault-tolerant control method for vibration control of flexible structures. We consider a case where the fault phenomena of the control system for flexible structures can be treated as a change of system parameters. Therefore, the adaptive control method can be applied to a vibration control system for flexible structures with a fault. In this paper, a neural network (NN) adaptive control system is used to compensate for the change in the parameters of a plant with a fault. When the characteristics of the plant and of a nominal model have been agreed by a NN adaptive control system, the control method designed for the nominal model, such as decoupling feedback control or linearizing feedback control, can be used even if the change in the system parameters has been caused by a fault. To confirm the effectiveness of the proposed fault-tolerant control method, the simulational results from a 5-link robotic arm are shown at the end of the paper. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Integrated PID-type Learning and Fuzzy Control for Flexible-joint Manipulators

The increased complexity of the dynamics of robots considering joint elasticity makes conventional model-based control strategies complex and difficult to synthesize. In this paper, a model-free control using integrated PID-type learning and fuzzy control for flexible-joint manipulators is proposed. Optimal PID gains can be learned by a neural network learning algorithm and then a simple standard fuzzy control could be incorporated in the overall control strategy, if needed, for enhancing the system responses. A modified recursive least squares algorithm is suggested for faster learning of the connection weights representing the PID-like gains. Simulation results show that the suggested simple model-free approach can control a complex flexible-joint manipulator to meet stringent requirements for both transient and steady-state performances.     

Nonlinear and Adaptive Intelligent Control Techniques for Quadrotor UAV – A Survey

Parametric uncertainties and coupled nonlinear dynamics are inherent in quadrotor configuration and infer adaptive nonlinear approaches to be used for flight control system. Numerous adaptive nonlinear and intelligent control techniques, which have been reported in the literature for designing quadrotor flight controller by various researchers, are investigated in this paper. As a priori, each conventional nonlinear control technique is discussed broadly and then its adaptive/observer based augmentation is conferred along with all possible variants. Among conventional nonlinear control approaches, feedback linearization, backstepping, sliding mode, and model predictive control, are studied. Intelligent control approaches incorporating fuzzy logic and neural networks are also discussed. In addition to adaption based parametric uncertainty handling, various other aspects of each control technique regarding stability, disturbance rejection, response time, asymptotic, exponential and finite time convergence etc., are discussed in sufficient depth. The contribution of this paper is the provision of detailed and in depth discussion on quadrotor nonlinear control approaches to the flight control designers.     

一类离散时间非线性系统不依赖受控系统数学模型的学习自适应控制

给出了一类离散时间非线性系统的不依赖受控系统数学模型的学习自适应控制方案，它不需要受控系统的结构信息、数学模型、外部试验信号和训练过程，仅用受控系统的I／O数据来设计，传统的未建模动态不存在，所给出的计算机仿真结果说明了所给出的方案的正确性和有效性。     

肌腱驱动连续体/软体机器人控制策略

深入分析了为肌腱驱动连续体机器人开发的各种控制器,以作为肌腱驱动连续体机器人领域未来应用的参照．控制策略分为基于模型的控制和无模型控制,其中,基于模型的控制可分为静态控制、动态控制及几何运动学模型控制;而无模型控制是一个相对未知的领域,本文主要从无模型静态控制、无模型动态控制及无模型形状感知控制3个方面进行全面分析．最后,对基于模型和无模型的各种控制策略进行了综合评价,并对该领域未来的研究方向进行了展望．     

Observer-based adaptive neural dynamic surface control for a class of non-strict-feedback stochastic nonlinear systems

The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The neural network (NN) approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment (or mean square). Two simulation examples are given to illustrate the effectiveness of the proposed control design.     

带钢热连轧活套系统的NNMSAC解耦控制

针对现代带钢热连轧中活套系统的解耦控制问题,提出了基于神经网络简单自适应多变量解耦控制方法（NNMSAC）。该方法运用简单自适应理论结合了神经网络技术用于多变量系统。着重描述了该控制算法的结构和原理,给出了参数选择原则和设计方法,并针对活套系统实例进行了仿真研究。仿真结果表明,这种控制算法具有实现简单,解耦效果好的特点,有效地改进了活套系统的控制效果。     

Adaptive backstepping control for a class of nonlinear systems using neural network approximations

In this paper, an adaptive neural network (NN) backstepping technique is developed for tracking control of a class of nonlinear systems. NNs are used to compensate for the unknown nonlinear functions in the system. A systematic backstepping approach is established to synthesize the adaptive NN control scheme that ensures the boundedness of all the signals in the closed‐loop system, and yields a small tracking error. The issue of transient performance is also addressed under an analytical framework. The effectiveness of the proposed scheme is demonstrated by computer simulations. Copyright © 2004 John Wiley & Sons, Ltd.