改进的多变量广义预测控制算法

本文中,我们把改进的广义预测控制推广列多变量线性系统中.这种算法大量地减少了计算量,能控制任意维输入任意维输出的线性系统.     

多变量模型不确定系统的二阶滑模分解控制方法

提出一种多变量模型不确定系统的二阶终端滑模分解控制方法。通过状态变换和去耦合处理将系统转换为块能控标准型，它由输入输出子系统和穗定的零动态子系统组成。提出了特殊的二阶终端滑模超曲面和相应的控制策峪，使输入输出子系统状态渐近收敛到平衡点，零动态子系统随后也渐近收敛到平衡点。所提出方法对于控制维教较高的系统具有较大的意义，可简化设计，实现鲁棒分解控制。由于采用了二阶滑模的思想，可有效地消除系统的高频抖振。仿真实例表明了该方法的有效性。     

多变量线性模型不确定系统终端滑模分解控制方法

针对线性多变量模型不确定系统系统,提出了一种终端滑模分解控制方法.通过状态变换和去耦合处理将系统转换为块能控标准型,它由值域空间子系统和稳定的零空间子系统组成.提出了特殊的终端滑模超曲面,采用滑模控制策略,使值域空间子系统的状态在有限时间内收敛至平衡点,随后稳定的零空间子系统渐近收敛至平衡点.所提出的方法对于维数较高系统的控制具有较大意义,可简化设计,实现递阶控制.仿真验证了该方法的有效性.     

多变量系统灰色预测函数控制方法研究

针对多变量时滞系统,提出一种以灰色模型为基础的多变量灰色预测函数控制策略,并给出多变量灰色预测函数控制算法.分析了灰色系统建模,灰色模型预测输出,和控制量计算方程的求解.仿真实验表明,该方法有较强的鲁棒性,快速性和强抗干扰能力强的特点.     

预测控制滚动优化的时间分解方法

基于大系统分解协调思想,针对预测控制系统,提出了一种带有并行结构的时间分解算法,以提高滚动优化在线计算效率.仿真结果表明了该算法的有效性.     

多变量广义预测控制的快速算法

针对传统广义预测控制算法的计算量大这一缺陷，通过对未来的控制序列的离线近似计算，而只精确求解当前时刻要实施的控制量，提出了一种广义预测控制的快速算法。该算法简单．适用于任意维输入任意维输出(ADIADO)线性系统。由于不必求解Diophantine方程，并在求解逆矩阵时．降低了逆矩阵的维数，从而大大减小了在线计算量。仿真结果证实了该算法有效性和实用性。     

基于滑动模型与图论的多机器人跟踪控制

移动机器人系统的非线性环节是导致控制设计困难的主要原因之一,在三维空间中的运动较二维空间更为复杂.针对三维空间中非线性移动机器人系统的跟踪控制问题,为了提高跟踪速度和改善动态特征,提出了一种基于滑动模型与图论相结合的控制策略.滑模控制方法广泛地适用于非线性对象,并且具有良好的鲁棒性,适合用于非线性机器人控制;从系统整体出发利用图论的知识,对整个编队进行约束,从而构成对整个系统的控制策略,表明能跟踪静态的目标和跟踪动态的目标.仿真提高了系统的稳定性,说明了控制方法的有效性和适用性.     

基于RBF神经网络的改进多变量预测控制

针对一类多输入多输出非线性被控对象,提出一种基于单神经网络的预测控制算法,应用RBF神经网络对非线性系统进行辨识,并计算被控系统多步预测输出值.该方法通过对传统预测目标函数加以改进,给出一种带微分项的多步预测目标函数,通过迭代寻优实时给出优化控制量.该方法实时性好,简化了传统预测控制算法,加快了滚动寻优的速度,有效地抑制了系统惯性和输入时滞所带来的超调,减小了模型误差、干扰及不确定性对控制器的影响.仿真及应用结果表明了该方法的有效性.     

一种基于多步预测的多变量自校正控制算法

本文提出一种简单的基于多步预测的自校正控制算法.该算法以 CARIMA 模型为基础,采用多步预测、优化,使其具有较强的鲁棒性.本文提出的算法简单、易于实现,只需较少的关于系统的验前知识.     

Fast algorithms for generalized predictive control

Fast algorithms for generalized predictive control (GPC) are derived by adopting an approach whereby dynamic programming and a polynomial formulation are jointly exploited. They consist of a set of coupled linear polynomial recursions by which the dynamic output feedback GPC law is recursively computed wwith only O(Nn) computations for an n-th order plant and N-steps prediction horizon.     

Rapid distributed model predictive control design using singular value decomposition for linear systems

The issue of model predictive control design of distribution systems using a popular singular value decomposition (SVD) technique is addressed. Namely, projection to a set of conjugate structure is dealt with in this paper. The structure of the resulting predictive model is decomposed into small sets of subsystems. The optimal inputs can be separately designed at each subsystem in parallel without any interaction problems. The optimal inputs can be directly obtained and the communication among the subsystems can be significantly reduced. In addition, the design of distribution model predictive control (DMPC) with constraints using the SVD framework is also presented. The unconstraint inputs are checked in parallel in the conjugate space. Without solving the QP problem of each subsystem, the suboptimal solution can be quickly obtained by selecting the bigger singular values and discarding the small singular values in the singular value space. The convergence condition of the proposed algorithm is also proved. Two case studies are used to illustrate the distribution control systems using the suggested approach. Comparisons between the centralized model predictive control method and the proposed DMPC method are carried out to show the advantages of the newly proposed method.     

GA based decomposition of large scale distributed model predictive control systems

A novel method is proposed to find the optimal decomposition structure of distributed model predictive control (DMPC) systems. The input clustering decomposition (ICD) is first developed to minimize the coupling effects of subsystems and average the computational balance of each subsystem. To select the inputs and outputs in each subsystem, the input–output pairing decomposition (IOPD) is done. Then the genetic algorithm is used to solve decomposition problems for ICD and IOPD. The proposed method can achieve efficient coordination. Its structure is more flexible than the traditional DMPC. Two examples are used to show the abilities of the proposed method.     

Distributed nonlinear model predictive control based on contraction theory

A novel distributed model predictive control algorithm for continuous‐time nonlinear systems is proposed in this paper. Contraction theory is used to estimate the prediction error in the algorithm, leading to new feasibility and stability conditions. Compared to existing analysis based on Lipschitz continuity, the proposed approach gives a distributed model predictive control algorithm under less conservative conditions, allowing stronger couplings between subsystems and a larger sampling interval when the subsystems satisfy the specified contraction conditions. A numerical example is given to illustrate the effectiveness and advantage of the proposed approach.     

Interpolation methods in model predictive control: an overview

Interpolation methods are one means of tackling the classical performance versus feasibility compromise in model predictive control (MPC). However, although some details are available in various conferences, very little has appeared in the published journals and also there is no paper pulling all the various algorithms together. Hence this article seeks to give a brief but insightful survey of existing proposals along with their strengths and weaknesses, before proposing useful avenues for future studies.     

A data-based approach for multivariate model predictive control performance monitoring

An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the “golden” user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood-Berry distillation column system.     

A new robust model predictive control method I: theory and computation

The success of the single-model MPC (SMPC) controller depends on the accuracy of the process model. Modeling errors cause sub-optimal control performance and may cause the control system to become closed-loop unstable. The goal of this paper is to examine the control performance of the robust MPC (RMPC) method proposed by Wang and Rawlings [34] on several illustrative examples. In this paper, we show the RMPC method successfully controls systems with time-varying uncertainties in the process gain, time constant and time delay and achieves offset-free non-zero set point tracking and non-zero disturbance rejection subject to input and output constraints.     

Dual-mode predictive control algorithm for constrained Hammerstein systems

In the process industry, there exist many systems which can be approximated by a Hammerstein model. Moreover, these systems are usually subjected to input magnitude constraints. In this paper, a multi-channel identification algorithm (MCIA) is proposed, in which the coefficient parameters are identified by least squares estimation (LSE) together with a singular value decomposition (SVD) technique. Compared with traditional single-channel identification algorithms, the present method can enhance the approximation accuracy remarkably, and provide consistent estimates even in the presence of coloured output noises under relatively weak assumptions on the persistent excitation (PE) condition of the inputs. Then, to facilitate the following controller design, this MCIA is converted into a two stage single-channel identification algorithm (TS-SCIA), which preserves most of the advantages of MCIA. With this TS-SCIA as the inner model, a dual-mode non-linear model predictive control (NMPC) algorithm is developed. In detail, over a finite horizon, an optimal input profile found by solving a open-loop optimal control problem drives the non-linear system state into the terminal invariant set; afterwards a linear output-feedback controller steers the state to the origin asymptotically. In contrast to the traditional algorithms, the present method has a maximal stable region, a better steady-state performance and a lower computational complexity. Finally, simulation results on a heat exchanger are presented to show the efficiency of both the identification and the control algorithms.     

Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition

Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on two case studies.     

低计算复杂度的永磁同步电机多步预测电流控制

传统的永磁同步电机模型预测电流控制策略仅在一个采样周期内寻优,难以避免陷入局部最优问题,而多步预测会增加预测次数,计算复杂度成倍增长.为此,提出一种低复杂度的永磁同步电机三步电流预测控制策略.首先,在延时补偿的基础上,两步预测结合三矢量电压控制和最优占空比电压控制,三步预测保持与两步预测相同的电压矢量,然后由代价函数选...