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.     

A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm

Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions.     

Improved data-driven optimal TILC using time-varying input signals

A new improved data-driven optimal TILC (DDOTILC) is proposed for a class of nonlinear discrete-time systems by using time-varying control input signals to enhance control performance. An equivalent dynamical linear presentation is developed in the iteration domain for the repeatable nonlinear system, where the time-varying partial derivatives of the system with respect to the time-varying control inputs reflect the dynamical characters of the plant. Both the time-varying input signals and time-varying partial derivatives over the entire finite time interval are updated in batches, respectively. The proposed approach is a data-driven control scheme and only the boundedness of the partial derivatives is needed for control system design and analysis. Both rigorous mathematical analysis and the simulation results are provide to verify the applicability and effectiveness of the proposed approach further.     

ILC strategy for progress improvement of economic performance in industrial model predictive control systems

A novel approach to progress improvement of the economic performance in model predictive control (MPC) systems is developed. The conventional LQG based economic performance design provides an estimation which cannot be done by the controller while the proposed approach can develop the design performance achievable by the controller. Its optimal performance is achieved by solving economic performance design (EPD) problem and optimizing the MPC performance iteratively in contrast to the original EPD which has nonlinear LQG curve relationship. Based on the current operating data from MPC, EPD is transformed into a linear programming problem. With the iterative learning control (ILC) strategy, EPD is solved at each trial to update the tuning parameter and the designed condition; then MPC is conducted in the condition guided by EPD. The ILC strategy is proposed to adjust the tuning parameter based on the sensitivity analysis. The convergence of EPD by the proposed ILC has also been proved. The strategy can be applied to industry processes to keep enhancing the performance and to obtain the achievable optimal EPD. The performance of the proposed method is illustrated via an SISO numerical system as well as an MIMO industry process.     

基于小空间的火焰检测方法研究与实现

根据色度学和数字图像处理原理,讨论了如何计算数字图像区域周长和致密度.从理论分析及实验结果中得出了该方法具有精度高、适应性强、易于计算等特点,可以抵抗常见的干扰对系统的影响并较好的实现识别系统的鲁棒性与敏锐性的统一.     

A high performance framework for modeling and simulation of large-scale complex systems

Due to the quick advances in the scale of problem domain of complex systems under investigation, the complexity of multi-input component models used to construct logical processes (LP) has significantly increased. High-performance computing technologies have therefore been extensively used to enable parallel simulation execution. However, the traditional multi-process parallel method (MPM) executes LPs in parallel on multi-core platforms, which ignores the intrinsic parallel capabilities of multi-input component models. In this study, a vectorized component model (VCM) framework has been proposed. The design aims to better utilize the parallelism of multi-input component models. A two-level composite parallel method (CPM) has then been constructed within the framework, which can sustain complex system simulation applications consisting of multi-input component models. CPM first employs MPM to dispatch LPs onto a multi-core computing platform. It then maps VCMs to the multiple-core platform for parallel execution. Experimental results indicate that (1) the proposed VCM framework can better utilize the parallelism of multi-input component models, and (2) CPM can significantly improve the performance comparing to the traditional MPM. The results also show that CPM can effectively cope with the size and complexity of complex simulation applications with multi-input component models.     

H ∞ switching filter design for LPV systems in finite frequency domain

This article studies the problem of finite frequency H _∞ filter design for switching linear parameter varying (LPV) systems with disturbance frequency affected by system parameters. A set of switching filters are designed, each suitable for a specific parameter subregion and a specific finite frequency performance index. A hysteresis switching logic is adopted, and the design problem is finally formulated into linear matrix inequality (LMI) which can be solved efficiently with LMI Control Toolbox. A numerical example is given to illustrate the effectiveness of the proposed method.     

Fuzzy goal-driven intelligent control for satellite environmental qualification

This paper reports on the application of Fuzzy Reference Gain-Scheduling Control (FRGS) to control a thermal-vacuum unit that emulates space environmental conditions for satellite and space device qualification. FRGS is a variation of fuzzy control that changes the controller gain surface in accordance to distinct operational conditions established by the reference (goal). This system allows to incorporate the experience of human operators by emulating human decision-making and reasoning, with the advantage of adaptation when parameters are adjusted on-line. The controller gain surface is adapted by modifying the shapes of the membership functions according to piecewise constant reference values. While the goal-driven adjustment is accomplished in a feedforward manner, feedback control technique is used to guarantee dynamical response. The experimental response obtained in the application to the real system has shown the effectiveness and flexibility of this control approach for such a nonlinear system.     

有界模型检测的优化

G(p)和G(p→F(q))是有界模型检测(bounded model checking,简称BMC)中的两个重要的常用模态算子.对验证G(p)和G(p→F(q))编码转换公式进行优化.通过分析当验证这些模态算子时FSM(finite state machine)的状态转移和线性时序逻辑(linear-time temporal logic,简称LTL)的语义特征.在现有的编码公式的基础上,给出了简洁、高效的递推公式,该公式有利于高效编码成SAT(satisfiability)实例;证明了递推公式和原转换公式的逻辑关系.通过实验比较分析,在生成SAT实例规模和易求解方面都优于BMC中求解这些模态算子的现有的两种重要方法AA_BMC和Timo_BMC.所给出的方法和思想对于BMC中验证其他模态算子时的编码优化也有参考价值.