智能井非线性预测控制器设计

随着石油开采中出现的新技术一智能井的应用,井控由地面转移到地下.为了解决地层物理属性难以准确获得以及流体流动的强非线性给井下控制带来的困难,研究了带有模型不确定性的非线性预测控制器.首先建立了带有不确定性的预测控制模型;然后基于反馈线性化和反步设计的思想设计非线性预测控制器.该控制器在模型带有不确定性时更易使闭环系统稳定,且动态特性良好.以北海油田某区块为例进行的仿真研究结果验证了控制器是有效的.     

基于Taylor 逼近的非线性系统P ID 型多步预测控制

基于局部递归神经网络对非线性系统进行递归多步向前预测，将系统实际多步向前预测值按泰勒公式在其递归预测值上展开，实现对非线性系统多步预测输出值的二次逼近，减少了预测误差，进而通过对PID型多步预测性能指标函数极小化求取控制量，控制器与广义预测控制器结构相似，其参数通过神经网络在线辨识获得，仿真实验表明了该方法的有效性。     

基于反步设计的构造性非线性预测控制算法

针对具有状态和输入约束的严格反馈非线性系统,提出一种反步设计构造性非线性预测控制算法.利用反步设计法离线构造系统的控制李亚普诺夫函数,进而得到系统的镇定可调控制器即稳定控制类.基于性能指标,滚动优化控制器可调参数,计算满足系统约束的预测控制量.进一步,运用控制李亚普诺大函数的性质建立闭环系统的稳定性.最后,应用轮式移动机器人的优化控制验证本文结果的有效性.     

基于输入输出线性化的连续系统非线性模型预测控制

非线性约束预测控制关键是求得可行性优化解. 输入输出反馈线性化是非线性控制一种常用的方法, 其系统的初始线性输入约束转化成非线性基于状态的约束, 因而无法采用常规的二次规划(QP)求解优化问题. 针对连续状态空间模型系统, 本文提出迭代二次规划方法来寻求非线性优化解. 为了保证算法的收敛性, 系统加入另外一种迭代算法来保证其在整个预测时域上能得到可行解. 仿真控制结果表明了该方法的有效性.     

非线性船舶航向控制器Backstepping设计

讨论了Backstepping（反向递推）设计方法在船舶航向控制器设计中的应用问题，基于诺宾（Norbin)非线性船舶模型，应用反向递推方法，成功地设计出非线性航向控制器。Matlab Simulink仿真试验表明，Backstepping（反向递推）算法可以有效克服船舶的非线操作特性，从而达到较好的航向跟踪效果。     

一类不确定非线性系统的鲁棒自适应控制

针对一类不确定非线性系统 ,设计出一种新的自适应控制器 ,它克服了现有方案存在的过多参数辨识问题 ,从而降低了控制器的动态阶次 .在较弱条件下 ,该控制器不仅能保证闭环系统所有信号有界 ,而且能使跟踪误差以指数速度收敛到零的有界小邻域内     

广义准无限时域非线性预测控制

将准无限时域非线性预测控制方法推广到更一般的情况, 并给出了闭环约束系统的稳定性条件及最优解的存在条件. 基于反馈线性化技术讨论了广义准无限时域非线性预测控制的实现及较大终端域的获取. 该方法能显著减少在线优化所需的时间.     

基于非线性干扰观测器的不确定非线性系统鲁棒轨迹线性化控制

针对一类不确定非线性系统，基于轨迹线性化控制方法(TLC)及非线性干扰观测器技术(NDO)研究了一种新的非线性鲁棒控制策略．TLC是一种有效的非线性跟踪和解耦控制方法，但当系统中存在内部未建模动态和外界干扰时，当前TLC控制器性能将显著下降．本文利用NDO对系统中的不确定项进行估计，其输出与TLC控制律结合来对消不确定性的影响．最后通过一个数值仿真实例验证了本文提出的方法的有效性,仿真结果表明该鲁棒轨迹线性化控制方法具有很好的干扰衰减能力和鲁棒性能．     

一类不确定非线性系统的鲁棒自适应控制

针对一类MIMO不确定非线性系统的输出跟踪问题, 基于自适应反步法和滑模控制为其设计了鲁棒自适应控制器. 模型包含3种不确定性: 1) 参数不确定性; 2) 输入增益的不确定性; 3) 代表系统未建模动态和干扰的不确定函数, 该函数有界. 以非完整移动机械臂的输出跟踪控制为目标, 对其进行仿真实验, 实验结果表明所提出的控制算法是正确有效的.     

非线性不对称系统的模型预测控制

针对基于正反方向上的两个线性模型分别设计 PID 控制器的缺陷,提出根据正反方向上的线性模型分别设计相应的状态反馈预测控制器.采用输入输出约束策略保证模型准确,并通过可行性分析确定最终的控制作用. pH 值控制的仿真实验表明,其对不对称非线性系统的控制效果明显优于传统的基于单一线性模型的预测控制及正反方向分别采用 PID 控制的控制效果.     

Robust discrete-time set-based adaptive predictive control for nonlinear systems

The problem of robust adaptive predictive control for a class of discrete-time nonlinear systems is considered. First, a parameter estimation technique, based on an uncertainty set estimation, is formulated. This technique is able to provide robust performance for nonlinear systems subject to exogenous variables. Second, an adaptive MPC is developed to use the uncertainty estimation in a framework of min–max robust control. A Lipschitz-based approach, which provides a conservative approximation for the min–max problem, is used to solve the control problem, retaining the computational complexity of nominal MPC formulations and the robustness of the min–max approach. Finally, the set-based estimation algorithm and the robust predictive controller are successfully applied in two case studies. The first one is the control of anonisothermal CSTR governed by the van de Vusse reaction. Concentration and temperature regulation is considered with the simultaneous estimation of the frequency (or pre-exponential) factors of the Arrhenius equation. In the second example, a biomedical model for chemotherapy control is simulated using control actions provided by the proposed algorithm. The methods for estimation and control were tested using different disturbances scenarios.     

一类非线性系统的微分平滑反步自适应输出反馈控制

研究了一类含不确定参数且存在未知扰动的严反馈非线性系统输出反馈控制问题,设计了一种新型的反步递推(Backstepping)自适应控制器.为实现输出反馈,设计过程引入了虚拟的全维状态观测器.由于Backstepping的虚拟控制量与未知参数逼近值及其高阶导数有关,为此通过微分平滑算法对原系统进行相应的动态扩展.在稳定性分析中,利用Lyapunov定理,得到了系统全局一致有界稳定的条件,并求出系统的稳态跟踪误差.最后给出的仿真算例验证了本文方法的有效性和可行性.     

一类不确定非线性系统反步自适应神经网络控制研究

针对合有高阶不确定扰动项且不可参数线性化的一类非线性系统，采用反步递推方法设计基于多层神经网络的自适应控制器，多层神经网络可较好地逼近非线性系统，其权值能在系统先验知识不多的情况下在线调整，给出了神经网络Lyapunov意义下稳定的在线自适应律，在设计控制器的过程中，采用类加权形式Lyapunov函数，使得控制器能有效处理自适应控制奇异性问题，仿真结果表明，该控制器对系统参数的不确定性和有界干扰具有一定的鲁棒性，并能保证闭环系统全局稳定。     

Adaptive output‐feedback Lyapunov‐based model predictive control of nonlinear process systems

This work addresses the problem of output feedack control of nonlinear uncertain systems via adaptive Lyapunov‐based model predictive control design. To this end, at every control implementation, a moving horizon mechanism is first utilized to generate current estimates of the uncertainty and states. The model with the current estimated uncertainty is then used in a Lyapunov‐based model predictive controller to achieve uncertainty rejection. The key ideas are explained through an illustrative example and the application demonstrated on a networked reactor‐separator process subject to measurement noise and uncertainty.     

Adaptive backstepping control for a class of semistrict feedback nonlinear systems using neural networks

This paper addresses a neural adaptive backstepping control with dynamic surface control technique for a class of semistrict feedback nonlinear systems with bounded external disturbances.Neural networks (NNs) are introduced as approximators for uncertain nonlinearities and the dynamic surface control (DSC) technique is involved to solve the so-called "explosion of terms" problem.In addition,the NN is used to approximate the transformed unknown functions but not the original nonlinear functions to overcome the possible singularity problem.The stability of closed-loop system is proven by using Lyapunov function method,and adaptation laws of NN weights are derived from the stability analysis.Finally,a numeric simulation validates the results of theoretical analysis.     

Decentralized filtered feedback linearization for uncertain nonlinear systems

This paper presents decentralized filtered feedback linearization (D‐FFL), which is a decentralized controller for uncertain nonlinear systems with potentially unknown disturbance. Moreover, D‐FFL uses only local‐state feedback (or, in certain cases, local‐output feedback) and local reference‐model‐input feedforward and requires limited model information. For sufficiently small initial conditions and sufficiently large choice of a scalar control parameter, D‐FFL makes the norm of the command‐following error arbitrarily small.     

Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

Adaptive model predictive control of nonlinear systems with state‐dependent uncertainties

This paper studies adaptive model predictive control (AMPC) of systems with time‐varying and potentially state‐dependent uncertainties. We propose an estimation and prediction architecture within the min‐max MPC framework. An adaptive estimator is presented to estimate the set‐valued measures of the uncertainty using piecewise constant adaptive law, which can be arbitrarily accurate if the sampling period in adaptation is small enough. Based on such measures, a prediction scheme is provided that predicts the time‐varying feasible set of the uncertainty over the prediction horizon. We show that if the uncertainty and its first derivatives are locally Lipschitz, the stability of the system with AMPC can always be guaranteed under the standard assumptions for traditional min‐max MPC approaches, while the AMPC algorithm enhances the control performance by efficiently reducing the size of the feasible set of the uncertainty in min‐max MPC setting. Copyright © 2017 John Wiley & Sons, Ltd.     

基于神经网络的非仿射块控非线性系统动态反演控制

即使已知非仿射非线性系统的逆存在,利用隐函数定理求解该显式逆仍然非常困难.为此,针对一类不确定块控非仿射系统,将动态反馈、反演、神经网络和反馈线性化技术相结合,提出一种自适应鲁棒控制器的设计方法.利用神经网络来逼近和消除未知函数,并证明了整个闭环系统在李雅普诺夫意义下是稳定的.仿真结果表明了所提出方法的有效性.