Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition-based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.     

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

MIMO系统的约束广义预测控制

讨论了输出终端带不等式约束情形的多输入多输出系统，说明了这些约束可以通过修改输入柔化系数而使其得以满足，在柔化系数的选择范围确定过程中，为了避免矩阵求逆，采用利用已知数据辨识参数的方法来确定柔化系数，从而使得问题简化。降低了计算量且加快了收敛速度。     

动态矩阵控制在浆料浓度控制系统中的应用

针对PTA生产中浆料配制过程这一有纯滞后、大时间常数的非线性系统，进行系统的动态机理分析，与工作点附近的线性化，在此基础上设计DMC-PID串级加前馈控制方案。目前该系统在扬子石化化工厂PTA装置上已成功投运。     

Stabilizing decentralized model predictive control of nonlinear systems

This note presents a stabilizing decentralized model predictive control (MPC) algorithm for nonlinear discrete time systems. No information is assumed to be exchanged between local control laws. The stability proof relies on the inclusion of a contractive constraint in the formulation of the MPC problem.     

Linear predictive transform of monochrome images

Digital monochromatic images are encoded using a novel minimum mean square error (MSE) linear predictive transform (LPT) coding formulation. The new formulation is appealing for two important reasons. First, it leads to simple coder implementation with a satisfactory signal-to-noise ratio (SNR). Second, it provides a general theoretical framework from which minimum MSE predictive coding and minimum MSE transform coding arise as special cases. Some specific results of this paper that illustrate the previous ideas are: a simple and generally suboptimum two-dimensional LPT coder operating at 2 bit pixer⁻¹ has approximately one third the complexity of a 4 × 4 Hadamard coder while yielding a better SNR; an optimum 2D LPT coder operating at 2 bit pixer⁻¹ has approximately one sixth the complexity of a 4 × 4 Karhunen-Loeve transform (KLT) coder while yielding a better SNR.     

A simple conceptual model for predicting the dissolution of phosphate rock in soils

Laboratory experimental and literature data were integrated to develop a simple, conceptual model to describe and predict the dissolution of a reactive phosphate rock (Gafsa phosphate rock, GPR) in soils. The model showed that initial solution Ca concentration strongly influences the time required for a single application of GPR (at 30 kgP ha^?1) to dissolve. The model predicts that all of the GPR will dissolve within a year of application in an unlimed, acid (pH_w 4.5) loam. However, if the soil has previously been limed to (pH_w 5.8, and contains permanent charge only, the model predicts that only about 50% of the GPR would have dissolved by the end of the second year after application. On the other hand, if a previously limed soil ((pH_w 5.8) contains variable-charge components, the model predicts that virtually all of the GPR would have dissolved in this soil by the end of the second year after application. These results emphasise that, even in the presence of a high proton supply, solution Ca has an overriding influence on the dissolution of GPR. The faster rate of GPR dissolution in the limed soil with variable charge, compared to that in the limed soil with permanent charge only, demonstrates the ability of the variable-charge component of soil to act as a sink for some of the lime-derived Ca. According to the solubility product principle, this allows more GPR to dissolve. Because of the generally stronger buffering of soil for P than for Ca, a relatively large proportion of any P removed by leaching and plant uptake is buffered by the sorbed phase. Consequently, the influence of leaching and plant uptake on GPR dissolution is attributed primarily to the removal of the relatively less-strongly buffered Ca.     

自适应密集预测的模型算法控制

本文中提出了一种新的预测控制策略:在一个模型采样周期内(本文中定义)进行数次预测控制。     

广义预测控制系统的仿真软件设计与开发

介绍了基于广义预测控制的模型预测控制（MPC）系统仿真软件的开发与实现过程。该软件包含了MAC，IMAC，DMC，DDMC和GPC五种预测控制算法，可以用不同的方法实现对预测控制系统的综合性仿真以及控制器参数的调整。利用该软件可对模型预测控制系统的作用机理做进一步仿真分析，并有助于定性分析各种算法中设计参数对系统性能的影响以及不同算法之间的区别。