基于神经网络与多模型的非线性自适应广义预测控制

针对一类不确定非线性离散时间动态系统, 提出了基于神经网络与多模型的非线性广义预测自适应控制方法. 该自适应控制方法由线性鲁棒广义预测自适应控制器, 神经网络非线性广义预测自适应控制器和切换机制三部分构成. 线性鲁棒广义预测自适应控制器保证闭环系统的输入输出信号有界, 神经网络非线性广义预测自适应控制器能够改善系统的性能. 切换策略通过对上述两种控制器的切换, 保证系统稳定的同时, 改善系统性能. 给出了所提自适应方法的稳定性和收敛性分析. 最后通过仿真实例验证了所提方法的有效性.     

Adaptive neural control for uncertain stochastic nonlinear strict-feedback systems with time-varying delays: A Razumikhin functional method

This paper addresses the problem of adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays. A novel adaptive neural control scheme is presented for this class of systems, based on a combination of the Razumikhin functional approach, the backstepping technique and the neural network (NN) parameterization. The proposed adaptive controller guarantee that all the error variables are 4-Moment semi-globally uniformly ultimately bounded in a compact set while the system output converges to a small neighborhood of the reference signal. Two simulation examples are given to demonstrate the effectiveness of the proposed control schemes.     

Neuro-control of unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.     

Observer-based Finite-time Control of Stochastic Non-strict-feedback Nonlinear Systems

This paper investigates the observer-based adaptive finite-time neural control issue of stochastic non-strict-feedback nonlinear systems. By establishing a state observer and utilizing the approximation property of the neural network, an adaptive neural network output-feedback controller is constructed. The controller solves the issue that the states of stochastic nonlinear system cannot be measured, and assures that all signals in the closed-loop system are bounded. Different from the existing adaptive control researches of stochastic nonlinear systems with unmeasured states, the proposed control scheme can guarantee the finite-time stability of the stochastic nonlinear systems. Furthermore, the effectiveness of the proposed control approach is verified by the simulation results.

     

基于神经网络与多模型的非线性自适应广义预测解耦控制

针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性.     

Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems

In this paper, an adaptive backstepping control problem is proposed for a class of multiple-input-multiple-output nonlinear non-affine uncertain systems. An output recurrent wavelet neural network (ORWNN) is used to approximate the unknown nonlinear functions to develop the proposed adaptive backstepping controller. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network, and output feedback layer to achieve higher approximation accuracy and faster convergence. According to the estimation of ORWNN, the control scheme is designed by backstepping approach such that the system outputs follow the desired trajectories. Based on the Lyapunov approach, our approach guarantees that the system outputs converge to a small neighborhood of the references signals, that is, all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results including double pendulums system and two inverted pendulums on carts system are shown to demonstrate the performance and effectiveness of our approach.     

基于快速算法的模糊神经网络自适应控制

针对过程控制中被控对象常具有非线性、不确定性及参数时变等复杂因素,而难以建立精确的数学模型的情况,提出了一种基于快速学习算法的模糊神经网络自适应预测控制方案。该方案用神经网络作辨识器,模糊神经网络作控制器来实现非线性系统的自适应预测控制。为了克服传统的梯度下降法收敛速度慢、容易陷入局部极小值的缺点,该方案采用递推最小二乘法训练模糊神经网络。仿真结果表明,该方案可以实现模糊控制和神经网络的优势互补,对不确定非线性系统具有很好的控制效果。     

Robust output feedback control of nonlinear stochastic systems using neural networks

We present an adaptive output feedback controller for a class of uncertain stochastic nonlinear systems. The plant dynamics is represented as a nominal linear system plus nonlinearities. In turn, these nonlinearities are decomposed into a part, obtained as the best approximation given by neural networks, plus a remaining part which is treated as uncertainties, modeling approximation errors, and neglected dynamics. The weights of the neural network are tuned adaptively by a Lyapunov design. The proposed controller is obtained through robust optimal design and combines together parameter projection, control saturation, and high-gain observers. High performances are obtained in terms of large errors tolerance as shown through simulations.     

一类非线性非最小相位系统的直接自适应控制

针对一类不确定的离散时间非线性非最小相位动态系统,提出了一种基于神经网络和多模型的直接自适应控制方法．该控制方法由线性直接自适应控制器,神经网络非线性直接自适应控制器以及切换机构组成．线性控制器用来保证闭环系统输入输出信号有界,非线性控制器用来改善系统性能．切换策略通过对上述两种控制器的切换,保证闭环系统输入输出有界的同时,改善了系统性能．理论分析以及仿真结果表明了所提出的直接自适应控制方法的有效性．     

不确定性自由漂浮空间机器人神经自适应控制

针对具有模型不确定性以及外部干扰下的自由漂浮空间机器人,采用一种整体逼近的神经网络自适应控制方法。该方法采用RBF神经网络对不同重力环境下系统模型的不确定项进行整体逼近,对系统的不确定项进行在线自适应学习。神经网络的逼近误差以及外界干扰由鲁棒项进行消除。该方法不依赖于系统模型,简化了控制系统的结构,在考虑重力等不确定项的情况下不用改变控制器也能进行控制,并且根据李亚普诺夫理论证明了所设计控制器使系统渐进稳定。在不同重力环境下进行了仿真,验证了控制方案的有效性。     

A real-time neuro-adaptive controller with guaranteed stability

This paper presents a new model reference adaptive neuro-control scheme using feedforward neural networks with momentum back-propagation (MBP) learning algorithm. Training is done on-line to tune the parameters of the neuro-controller that provides the control signal. Noting that pre-learning is not required and the structure of overall system is very simple and straightforward. No additional controller or robustifying signal is required. Tracking performance is guaranteed via Lyapunov stability analysis. Both tracking error and neural network weights remain bounded. An interesting fact about the proposed approach is that it does not require a NN being capable of reconstructing globally model non-linearities.     

基于预测控制方法的非线性神经网络逆控制

针对一类多输入多输出非线性被控对象,利用前向神经网络逼近原系统的逆系统,将其作为控制器,采用预测滚动优化性能指标训练该神经网络逆控制器,以克服干扰和不确定性影响,实现对多变量非线性对象的解耦控制。对某微型锅炉对象进行了控制算法仿真,结果表明,所提出的控制方法能够克服模型误差的影响,实现稳定解耦控制,且易于实现。在仿真过程中通过实验方法建立该锅炉对象的神经网络预测模型,并注意采用泛化方法采集训练样本数据和训练神经网络,以提高神经网络模型的泛化能力。     

Adaptive control for uncertain nonlinear systems based on multiple neural networks.

A new adaptive multiple neural network controller (AMNNC) with a supervisory controller for a class of uncertain nonlinear dynamic systems was developed in this paper. The AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks was used to approximate system nonlinearity for the given task. Each neural network represents the system dynamics for each task. For a job where some tasks are repeated but information on the load is not defined and unknown or varying, the proposed controller is effective because of its capability to memorize control skill for each task with each neural network. For a new task, most similar existing control skills may be used as a starting point of adaptation. With the help of a supervisory controller, the resulting closed-loop system is globally stable in the sense that all signals involved are uniformly bounded. Simulation results on a cartpole system for the changing mass of the pole were illustrated to show the effectiveness of the proposed control scheme for the comparison with the conventional adaptive neural network controller (ANNC).     

Model-free control of nonlinear stochastic systems withdiscrete-time measurements

Consider the problem of developing a controller for general (nonlinear and stochastic) systems where the equations governing the system are unknown. Using discrete-time measurement, this paper presents an approach for estimating a controller without building or assuming a model for the system. Such an approach has potential advantages in accommodating complex systems with possibly time-varying dynamics. The controller is constructed through use of a function approximator, such as a neural network or polynomial. This paper considers the use of the simultaneous perturbation stochastic approximation algorithm which requires only system measurements. A convergence result for stochastic approximation algorithms with time-varying objective functions and feedback is established. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations     

Implementation of indirect neuro-control for a nonlinear two-robot MIMO system

This paper presents identification and control designs using neural networks for a nonlinear two-robot multiinput multioutput (MIMO) system. The proposed neuro-controller is a combination of linear controllers and a neural-network controller, and is trained by an indirect neuro-control scheme. The proposed neuro-controller is implemented and tested on an IBM PC-based two 2-bar systems holding an object, and is applicable to many d.c.-motor-driven precision nonlinear two-robot MIMO systems. The algorithm and experimental results are described. The experimental results are shown to be superior to those of conventional control.     

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡, 且无法实现系统性能整体优化的问题, 本文提出一种制冷站非线性 预测控制策略, 优化目标函数设计为满足建筑冷量需求的同时, 尽可能提高系统整体能效. 为解决上述两个优化目 标之间的矛盾关系, 本文采用模糊逻辑设计了优化目标权重自适应模块, 实时求取权重因子最优解; 针对非线性系 统在线优化求解困难问题, 本文提出了基于神经网络的非线性滚动优化算法, 采用神经网络作为反馈优化控制器, 并将系统优化目标函数作为在线寻优性能指标, 结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进 行在线寻优, 算法计算量小, 占用存储空间适中, 便于采用低成本的现场控制器实现制冷站预测控制. 仿真实验结果 表明, 本文所提出的预测控制策略与PID控制相比, 在未加入优化目标函数权重自适应模块情况下, 系统平均能效 比提高约32.5%; 进行优化目标函数权重自适应寻优后, 系统平均能效提高约39.43%.     

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.     

Optimal disturbance rejection control approach based on a compound neural network prediction method

A new optimal disturbance rejection control method is proposed for the system with disturbances via a compound neural network prediction approach in this paper. The disturbances caused by external disturbances and model mismatches can be estimated by a disturbance observer, and the estimation of disturbances is introduced into the neural network predictive model to make the predictive output more accurate. Then based on the new compound neural network predictive model, a controller, which ensures both optimal performance by the receding horizon optimization and strong disturbance rejection ability, is obtained. The proposed scheme is applied to control the temperature of a simplified jacketed stirred tank heater (JSTH). Simulation results demonstrate the effectiveness of the proposed control method.     

Robust exponential stability of uncertain stochastic systems with probabilistic time‐varying delays

This paper is concerned with the problem of delay‐distribution–dependent robust exponential stability for uncertain stochastic systems with probabilistic time‐varying delays. Firstly, inspired by a class of networked systems with quantization and packet losses, we study the stabilization problem for a class of network‐based uncertain stochastic systems with probabilistic time‐varying delays. Secondly, an equivalent model of the resulting closed‐loop network‐based uncertain stochastic system is constructed. Different from the previous works, the proposed equivalent system model enables the controller design of the network‐based uncertain stochastic systems to enjoy the advantage of probability distribution characteristic of packet losses. Thirdly, by applying the Lyapunov‐Krasovskii functional approach and the stochastic stability theory, delay‐distribution–dependent robust exponential mean‐square stability criteria are derived, and the sufficient conditions for the design of the delay‐distribution–dependent controller are then proposed to guarantee the stability of the resulting system. Finally, a case study is given to show the effectiveness of the results derived. Moreover, the allowable upper bound of consecutive packet losses will be larger in the case that the probability distribution characteristic of packet losses is taken into consideration.     

时变时滞随机非线性系统的自适应神经网络跟踪控制

针对一类具有时变时滞的不确定随机非线性严格反馈系统的自适应跟踪问题,利用Razumikhin引理和backstepping方法,提出一种新的自适应神经网络跟踪控制器.该控制器可保证闭环系统的所有误差变量皆四阶矩半全局一致最终有界,并且跟踪误差可以稳定在原点附近的邻域内.仿真例子表明所提出控制方案的有效性.