Bioreactor temperature profile controller using inverse neural network (INN) for production of ethanol

This paper presents the use of inverse neural networks (INN) for temperature control of a biochemical reactor and its effect on ethanol production. The process model is derived indicating the relationship between temperature, pH and dissolved oxygen. Using fundamental model obtained data sets; an inverse neural network has been trained using the back-propagation learning algorithm. Two types of temperature profile are used to compare the performance of the INN and conventional PID controllers. These controllers have been simulated in MATLAB for a quantitative comparison. The results obtained by the neural network based INN controller and by the PID controller are presented and compared. There is an improvement in the performance of INN controller in settling time and dead time and steady state error over the PID controller.     

Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC). Basically, mobile robots are required to work and navigate under exigent circumstances where the environment is hostile, full of disturbances such as holes and stones. The robot navigation leads to an autonomous decision making to overcome an obstacle and/or to stop the engine to protect it. In fact, the actuators that drive the robot should in no way be damaged and should stop to change direction in case of insurmountable disturbances. In this context, two controllers are implemented and a comparative study is carried out to demonstrate the effectiveness of the proposed approaches. For the first one, neural networks are used to optimize the parameters of a PID controller and for the second a fuzzy inference system type Mamdani based controller is adopted. The goal is to implement control algorithms for safe robot navigation while avoiding damage to the motors. In these two control cases, the smart robot has to quickly perform tasks and adapt to changing environment conditions while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulations results aren't done in real environments, but are obtained with the Matlab/Simulink environment in which holes and stones are modeled by different load torques and are applied as disturbances on the mobile robot environment. These simulation results and the robot performances are satisfactory and are compared to a PID controller in which parameters are tuned by the Ziegler–Nichols tuning method. The applied methods have proven to be highly robust.     

Neural network-based asymptotic tracking control of unknown nonlinear systems with continuous control command

ABSTRACT

This paper proposes a robust tracking controller for a class of nonlinear second-order systems with time-varying uncertainties. The controller is mainly based on the robust integral of the sign of the error (RISE) control approach to achieve an asymptotic stability result with a continuous control command in the presence of additive uncertainties. An adaptive feedforward neural network control term is blended with a new RISE controller to improve the system's transient performance. The proposed RISE controller is a modified version of the existing saturated RISE controller such that only sign of the derivative of the output is needed. The stability of the closed-loop system is well studied, where a local asymptotic stability is proven. The controller performance is validated through simulations on a two-degree-of-freedom lower limb robotic exoskeleton.     

Development of nonlinear disturbance observer based control and nonlinear PID: A personal note

This paper gives an overview of early development of nonlinear disturbance observer design technique and the disturbanceobserver based control (DOBC) design. Some critical points raised in the development of the methods have been reviewed anddiscussed which are still relevant for many researchers or practitioners who are interested in this method. The review is followedby the development of a new type of nonlinear PID controller for a robotic manipulator and its experimental tests. It is shown that,under a number of assumptions, the DOBC consisting of a predictive control method and a nonlinear disturbance observer couldreduce to a nonlinear PID with special features. Experimental results show that, compared with the predictive control method,the developed controller significantly improves performance robustness against uncertainty and friction. This paper may triggerfurther research and interests in the development of DOBC and related methods, and building up more understanding betweenthis group of control methods with comparable ones (particularly control methods with integral action).     

CMAC神经网络与PID复合控制的应用研究

阐述了CMAC神经网络的基本原理,并结合PID控制的特点,将CMAC神经网络与PID复合控制算法应用在工业领域的温度控制系统中,并同传统的Zieglar-Nichols阶跃响应法及单纯形算法作了比较。仿真结果表明:该复合控制算法具有较高的控制精度及良好的控制效果。     

Identification and control of nonlinear systems by a time-delay recurrent neural network

In this paper, we first present a novel time-delay recurrent neural network (TDRNN) model by introducing the time-delay and recurrent mechanism. The proposed TDRNN model has special advantages such as simple structure, deeper depth and higher resolution ratio in memory. Thereafter, we develop the dynamic recurrent back-propagation algorithm for the TDRNN. To guarantee the fast convergence, the optimal adaptive learning rates are also derived in the sense of discrete-type Lyapunov stability. More specifically, a TDRNN identifier and a TDRNN controller are constructed to perform the identification and control of the nonlinear systems. Numerical experiments show that the TDRNN model has good effectiveness in the identification and control for dynamic systems.     

A constructive approach for an optimal control applied to a class of nonlinear time delay systems

In this contribution, we obtain a nonlinear controller for a class of nonlinear time delay systems, by using the inverse optimality approach. We avoid the solution of the Hamilton Jacobi Bellman type equation and the determination of the Bellman's functional by extending the inverse optimality approach for delay free nonlinear systems to time delay nonlinear systems. This is achieved by combining the Control Lyapunov Function framework and Lyapunov-Krasovskii functionals of complete type. Explicit formulas for an optimal control are obtained. The efficiency of the proposed method is illustrated via experimental results applied to a dehydration process whose model includes a delayed state linear part and a delayed nonlinear part. To give evidence of the good performance of the proposed control law, experimental comparison against an industrial Proportional Integral Derivative controller and optimal linear controller. Additionally experimental robustness tests are presented.     

Adaptive L2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set.Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.     

Control of a distillation column by type-2 and type-1 fuzzy logic PID controllers

The aim of this paper is to develop a type-1 and a type-2 fuzzy logic PID controller (type-1 FLC and type-2 FLC, respectively) for the control of a binary distillation column, the mathematical model of which is characterized by both high nonlinearities and parameter uncertainties. Attention was focused on the tuning procedure proposed by the authors and representing a development of the original Jantzen [1] method for type-1 and type-2 fuzzy controllers, in particular including input type-2 Gaussian membership functions. A theoretical explanation of the differences in fuzzy controller performance was in fact provided in the light of simulation results. The performance of a type-1 FLC was then compared in simulation with the one of type-2 FLC. All the simulation results confirmed the robustness and the effective control action of each fuzzy controller, with evident advantages for the type-2 FLC.     

基于CMAC神经网络与PID的并行控制器设计与应用

提出一种基于CMAC神经网络与PID的并行控制器的设计方法,利用传统PID实现反馈控制,保证系统的稳定性,且抑制扰动,利用CMAC神经网络控制器实现前馈控制,确保系统的控制精度和响应速度。该算法直接应用于控制直流电机调速系统,仿真结果表明,与传统数字PID控制算法相比较,该并行控制算法增强了系统的控制精度,提高了系统的响应速度,并且具备较强的抗干扰能力和鲁棒性。     

一类非线性时滞系统的自适应动态面控制

本文对于一类含不确定输入时滞和干扰的非线性系统的跟踪控制问题提出了一种自适应动态面控制方案. 利用动态面控制方法避免了传统的后推设计中存在的复杂度爆炸问题. 分别构造了一个滤波器和一个虚拟观测器来产生辅助信号. 采用神经网络来逼近未知的连续函数. 跟踪误差被证明最终收敛到一个足够小的紧集. 给出了一个数字仿真示例验证了理论结果.     

Adaptive neural network sliding mode control of a nonlinear two-degrees-of-freedom helicopter system

The helicopter can play an important role in military and civil applications owing to its super maneuvering ability, which is closely related to its control system. To improve control performance, this study presents an adaptive sliding mode control strategy merging an adaptive neural network for a nonlinear two-degrees-of-freedom (2-DOF) helicopter system. By setting up the Lyapunov function, the asymptotic stability of the closed-loop system is guaranteed, the astringency of the neural network weight renewal course is pledged, and the asymptotic attitude adjustment and trajectory tracking for the desired set point are realized. The availability of the adaptive radial basis function sliding mode control is finally verified via the simulation and real implementation on a nonlinear 2-DOF helicopter platform.     

Design and Implementation of a Hierarchical‐Clustering CMAC PID Controller

In industrial control processes, proportional‐integral‐derivative (PID) control algorithm is widely employed. Therefore, it is meaningful to design advanced PID controllers, especially for nonlinear control objects. One of the advanced PID controllers is a cerebellar model articulation controller (CMAC) PID controller. In this controller, the PID control parameters are calculated and tuned. The CMAC achieves a higher accuracy by increasing the number of labels of each weight table; this requires a larger memory, and the generalization ability of the controller decreases. On the other hand, if the CMAC requires less memory, the generalization ability increases and accuracy decreases. Hence, in this paper, a novel CMAC in which the accuracy is compatible with the generalization ability is proposed in this paper. In the proposed CMAC, the number of labels of each weight table can be decided by using a hierarchical clustering technology. Moreover, the efficiency of the memory allocation is improved. The effectiveness of the proposed method is verified by experiments.     

一类非线性离散系统自适应准滑模控制

针对一般非线性离散时间系统的不确定性和扰动抑制问题, 提出一种新的自适应准滑模控制算法. 算法包括两部分, 其一是基于紧格式动态线性化模型的自适应准滑模控制器设计, 其中动态线性化方法中“伪偏导数”的估计算法仅依赖于系统I/O 实时量测值. 其二是采用径向基神经网络估计器来估计系统的综合不确定性. 理论分析证明了系统的BIBO稳定性. 仿真结果验证了所提算法的有效性.     

一类非线性系统基于Backstepping的自适应鲁棒神经网络控制

针对一类未知非线性系统提出了一种基于Backstepping的自适应神经网络控制方法, 放松了满足匹配条件, 要求神经网络逼近误差的边界已知等一些限制性的假设. 扩展了自适应backstepping和自适应神经控制的适用范围, 整个闭环系统表明是最终一致有界的, 跟踪误差收敛于原点的一个大小可调的邻域.     

A comparative study on a novel model‐based PID tuning and control mechanism for nonlinear systems

This work presents a novel predictive model‐based proportional integral derivative (PID) tuning and control approach for unknown nonlinear systems. For this purpose, an NARX model of the plant to be controlled is obtained and then it used for both PID tuning and correction of the control action. In this study, for comparison, neural networks (NNs) and support vector machines (SVMs) have been used for modeling. The proposed structure has been tested on two highly nonlinear systems via simulations by comparing control and convergence performances of SVM‐ and NN‐Based PID controllers. The simulation results have shown that when used in the proposed scheme, both NN and SVM approaches provide rapid parameter convergence and considerably high control performance by yielding very small transient‐ and steady‐state tracking errors. Moreover, they can maintain their control performances under noisy conditions, while convergence properties are deteriorated to some extent due to the measurement noises. Copyright © 2009 John Wiley & Sons, Ltd.     

Direct adaptive neural network controller for a class of nonlinear systems based on fuzzy estimator of the control error

A new approach of direct adaptive control of single input single output nonlinear systems in affine form using single-hidden layer neural network (NN) is introduced. In contrast to the algorithms in the literature, the weights adaptation laws are based on the control error and not on the tracking error or its filtered version. Since the control error is being expressed in terms of the NN controller, hence its weights updating laws are obtained via back-propagation concept. A fuzzy inference system (FIS) with heuristically defined rules is introduced to provide an estimate of this error based on the past history of the system behaviour. The stability of the closed loop is studied using Lyapunov theory. A fixed structure is then proposed for the FIS and the design parameters reduce to the parameters of the NN. The method is reproducible and does not require any pre-training of the network weights.     

一类非线性切换系统的自适应神经动态面控制

针对一类严格反馈型不确定非线性切换系统,提出了一种鲁棒自适应神经动态面跟踪控制方案.该方案在基于共同Lyapunov函数的后推法设计中引入动态面控制(dynamic surface control,DSC)技术,利用径向基神经网络逼近构造的未知共同上界函数,并将滤波器输出导数取代传统中间变量作为神经网络输入,降低了网络输入维度;同时利用Young’s不等式技术有效减少了神经网络控制器的可调参数数目.此外,理论证明了该控制方案可以保证在任意切换下闭环系统所有信号半全局一致终结有界,且跟踪误差在有限时间收敛到零的小邻域内.实验结果表明了所提方法达到了很好的跟踪性能.     

一类不确定非线性系统反步自适应神经网络控制研究

针对合有高阶不确定扰动项且不可参数线性化的一类非线性系统，采用反步递推方法设计基于多层神经网络的自适应控制器，多层神经网络可较好地逼近非线性系统，其权值能在系统先验知识不多的情况下在线调整，给出了神经网络Lyapunov意义下稳定的在线自适应律，在设计控制器的过程中，采用类加权形式Lyapunov函数，使得控制器能有效处理自适应控制奇异性问题，仿真结果表明，该控制器对系统参数的不确定性和有界干扰具有一定的鲁棒性，并能保证闭环系统全局稳定。     

Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot

An adaptive controller of nonlinear PID-based analog neural networks is developed for the velocity- and orientation-tracking control of a nonholonomic mobile robot. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. It is appropriate for a kind of plant with nonlinearity uncertainties and disturbances. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity- and orientation-tracking control of the nonholonomic mobile robot. The effectiveness of the proposed control algorithm is demonstrated through the simulation experiment, which shows its superior performance and disturbance rejection.