A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes

We develop a novel adaptive tuning method for classical proportional–integral–derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input–output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.     

基于多分支RNN快速学习算法的混沌时间序列预测

针对传统递归神经网络中出现的网络结构与计算复杂性,提出了使用多分支递归神经网络学习算法,并将其应用到混沌时间序列预测领域。首先缩减了部分冗余的分支,只保留了节点与自身之间以及节点与代表以后时刻的节点之间的分支;然后使用规则导数代替惯用的一般偏导数,有助于同时反映权值对目标函数的直接影响和间接影响;最后使学习率根据学习情况进行动态调整,有助于加快学习算法的收敛速度。仿真实验表明,当参数的选取合理时,多分支递归神经网络能够达到较高的性能。     

用连续回归神经网络求解泛函极值问题

针对信息科学和控制理论中经常涉及的一类泛函极值问题,提出基于连续回归神经网络的求解方法。推导了求解泛函的连续BPTT算法,进而对该算法进行改进,得出一种在线学习算法,为并行实现打下了基础．     

Discrete state space modeling and control of nonlinear unknown systems

A novel procedure for integrating neural networks (NNs) with conventional techniques is proposed to design industrial modeling and control systems for nonlinear unknown systems. In the proposed approach, a new recurrent NN with a special architecture is constructed to obtain discrete-time state-space representations of nonlinear dynamical systems. It is referred as the discrete state-space neural network (DSSNN). In the DSSNN, the outputs of the hidden layer neurons of the DSSNN represent the system's (pseudo) state. The inputs are fed to output neurons and the delayed outputs of the hidden layer neurons are fed to their inputs via adjustable weights. The discrete state space model of the actual system is directly obtained by training the DSSNN with the input–output data. A training procedure based on the back-propagation through time (BPTT) algorithm is developed. The Levenberg–Marquardt (LM) method with a trust region approach is used to update the DSSNN weights. Linear state space models enable to use well developed conventional analysis and design techniques. Thus, building a linear model of a system has primary importance in industrial applications. Thus, a suitable linearization procedure is proposed to derive the linear state space model from the nonlinear DSSNN representation. The controllability, observability and stability properties are examined. The state feedback controllers are designed with both the linear quadratic regulator (LQR) and the pole placement techniques. The regulator and servo control problems are both addressed. A full order observer is also designed to estimate the state variables. The performance of the proposed procedure is demonstrated by applying for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems.