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1.
基于T-S模糊模型的非线性预测控制策略   总被引:15,自引:1,他引:15  
提出了一种新的基于T-S模糊模型的非线性预测控制策略. T-S模糊模型用于描述对象的非线性动态特性, 通过将模糊模型的输出反馈回来作为模型输入, 从而构成了模糊多步预报器. 由于T-S模糊模型每条规则的结论部分是一个线性模型, 因此整个模糊模型可以看作一个线性时变系统, 从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题, 以方便求解. pH中和过程的仿真结果表明其性能优于传统的动态矩阵控制器.  相似文献   

2.
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.  相似文献   

3.
针对炉窑温度系统的大时滞、多扰动和非线性的特点,将T-S模糊状态空间模型作为预测控制的预测模型,并将T-S模糊表示的非线性系统转化为线性时变系统,给出了基于状态空间的多变量复杂系统的T-S模糊模型表达形式,设计出预测时域内多模型的非线性模糊预测控制器。根据实际控制中对控制量和输出的约束,将控制器输出求解转化为二次规划问题。  相似文献   

4.
基于T-S 模型和小世界优化算法的广义非线性预测控制   总被引:1,自引:0,他引:1  
提出一种新型的基于T-S模糊模型和小世界优化算法的广义非线性预测控制策略.采用基于混沌遗传算法的T-S模糊模型描述复杂非线性系统的动态特性,构成模糊多步预报器.同时,针对现有基于二进制和十进制编码小世界优化算法运行时间长等缺点,提出一种新型的基于实数编码的小世界优化算法,函数测试和应用于非线性预测控制的滚动优化反映了其较强的寻优能力.最后,将其应用于基于实际数据的T-S模糊模型的广义非线性预测控制,满足了系统实时性和快速稳定性的要求.  相似文献   

5.
基于T-S 模型的模糊预测控制研究   总被引:13,自引:1,他引:13  
提出一种基于T—S模型的模糊预测控制策略.利用模糊聚类算法高线辨识T—S模型,采用带遗忘因子的递推最小二乘法进行模型参数的选择性在线学习;对模糊模型在每一采样点进行线性化,将T—S模型表示的非线性系统转化为线性时变状态空间模型,并将约束非线性优化问题转化为线性二次规划问题,解决了非线性预测控制中如何获得非线性模型和非线性优化在线求解的难题.将预测域内的线性模型序列作为预测模型,减小了模型误差,提高了控制性能.pH中和过程的仿真验证了该方法的有效性.  相似文献   

6.
质子交换膜燃料电池(PEMFC)是21世纪最有前景的发电技术之一。该文针对PEMFC等一类具有严重非线性的复杂被控对象,提出一种基于模糊模型的非线性预测控制算法对PEMFC系统进行建模与控制。在建模与控制过程中,采用模糊聚类和线性辨识方法在线建立PEMFC控制系统的T-S模糊预测模型,然后基于分支定界法的基本原理对控制量进行离散寻优,从而实现PEMFC的非线性预测控制。仿真和实验结果证明了该算法的有效性和优越性。  相似文献   

7.
针对传统的模型预测控制不能很好解决具有严重非线性、不确定性的对象或过程的控制问题。提出将模糊模型用于描述对象的非线性动态特性。通过将模糊模型的输出反馈回来作为模型输入,从而构成了模糊多步预测器,采用一种收敛精度高、速度快的具有最优保留特性遗传算法(EGA)依据模型预测输出在线滚动求解控制律的非线性预测控制算法。仿真结果表明该算法对一类非线性系统具有较快的响应速度和较强的抗干扰能力。  相似文献   

8.
针对离散时间非线性系统,提出了一种基于T-S模糊模型的自适应预测函数控制算法。该算法利用加权递推最小二乘法在线辨识T-S模糊模型后件参数,以克服模型失配对系统性能的影响。根据辨识得到的模型参数直接递推计算模型的预测输出,而不需要求解Diophantine方程,进而直接递推求解预测控制律,而不需要求解矩阵逆。仿真结果表明,该算法具有良好的跟踪性能和较强的鲁棒性。  相似文献   

9.
多变量非线性系统的模糊内模控制   总被引:2,自引:0,他引:2  
靳其兵  林艳春  袁琴  赵大力 《计算机仿真》2007,24(2):134-136,190
大多数的先进控制器是基于线性模型的,它们对化学工业中常见的非线性过程的控制效果并不能达到最优.因此,考虑使用非线性模型,以使控制性能获得改善.用基于T-S模型的自适应模糊聚类辨识算法对系统进行辨识.T-S模型是用线性的方程来描述非线性系统,从而利于求出模型的逆.而模型逆又是IMC的关键一步,因此选用这种基于T-S模糊模型的控制器(FIMC)来实现对非线性多变量系统的控制.对2输入2输出的非线性系统进行仿真,结果表明FIMC在多变量系统中可以实现好的控制.  相似文献   

10.
提出全航速减摇鳍控制策略,对低航速和中航速切换策略进行研究.采用升力反馈封装鳍角到升力的不确定性,避免在零航速和低航速下复杂的控制规律;同时,基于T-S模型将横摇非线性不确定系统转化为线性时变系统,并将控制中的约束非线性优化问题转化为二次规划问题,避免了预测控制中的非线性优化在线求解.以某船为例,给出了低中航速的切换控制策略,仿真结果表明了该方法可以使减摇鳍满足全航速减摇要求.  相似文献   

11.
In this work a Model Predictive Control (MPC) approach is used for controlling a Pulsed Electrochemical Machining (PECM) process. The MPC problem is formulated in order to optimally reach a desired state while satisfying various restrictions. PECM is modeled as a constrained nonlinear system. In the first approach the system is input-output linearized and a linear MPC scheme is applied to control it. In comparison a second approach uses the linearization around the current working point resulting in a Linear Time Variant system. This linear system is controlled using Linear Time Variant MPC (LTV-MPC). The simulation results are compared and the most promising controller is implemented on a real time platform controlling a PECM plant. The experimental results with online parameter estimation are shown and discussed.  相似文献   

12.
A neurofuzzy scheme has been designed to carry out on-line identification, with the aim of being used in an adaptive–predictive dynamic matrix control (DMC) of unconstrained nonlinear systems represented by a transfer function with varying parameters. This scheme supplies to the DMC controller the linear model and the nonlinear output predictions at each sample instant, and is composed of two blocks. The first one makes use of a fuzzy partition of the external variable universe of discourse, which smoothly commutes between several linear models. In the second block, a recurrent linear neuron with interpretable weights performs the identification of the models by means of supervised learning. The resulting identifier has several main advantages: interpretability, learning speed, and robustness against catastrophic forgetting. The proposed controller has been tested both on simulation and on a real laboratory plant, showing a good performance.  相似文献   

13.
Recently, a linear Model Predictive Control (MPC) suitable for closed-loop re-identification was proposed, which solves the potential conflict between the persistent excitation of the system (necessary to perform a suitable identification) and the control, and guarantees recursive feasibility and attractivity of an invariant region of the closed-loop. This approach, however, needs to be extended to account for a proper robustness to moderate-to-severe model mismatches, given that re-identifications are necessary when the system is not close to the operating point where the current linear model was identified. In this work, new results on robustness are presented, and an exhaustive application of the new MPC suitable for closed-loop re-identification to a nonlinear polymerization reactor simulator is made to explore the difficulties arising from a real life identification. Furthermore, several closed-loop re-identification are performed in order to clearly show that the proposed controller provides uncorrelated input–output data sets, which together with the guaranteed stability, constitute the main controller benefit.  相似文献   

14.
This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.  相似文献   

15.
In the paper, a well-known predictive functional control strategy is extended to nonlinear processes. In our approach the predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The prediction is based on a global linear model in the state space domain. The global linear model is obtained by the fuzzy model in Takagi–Sugeno form and actually represents a model with changeable parameters. A simulation of the system, which exhibits a strong nonlinear behaviour together with underdamped dynamics, has evaluated the proposed fuzzy predictive control. In the case of underdamped dynamics, the classical formulation of predictive functional control is no longer possible. That was the main reason to extend the algorithm into the state space domain. It has been shown that, in the case of nonlinear processes, the approach using the fuzzy predictive control gives very promising results.  相似文献   

16.
Recurrent neuro-fuzzy networks for nonlinear process modeling   总被引:14,自引:0,他引:14  
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.  相似文献   

17.
Hybrid Fuzzy Modelling for Model Predictive Control   总被引:1,自引:0,他引:1  
Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.  相似文献   

18.
预测模糊控制在聚合反应釜中的仿真研究   总被引:1,自引:0,他引:1  
聚合反应釜温度控制系统的数学模型具有非线性、大惯性、纯滞后以及时变等特点,传统的PID控制依赖于被控对象的精确数学模型,很难达到令人满意的控制效果。模糊控制和预测控制都是对不确定系统进行控制的有效方法,而预测模糊控制作为二者相结合的产物,可进一步提高控制的效果。该文提出了将预测模糊控制运用于聚合反应釜温度控制器的设计,为聚合反应釜的控制提出了一种新算法。理论分析和基于MATLAB的仿真研究表明,该控制方法具有使系统超调量小、调整时间短、对系统参数变化和外界干扰有较强的鲁棒性等优点,是一种提高聚合反应釜温度控制效果的有效方法。  相似文献   

19.
The performance of model-based controller design relies heavily on the quality and suitability of the utilized process model. This contribution proposes a fuzzy network based nonlinear controller design methodology. Fuzzy networks are a model approach combining high approximation quality with high interpretability. The input/output (I/O) models commonly used for identification are transformed to fuzzy state-space models. Transferring and adjusting methods from linear state-space theory a control concept consisting of a fuzzy state controller and an adaptive set-point filter for nonlinear dynamic processes is deduced. The capability of the method is demonstrated for a hydraulic drive  相似文献   

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