通信拓扑切换下车辆队列分布式模型预测控制

考虑通信拓扑切换下异质非线性车辆队列系统协同控制问题,提出一种能够保证车辆队列稳定和弦稳定的分布式模型预测控制策略.先结合车辆队列动态通信拓扑切换过程,构建与时间相关的图函数,再利用邻居车辆状态信息描述平均协同代价函数,并将其引入局部滚动时域优化控制问题.进一步,应用平均停留时间概念和切换系统Lyapunov稳定性理论...     

基于RVM的非线性预测控制及在聚丙烯牌号切换中的应用

针对由被控对象非线性和优化目标函数非凸性带来的建模与实时优化问题求解的困难,提出一种基于相关向量机(RVM)的非线性多步模型预测控制算法.采用RVM建立非线性预测模型,并将差分进化算法引入非线性预测控制中发挥其伞局最优、鲁棒、快速收敛等优点,在线求解多变量、多约束的非线性规划问题.利用实际生产数据进行聚丙烯牌号切换仿真,结果表明,该算法可大幅度减少切换时间,降低过渡料产量,提高经济效益.     

一种基于Wiener模型的非线性预测控制算法

针对一类Wiener模型描述的非线性系统,提出了一种改进的非线性预测控制算法.该算法利用Laguerre函数描述Wiener模型动态线性部分的控制信号，将预测控制中在预测时域内优化求解未来控制输入序列转化为优化求解一组无记忆的Laguerre系数,以减少优化所需的计算量.利用静态模糊模型来逼近Wiener模型的非线性部分,将非线性预测控制优化问题转化为线性预测控制优化问题,克服了求控制输入时解非线性方程的困难,进而推导出了预测控制输入的解析式.CSTR过程的仿真结果表明了本文算法的有效性和可行性.     

多层次多模型预测控制算法的模型切换方法研究

针对多层次多模型(Multi-hierarchical multi-model, MHM)预测控制系统的模型切换问题, 在分析各通道非线性程度对模型层次切换以及层次 间模型切换过程对系统动态特性的影响的基础上, 提出了一种新的模型切换方法. 并将此方法应用到多输入多输出pH 中和过程, 仿真结果表明, 该方法有效地改善了系统工况大范围跳变时的动态性能.     

约束非线性系统切换鲁棒预测控制

采用“分段蕴含”(PWE)方法, 用一组线性变参数模型(LPV)近似约束非线性系统, 降低模型近似的保守性. 对每个LPV模型引入参数Lyapunov函数, 得到稳定的控制律, 并施加于非线性系统. 当检测到LPV模型发生切换时, 根据可行域的离线设计方法确定适当的切换律, 使系统按照设定的规则切换, 保证切换后的初始状态可行. 在文章最后给出了基于切换策略的控制算法的可行性和稳定性. 与传统非线性预测控制相比, 基于切换策略的鲁棒预测 控制方法保守性更低, 计算量更小.     

基于Hammerstein-Wiener逆模型补偿的预测控制非线性变换策略

针对一类Hammerstein-Wiener模型描述的非线性控制系统,提出一种基于逆模型补偿的预测控制策略.在控制优化计算中,利用Wiener非线性环节的逆模型分别对系统输出设定值和采样值进行变换;控制实施过程中,将控制器输出操作量经过Hammerstein静态非线性环节模型逆变换后施加到实际被控对象上,通过两次逆变换,使得标称模型下控制器输出与闭环系统中线性环节的输入相一致.通过非线性变换补偿将非线性过程的控制转化为线性系统控制,避免了对非线性模型进行优化计算量大及预测不准确的问题.最后通过仿真验证了所提方案的可行性及有效性.     

基于T—S模糊模型的多变量预测控制

提出一种基于T-S模糊模型的多输入多输出预测控制策略.T-S模糊模型用于描述对象的非线性动态特性,模糊规则将非线性系统划分为多个局部子线性模型.为提高预测控制性能,采用多步线性化模型构成多步预报器,从而将预测控制中的非线性优化问题转化为一个线性二次寻优问题.串接贮槽液位控制系统的仿真结果表明,多步线性化模型预测控制性能优于单步线性化模型预测控制性能.     

多模型预测控制在苯乙烯聚合反应中的应用

针对苯乙烯聚合反应过程的非线性特性,将预测控制方法与多模型建模和控制原理结合起来,提出了一种基于性能指标的切换多模型非线性预测控制方法,针对聚合反应过程进行的仿真实验结果表明,该方法对类似非线性对象具有适用性,控制性能相比较普通预测控制算法也有了很明显的改进和提高.     

基于切换系统的独立微电网稳定控制设计

独立微电网是一个复杂的非线性系统,为解决典型光储柴独立微电网在能源约束下存在控制单元切换的问题,提出将非线性切换系统理论应用到主从控制下的主控单元切换问题上。首先,当主从控制下的储能系统和柴油发电机分别作为主控单元时,将它们视为两个子系统,建立非线性切换系统模型。然后,应用多Lyapunov函数法分析切换系统的稳定性,同时利用backstepping方法分别设计子系统的非线性控制器,保留系统的非线性特征。最后,采用美国国家能源实验室开发的仿真软件中的硬充电策略作为切换策略,保证切换过程中系统的稳定性。通过MATLAB仿真证明了所设计方法的有效性。     

基于Volterra模型的预测控制及应用

由于工业实践的需要,非线性预测控制近年来受到广泛地关注.Volterra模型是一类特殊的非线性模型,非常适合描述工业过程中的无记忆非线性对象.传统的基于Volterra模型的控制器合成法及迭代计算预测控制器法计算量大,且不便于处理控制约束.非线性模型预测控制求解是典型的非线性规划问题,序列二次规划(sequential quadratic program,SQP)算法是求解非线性规划问题常用方法之一.针对Volterra非线性模型预测控制求解问题,本文将滤子法与一种信赖域SQP算法相结合,提出一种改进SQP算法用于基于非线性Volterra模型的带控制约束的多步预测控制求解,并分析了所提方法的收敛性.工业实例仿真结果证实了所提方法的可行性与有效性.     

Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks

Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accuracy of a nonlinear empirical model strongly depends on its original data or training data based on how the model is built up. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. New input patterns that imply extrapolations and thus unreliable prediction by an artificial neural network model can be recognized from a significant decrease in the regional-knowledge index. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The present state of the controlled process and the model fitness to the present input pattern determine the weightings of the controller's output. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system.     

神经网络非线性多步预测逆控制方法研究*

提出了基于多步预测控制方法的多变量非线性神经网络逆控制方案。利用预测模型对系统动态特性进行预测,使用一个带有时延因子的前馈神经网络作为控制器,利用多步预测性能指标对其在线训练,实现神经网络逆系统;在多步预测过程中还对每一步的预测误差进行预测,以实现预测误差补偿。将所提出的控制算法用于锅炉这种大滞后非线性对象的控制,仿真实验证明,该控制策略具有良好的解耦和动态跟踪性能。     

基于递归神经网络模型预测控制的模型平稳切换

不同生产条件下的控制系统可视多模型控制系统,但多模型控制在模型切换时会引起系统的瞬态响应。采用递归神经网络建立系统的多个模型,基于模型预测控制进行控制模型切换,克服了模型切换时引起的系统瞬态响应,实现系统的平稳切换。并通过仿真表明这种切换策略明显改善了模型切换过程的动态性能。     

Recurrent neuro-fuzzy networks for nonlinear process modeling

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input/output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process I/O data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learnt. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.     

Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis

An adaptive p-step prediction model for nonlinear dynamic processes is developed in this paper and implemented with a radial basis function (RBF) network. The model can predict output for multi-step-ahead with no need for the unknown future process output. Therefore, the long-range prediction accuracy is significantly enhanced and consequently is especially useful as the internal model in a model predictive control framework. An improved network structure adaptation is also developed with the recursive orthogonal least squares algorithm. The developed model is online updated to adapt both its structure and parameters, so that a compact model structure and consequently a less computing cost are achieved with the developed adaptation algorithm applied. Two nonlinear dynamic systems are employed to evaluate the long-range prediction performance and minimum model structure and compared with an existing PSC model and a non-adaptive RBF model. The simulation results confirm the effectiveness of the developed model and superior over the existing models.

     

Identification and predictive control of a melter unit used in the sugar industry

This paper presents the real identification and non-linear predictive control of a melter unit; the unit is used in a sugar factory placed in Benavente (Spain). The proposed approach uses a specific recurrent neural network that allows us to identify a non-linear model of the process, providing a mathematical representation in the state space form. Output and state variables can be obtained from the inputs and measured disturbances acting on the system. The neural based predictive control is carried out through the optimization of a cost function that takes into account the output prediction errors from a reference trajectory and the future control efforts, by using the identified model as a prediction model for the system outputs. The solution to this problem provides the optimal set of future control actions, but only the first one is applied to the real process, and the optimization problem is solved again at time t + 1.The results show the good performance of neural predictive control and its suitability for applications in real systems, particularly in the process industry.     

Dynamic output feedback model predictive control for nonlinear systems represented by Hammerstein–Wiener model

This paper presents dynamic output feedback model predictive control (DOFMPC) for nonlinear systems represented by a Hammerstein–Wiener model. Compared with a previous work (IET-OFMPC: output feedback model predictive control for nonlinear systems represented by Hammerstein–Wiener model. IET Control Theory & Applications, 2007, 1 (5) pp. 1302–1310), this paper uses the notion of quadratic boundedness to specify the closed-loop stability and guarantees the recursive feasibility of the optimization problems. By optimizing all the parameters of the dynamic output feedback law within a single optimization problem, the computational burden is very huge. Hence, an alternative formulation is also proposed with much lower on-line computational burden. Numerical examples are given to illustrate the effectiveness of the controllers.     

MPC for stable linear systems with model uncertainty

In this paper, we developed a model predictive controller, which is robust to model uncertainty. Systems with stable dynamics are treated. The paper is mainly focused on the output-tracking problem of a system with unknown steady state. The controller is based on a state-space model in which the output is represented as a continuous function of time. Taking advantage of this particular model form, the cost functions is defined in terms of the integral of the output error along an infinite prediction horizon. The model states are assumed perfectly known at each sampling instant (state feedback). The controller is robust for two classes of model uncertainty: the multi-model plant and polytopic input matrix. Simulations examples demonstrate that the approach can be useful for practical application.     

Constrained control strategies for disturbance rejection in a solar furnaces

This paper addresses the temperature control problem in a solar furnace. In particular, two control strategies for the disturbances rejection (represented by the variation of the input energy provided by the Sun, mainly because of passing clouds and the solar daily cycle) are proposed, based on a two-degrees-of-freedom scheme. The first one is based on generalized predictive control, where a nonlinear model is employed for free response prediction while a linearized model is used for the computation of the forced response. Amplitude and slew-rate constraints on both the control variable and the output of the system are taken into account. The second one is a constrained control strategy where both the process input and output constraints are taken into account explicitly. In both cases an adaptation of the most significant process parameter is performed. Simulation and experimental results show the effectiveness of the methodologies.¹