Fuzzy-model-based hybrid predictive control

In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustrative experiment on a hybrid tank system is conducted to demonstrate the benefits of the proposed approach.     

Data-driven modeling and predictive control for boiler–turbine unit using fuzzy clustering and subspace methods

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler–turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler–turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler–turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.     

Control of nonlinear processes by using linear model predictive control algorithms

Most chemical processes are inherently nonlinear. However, because of their simplicity, linear control algorithms have been used for the control of nonlinear processes. In this study, the use of the dynamic matrix control algorithm and a simplified model predictive control algorithm for control of a bench-scale pH neutralization process is investigated. The nonlinearity is handled by dividing the operating region into sub-regions and by switching the controller model as the process moves from one sub-region to another. A simple modification for model predictive control algorithms is presented to handle the switching. The simulation and experimental results show that the modification can provide a significant improvement in the control of nonlinear processes.     

基于简化模糊模型的广义预测控制器

利用适应模糊推理修正非线性系统的局部线性化，提出一种基于模糊辨识的非线性系统广义预测控制器。仿真研究表明，该算法能有效实现对非线性对象的预测控制，抑制外来噪声，具有良好的鲁棒性。     

Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics

In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms.     

基于灰色模型的CMP过程免疫预测R2R控制

针对化学机械研磨(chemical mechanical polishing,CMP)过程非线性、时变、产品质量不能在线测量的特性,为了提高CMP过程R2R(Rum-to-Run)控制的精度,提出了一种基于灰色模型和克隆选择免疫算法的CMP过程R2R预测控制器GI-PR2R。通过离线测量获得历史批次少量数据,构建CMP过程的在线灰色GM(1,N)预测模型,解决了复杂CMP过程难以建立精确数学模型的难题提高了预测模型的精度。通过基于克隆选择免疫算法的CMP过程预测控制的滚动优化,避免了基于导数的优化技术易陷入局部最优的问题,进而提高了控制精度。仿真结果表明,CMP过程GIPR2R控制器的控制精度优于EWMA(exponentially weighted moving average)方法,有效抑制了过程漂移,减小了不同批次间产品的差异,材料去除率(material removalrate,MRR)的均方根误差在总批次与控制目标不同这2种情况下分别降低了18.09%和16.84%。     

T–S fuzzy model predictive speed control of electrical vehicles

This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi–Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand.     

带料纠偏的模糊预测控制

带料纠偏是高度非线性过程,传统的模型预测控制(MPC)无法有效地处理这种过程.模糊神经网络(FNN)方法可以实现非线性过程模型.通过测量得到的数据作为样本来训练神经网络.预测准确度由前馈网络的插值能力保证.多维搜索技术用来解决非线性最优化问题,最优结果被嵌入BP神经网络预测控制器中.BP神经网络的快速计算能满足实时控制需要.带料纠偏试验结果已经证明了FNN预测控制的有效性.     

Takagi–Sugeno fuzzy model based robust dissipative control for uncertain flexible spacecraft with saturated time-delay input

In this paper, the problem of robust dissipative control is investigated for uncertain flexible spacecraft based on Takagi–Sugeno (T–S) fuzzy model with saturated time-delay input. Different from most existing strategies, T–S fuzzy approximation approach is used to model the nonlinear dynamics of flexible spacecraft. Simultaneously, the physical constraints of system, like input delay, input saturation, and parameter uncertainties, are also taken care of in the fuzzy model. By employing Lyapunov–Krasovskii method and convex optimization technique, a novel robust controller is proposed to implement rest-to-rest attitude maneuver for flexible spacecraft, and the guaranteed dissipative performance enables the uncertain closed-loop system to reject the influence of elastic vibrations and external disturbances. Finally, an illustrative design example integrated with simulation results are provided to confirm the applicability and merits of the developed control strategy.     

Properties of extended predictive control

Many tuning strategies for model predictive control algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost function. In this paper, the properties of a new predictive controller termed extended predictive control (EPC) are investigated and presented. These properties are important to the understanding of the unique tuning strategy of EPC. EPC is based on the assumption of infinite horizon which is preferable to guarantee stability. The EPC properties are derived using a second order plant with relatively large dead time and is applicable to any open-loop stable system. The tuning strategy of EPC was applied to generalized predictive control with good results.     

Receding horizon online optimization for torque control of gasoline engines

This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the Continuation/GMRES (generalized minimum residual) approach. Several receding horizon control schemes are designed to investigate the effects of the integral action and integral gain selection. Simulation analyses and experimental validations are implemented to demonstrate the real-time optimization performance and control effects of the proposed torque tracking controllers.     

基于状态反馈线性化的阀控非对称缸系统模型预测控制

针对使用PID方法对阀控非对称液压缸位置控制中出现的超调问题,以及传统非线性模型预测控制优化求解计算时间较长的问题,提出了一种基于状态反馈线性化的阀控非对称缸模型预测控制方案。首先建立了阀控系统状态空间模型,运用微分几何理论讨论系统可反馈线性化的充要条件,并将非线性系统映射为新坐标空间内的线性系统模型;设计了反馈线性化模型预测控制器(Feedback Linearization Model Predictive Controller, FLMPC),讨论了线性系统下的约束问题,其中由于系统仿真预测时域远小于系统响应时间,对模型预测控制的损失函数加以修正。结果证明,在相同输入情况下,反馈线性化系统与原系统的位置误差满足控制需要,且在保证被控对象快速稳定控制的条件下,对比该算法与非线性模型预测控制的单步计算时间,证明该算法能够缩短计算时间。     

Modeling and control of the magnetic suspension system

A fuzzy logic based controller applied to a simple magnetic suspension is presented in this paper. The simple electromagnet-ball system and the contactless optical position measurement system are developed as a physical model of the magnetic suspension. A nonlinear mathematical model is presented and linearized. This model has been used to design a discrete linear PID controller with optimal parameters. The physical real-time model was constructed in order to compare the performance of the linear discrete PID controller and the proposed fuzzy logic based PID controller. The decomposed fuzzy PID controller has proportional, integral, and derivative separate parts which are tuned independently. When testing it becomes clear that the decomposed fuzzy PID controller gives better performance over a typical operational range than a traditional linear PID controller.     

模糊自适应预测控制在航空发动机中的应用研究

对于航空发动机这样复杂的系统,其数学模型具有较大的不确定性,而模糊控制对于解决模型不确定性问题具有较好的优势,运用模糊控制理论就具有较好的实践意义。而且,航空发动机在实际运行中存在诸多可测与不可测的扰动,将模糊建模技术与预测控制算法相结合,采用输出误差反馈启发校正的方法,有效地降低了系统设计与实现的复杂性,提高了系统的实时性,并使得该算法的模糊预测控制在鲁棒性、动态性能等方面皆优于常规PID控制。最后,通过数字仿真,对比了经典PID控制和运用模糊自适应预测控制。仿真结果表明,文中所采用的方法有较好的效果,其证明了该方法在航空发动机控制中应用的可能性。     

Nonlinear Model-Based Predictive Control Using a Generalised Hammerstein Model and its Application to a Semi-Batch Reactor

In recent years, much attention has been focused upon predictive control of nonlinear systems. The implementation of such a control strategy for real processes has greatly improved their performance. This paper deals with a model-based predictivecontrol (MBPC) strategy using a generalised Hammerstein model and its application to the temperature control of a semibatch reactor. Both unconstrained and constrained adaptive control problems are considered. A simple identification method based on the weighted recursive least squares method (WRLS) is used to estimate the model parameters on-line. An indirect adaptive nonlinear controller is designed by combining the predictive controller with an indirect parameter estimation algorithm. This adaptive scheme has been applied for the control of a semi-batch chemical reactor. Experimental results show that the performance of the generalised Hammerstein MBPC (NLMBPC) was significantly better than that of a linear model predictive controller (LMBPC). ID="A1"Correspondance and offprint requests to: Dr F. M'sahli, Department of Electrical Engineering, ISETKH, Avenue Hadj Ali Soua, 5070, Ksar Hellal, Monastir, Tunisia. E-mail: msahli-fn@iyahoo.fr     

基于遗传算法的AMT车辆起步模糊控制

通过对熟练驾驶员起步过程的分析 ,提出了能反映驾驶员意图的起步模糊控制策略。针对模糊控制器传统设计过程中存在的人为主观因素较多 ,难以进行优化等缺点 ,采用遗传算法对起步模糊控制器隶属函数参数进行优化。并为此建立了起步模糊控制系统仿真模型。用优化的模糊控制器进行实车道路试验 ,取得了满意的效果。     

填料塔热交换系统的模糊模型PID优化控制

建立了填料塔热交换实验装置的计算机控制系统.针对过程的严重非线性与不稳定性,在机理分析的基础上建立了分段非线性模型,提出了一种基于T-S模糊模型的非线性系统PID优化控制算法,设计了该装置的模糊模型PID优化控制器.实验结果与传统的PID控制结果进行对比表明,采用模糊模型PID优化控制方法能够有效解决非线性控制问题,控制效果令人满意.     

Design of interval type-2 fuzzy precompensated PID controller applied to two-DOF robotic manipulator with variable payload

The interval type-2 fuzzy logic controller (IT2-FLC), with footprint of uncertainty (FOU) in membership functions (MF), has increasingly recognized for controlling uncertainties and nonlinearities. Within the ambit of this, the efficient interval type-2 fuzzy precompensated PID (IT2FP-PID) controller is designed for trajectory tracking of 2-DOF robotic manipulator with variable payload. A systematic strategy for optimizing the controller parameters along with scaling factors and the antecedent MF parameters for minimization of performance metric integral time absolute error (ITAE) is presented. Prominently, recently proposed optimization technique hybridizing grey wolf optimizer and artificial bee colony algorithm (GWO–ABC) is utilized for solving this high-dimensional constrained optimization problem. In order to witness effectiveness, the performance is compared with type-1 fuzzy precompensated PID (T1FP-PID), fuzzy PID (FPID), and conventional PID controllers. More significantly, the robustness of IT2FP-PID is examined for payload variation, model uncertainties, external disturbance, and noise cancellation. After experimental outcome, it is inferred that IT2FP-PID controller outperforms others and can be referred as a viable alternative for controlling nonlinear complex systems with higher uncertainties.     

基于遗传优化的无人车横向模糊控制

以视觉导航式无人车DLUIV-1为控制对象,对其进行横向运动控制研究。建立视觉导航式无人车横向运动控制系统模型,对无人车的横向运动行为进行描述。在此基础上,分析预瞄距离对横向控制系统动态性能的影响,并建立考虑速度因素的预瞄距离计算公式。针对无人车具有非完整运动约束、高度非线性动态特性以及参数的不确定性等特点,提出基于遗传算法的无人车横向模糊控制策略,通过遗传算法对横向模糊控制器的隶属度函数参数和控制规则的自动优化,从而有效地确定出横向模糊控制器的隶属度函数和控制规则。最后通过仿真和实车试验对该横向模糊控制器进行验证和评价,仿真和试验结果表明,该横向控制器可保证无人车稳定准确地跟踪参考路径,且具有较强的鲁棒性。     

pH过程的FNNC-PI控制研究

在化工、生物及废水处理工程中 ,大量存在着的 p H过程需要得到精确的控制。由于 p H过程是一个具有严重非线性及滞后性的被控对象 ,传统的 PID控制或非线性 PID控制难以达到理想的控制效果。这里给出了一种模糊神经网络控制器与传统 PI相结合的控制方法 ,即 p H过程的 FNNC- PI控制方案 ,该方案能很好地处理 p H过程的严重非线性和滞后性 ,而且具有较强的鲁棒性和抗干扰能力。