基于扩展BP神经网络的一类非线性系统自适应控制设计

针对单输入单输出非线性系统的不确定性问题,提出了一种新型的基于扩展反向传播（BP）神经网络的自适应控制方法。首先,采用离线数据来训练BP神经网络的权值向量;然后,通过在线调节伸缩因子和逼近精度估计值的更新律,从而来达到控制整个系统的目的。在控制器的设计过程中,利用李亚普诺夫稳定性分析原理,保证了闭环系统的所有状态一致终极有界（UUB）。相比传统的BP神经网络自适应控制,所提方法能有效地减少在线调节的参数数目、减轻计算负担。仿真结果表明,该方法能够使闭环系统的所有状态都趋于零,即系统达到稳定状态。     

Globally Stable Adaptive Tracking Control for Uncertain Strict‐Feedback Systems Based on Neural Network Approximation

This paper addresses the problem of globally stable adaptive neural tracking control for a class of strict‐feedback nonlinear systems. Compared with the existing works, the salient properties of the proposed scheme are given as follows. First, a novel switching controller is developed, which consists of a traditional adaptive neural controller and an extra robust controller to pull back the transient outside of the approximation domain. Second, only two adaptive parameters need to be tuned online, and the computational burden is considerably alleviated in practice. Third, to design the desired switching controller via the backstepping technique, a novel switching function, which has continuous derivatives up to the nth order, is constructed. It is shown that the system output converges to a small neighborhood of the reference signal and the closed‐loop system is globally stable. Finally, an example is provided to verify the effectiveness of the proposed control method.     

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 control for a class of nonlinearly parametric time-delay systems

In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.     

Online Adaptive Approximate Optimal Tracking Control with Simplified Dual Approximation Structure for Continuous-time Unknown Nonlinear Systems

This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics. The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values. An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network (NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman (HJB) equation, which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error. Stability of the whole system consisting of the identifier NN, the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.      

Specificity enhancement in classification of breast MRI lesion based on multi-classifier

In the conventional CMAC-based adaptive controller design, a switching compensator is designed to guarantee system stability in the Lyapunov stability sense but the undesirable chattering phenomenon occurs. This paper proposes a CMAC-based smooth adaptive neural control (CSANC) system that is composed of a neural controller and a saturation compensator. The neural controller uses a CMAC neural network to online mimic an ideal controller and the saturation compensator is designed to dispel the approximation error between the ideal controller and neural controller without any chattering phenomena. The parameter adaptive algorithms of the CSANC system are derived in the sense of Lyapunov stability, so the system stability can be guaranteed. Finally, the proposed CSANC system is applied to a Chua’s chaotic circuit and a DC motor driver. Simulation and experimental results show the CSANC system can achieve a favorable tracking performance. It should be emphasized that the development of the proposed CSANC system doesn’t need the knowledge of the system dynamics.     

Design of an amplitude-bounded output feedback adaptive neural control with guaranteed componentwise performance

This paper proposes a methodology for the design of a mixed output feedback linear and adaptive neural controller that guarantees componentwise boundedness of the tracking error within an a priori specified compact polyhedron for an uncertain nonlinear system. The approach is based on the design of a robust invariant ellipsoidal set where the adaptive neural network (NN) control is modeled as an amplitude-bounded signal. A linear error observer is employed to recover the unmeasured states, and a linear gain controller is used to enforce the containment of the ellipsoidal set within the performance polyhedron. The analysis and design of the observer and linear controller is set up as an LMI problem. The linear observer/controller scheme is then augmented with a general adaptive NN element having the purpose of approximating and compensating for the unknown nonlinearities thus providing performance improvement. The only requirement for the adaptive control signals is that their amplitudes must be confined within pre-specified limits. For this purpose, a novel mechanism called adaptive control redistribution is introduced to manage the adaptive NN control confinement during the online operation. A numerical example is used to illustrate the design methodology.     

Adaptive recurrent neural network control using a structure adaptation algorithm

This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L ² tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L ² control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.     

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.     

Synchronization of unified chaotic system via adaptive wavelet cerebellar model articulation controller

This study aims to propose a more efficient control algorithm for the chaotic system synchronization. In this study, a novel wavelet cerebellar model articulation controller (WCMAC) is proposed, which incorporates the wavelet decomposition property with a cerebellar model articulation controller (CMAC). This WCMAC is a generalization network; in some special cases, it can be reduced to a wavelet neural network, a neural network and a conventional CMAC. Then, an adaptive wavelet cerebellar model articulation control system (AWCCS) is proposed to synchronize a unified chaotic system. In this AWCCS, WCMAC is the main controller utilized to mimic a perfect controller and the parameters of WCMAC are online adjusted by the derived adaptive laws; and a compensation controller is designed to dispel the residual of the approximation error for achieving $ H^{\infty } $ robust performance. The derived AWCCS is then applied to the chaotic system synchronization control. Finally, the effectiveness of the proposed control system is demonstrated through simulation results.     

An Adaptive Neural Sliding Mode Controller for MIMO Systems

Here, a novel adaptive neural sliding mode controller (ANSMC) is proposed to handle the coupling and dynamic uncertainty of MIMO systems. The structure of this model-free new controller is based on a radial basis function neural network (RBFNN) which is derived from Lyapunov stability theory and relaxing Kalman–Yacubovich lemma to monitor the system for tracking a user-defined reference model. The weights of RBFNN can be initialized at zero, then, a novel online tuning algorithm is developed based on Lyapunov stability theory. A boundary layer function is introduced into the updating law to cover the parameter errors and modeling errors, and to guarantee the state errors converge into a specified error bound. An e-modification is added into the updating law to release the assumption of persistent excitation and obtain the appropriate values of the connecting weights of a RBFNN. To evaluate the control performance of the proposed controller, a two-link robot system is chosen as the simulation case. The numerical simulations results show that this novel controller has very good tracking accuracy, stability and robustness.     

考虑输出约束的机械臂自适应神经网络鲁棒控制

在工业机械臂系统的跟踪控制过程中,由于其结构和工作环境复杂,导致难以建立精确的系统模型,针对此问题提出了基于多层前馈神经网络的自适应鲁棒控制器.通过神经网络在线估计机械臂系统动力学模型,并在控制器中进行补偿,同时设计了一个在线更新的鲁棒项克服神经网络的重构误差;考虑机械臂实际系统的输出约束,采用障碍李雅普诺夫函数设计控制律并证明系统的稳定性从而使系统满足约束条件.仿真实验结果表明:在约束条件下所提出的控制器能够实现系统的一致最终有界稳定,且跟踪性能良好,并具有很好的抗干扰和自适应能力.     

Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. Simulation on a continuous stirred tank reactor (CSTR) and practical experiments on a three-tank liquid level process control system are included to verify the reliability and effectiveness.     

Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large-scale uncertain nonlinear time-delay systems with input saturation. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constrains are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion”, “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.     

A self-evolving functional-linked wavelet neural network for control applications

The structure of a neural network is determined by time-consuming trial-and-error tuning procedure in advance for the reason that it is difficult to consider the balance between the neuron number and the desired performance. To attack this problem, a self-evolving functional-linked wavelet neural network (SFWNN) is proposed. Without the need for preliminary knowledge, a self-evolving approach demonstrates that the properties of generating and pruning the hidden neurons automatically. Then, an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) system which is composed of a neural controller and a supervisory compensator is proposed. The neural controller uses a SFWNN to online estimate an ideal controller and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. To investigate the capabilities of the proposed ASFWNC approach, it is applied to a chaotic system and a DC motor. The simulation and experimental results show that favorable control performance can be achieved by the proposed ASFWNC scheme.     

An intelligent adaptive control scheme for postsurgical blood pressure regulation

This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability. System approximation formulated by the G-FNN is employed in the adaptive controller design for drug infusion in intensive care environment. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through intravenous infusion of sodium nitroprusside (SNP), which is one attractive application in automation of drug delivery. Simulation studies demonstrate the capability of the proposed approach in estimating the drug's effect and regulating blood pressure at a prescribed level.     

Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The hws for model updating and the control hws for the neural adaptive controller are derived from Lyaptmov stability theorem, therefore the semi - global stability of the closed-loop system is guaranteed. At last, the simulation results are illuswated.     

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances.      

Adaptive neural output feedback tracking control for a class of nonlinear systems

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.     

基于反馈增益的AUV稳定神经网络反步变深控制

为解决自主水下航行器的变深控制问题,提出一种基于反馈增益的反步控制方法.首先,通过设计控制器参数消除部分非线性项,在保证系统稳定性的同时设计神经网络控制器来补偿纵倾运动中的模型不确定性;然后,通过自适应鲁棒控制器对神经网络的逼近误差予以消除,以加快神经网络的收敛学习速度,神经网络权值和逼近误差估计的学习律可由李雅普诺夫稳定性理论推导得出,保证了闭环系统的一致最终有界性;最后,通过仿真实验验证了所提出方法的有效性.