新型小脑模型关联控制器复合控制在电动加载系统中的结构及算法

在电动加载系统中,多余力矩强扰动和其他非线性因素直接影响力矩跟踪精度,传统的控制方法很难得到满意的控制效果.本文分析了电动加载系统中多余力矩产生机理,提出了一种新型小脑模型关联控制器(CMAC)复合控制策略,并对其结构及算法进行了研究.在控制结构上以系统的指令输入和实际输出作为CMAC的激励信号,采用误差作为训练信号,并根据激励信号的特点,提出了非均匀量化的思想.不同于常规CMAC的误差平均分配,新型CMAC根据高斯权重系数来分配误差.动态仿真结果表明,该方法有效抑制了加载系统的多余力矩及摩擦等非线性因素干扰,提高了电动加载系统的控制精度,增强了系统的稳定性.     

CMAC-PID在柴油机调速系统中的应用

研究柴油机优化控制问题,柴油机调速控制要求快速、准确.针对柴油机调速系统的非线性、时变等特点,导致稳定时间长,传统PID控制不理想.为了提高自动调节供油量,达到控制准确度,提出一种小脑模型神经网络(CMAC)与PID并行控制的柴油机调速系统(CMAC-PID).采用现代控制理论与经典相结合的方法对柴油机调速系统进行优化设计,利用传统PID来实现柴油机调速属于反馈控制,保证了系统的稳定性,并且能够抑制扰动,结合CMAC神经网络对控制器进行前馈控制,确保系统的控制响应速度,减小超调量,提高控制精度.仿真结果表明,采用CMAC-PID算法控制精度更高,超调小,稳定时间短,鲁棒性强,能优化柴油机调速系统性能,为设计提供了参考.     

飞机空调车的模糊CMAC-PID温度控制器设计

关于飞机地面空调温度优化控制问题,飞机地面空调车温度控制系统具有参考模型不精确、非线性、时变、工作环境不稳定等特点。针对实际温度控制系统中应用到的传统PID温度控制器存在超调量大、响应速度慢、抗干扰能力弱等缺点,设计了一种新的响应速度快、稳定性高和抗干扰能力强的模糊CMAC-PID控制器。温度控制器利用小脑神经网络(CMAC)较强的自适应能力,与模糊PID控制器并行工作,能够迅速、精确、稳定的达到系统所要求的温度值。用Matlab软件进行实验,结果表明控制方式有效地改善了系统的动态性能、稳态精度和鲁棒性,具有较好的工程应用前景。     

气动人工肌肉关节的自抗扰控制

气动人工肌肉关节系统是一个强非线性时变自平衡2阶系统,鉴于传统控制方法难以克服控制精度、调节速度和稳定性之间的矛盾,设计了一种2阶自抗扰控制器.采用3阶扩张状态观测器实时估计未建模型部分及外界扰动,对系统进行线性补偿,并利用非线性反馈机制提高控制效率,在保证稳定性的前提下,提高调节速度和控制精度,使得系统的稳态精度小于...     

智能PID控制器优化仿真研究

研究PID控制器优化问题,现代工业控制过程中,由于许多被控对象受到于扰因素影响,具有高度非线性和不确定性,常规PID控制精度低,提出一种遗传算法、粒子群算法和RBF神经网络相融合的PID控制器设计方法(GA-PSO-RBF).首先采用遗传算法选择PID控制参数初始值,然后采用粒子群算法优化RBF神经网络参数,采用优后的RBF神经网络辨识控制对象的输出对输入的变化灵敏度,最后采用单神经元对PID控制器进行在线性调整,得到理想的控制效果.仿真结果表明,GA-PSO-RBF神经网络PID控制器的超调量小,响应速度快,提高了系统的控制精度.     

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。     

一种自适应CMAC在交流励磁水轮发电系统中仿真研究

在分析常规CMAC结构的基础上,针对一类非线性、参数时变和不确定的控制系统,提出了一种自适应CMAC神经网络的控制器．该控制器以系统动态误差和给定信号量作为CMAC的激励信号,并与自适应线性神经元网络相结合构成系统的复合控制．为了验证其有效性,将其应用到交流励磁水轮发电机系统的多变量非线性控制中,并与常规的PID控制效果进行了比较．仿真结果表明,该控制器具有较强鲁棒性和自适应能力,控制品质优良。     

非线性量化CMAC研究与应用

提出了基于非线性量化小脑模型神经网络（CMAC）算法,对CMAC的概念映射进行了自适应设计,提高CMAC的计算速度和精度以满足复杂动态环境下的非线性实时控制的需要。结合溶出预脱硅系统工艺优化的需求,提出了基于非线性量化CMAC的溶出预脱硅系统时间序列预测模型,用于准确实时地预测循环母液加入量,在此基础上进行循环母液投放措施优化。工业实验说明了该模型在对化工软计算的预测精度和快速性上具有明显的优越性,该模型已应用于某氧化铝厂工艺优化系统中动态调节循环母液投放量,节省了生产成本,取得了明显的经济效益。     

基于神经网络的自整定PID控制器设计

针对非线性时变系统,设计了一种基于神经网络的参数在线自整定PID控制器.该控制器采用基于最近邻聚类方法的RBF神经网络快速学习算法,通过实时在线辨识,建立被控系统的精确模型并得到准确的Jacobian信息;同时将此信息提供给BP神经网络,从而实现PID控制器参数的自动在线整定. 仿真结果表明,该方法提高了算法的精度和速度并具有较快的系统响应和良好的跟踪特性.     

基于RBF-PID的水轮发电机组控制系统

研究水轮发电机组稳定性控制优化问题,水轮发电机组是一个非线性、时变的复杂控制系统,很难建立精确模型。采用常规PID控制策略难以较高的控制精度,超调量大。为提高水轮发电机组控制精度,将自学习较强的RBF神经网络与常规PID相结合,提出一种基于RBF-PID组合的水轮发电机组控制算法。采用RBF神经网络对水轮发电机组控制系统的Jacobian矩阵信息进行在线辨识,实现RBF-PID参数在线自整定。仿真结果表明:RBF-PID组合控制器不仅提高控制系统的精度,而且超调量小、抗扰动能力强,能够很好实现水轮发电机组的稳定性优化控制。     

非线性量化小脑模型神经网络在溶出循环母液配比模型中的应用

采用自适应算法对小脑模型神经网络的概念映射进行设计,提出了非线性量化小脑模型神经网络算 法,提高小脑模型神经网络的计算速度和精度以满足复杂动态环境下的非线性实时控制的需要．提出了基于非线性 量化小脑模型神经网络的溶出预脱硅系统时间序列预测模型,用于准确实时地预测循环母液加入量,在此基础上进 行循环母液投放措施优化．试验说明了该模型在对化工软计算的预测精度和快速性上具有明显的优越性,本模型已 应用于某氧化铝厂工艺优化系统中,动态调节循环母液投放量以节省原料．     

基于小脑模型关节控制器与PID复合的高速公路交通流密度控制

高速公路交通控制系统是一个复杂的非线性时变系统, 传统的匝道控制方法难以取得满意的控制效果. 为此, 本文提出基于小脑模型关节控制器(CMAC)与PID复合的匝道控制方法. 首先建立了二阶宏观动态交通流模型, 然后研究了CMAC与PID复合控制算法, 结合非线性反馈理论, 设计了基于CMAC与PID复合的高速公路交通流密度控制器, 该密度控制问题是一个输出跟踪和扰动抑制问题, 最后采用两个仿真实例对该方法的有效性进行验证. 结果表明, 复合控制具有优越的密度跟踪性能和抑制噪声干扰的能力; 复合控制方法能够有效地消除交通拥挤, 并使主线车流趋于稳定.     

基于信度分配的并行集成CMAC及其在建模中的应用

Albus CMAC(cerebella model articulation controller) 神经网络是一种模拟人类小脑学习结构的小脑模型关节控制器, 它具有很强的记忆与输出泛化能力, 但对于在线学习来说, Albus CMAC仍难满足快速性的要求. 本文在常规CMAC神经网络的基础上, 针对其在学习精度与存储容量之间的矛盾, 引入信度分配概念, 提出了一种基于信度分配的并行集成CMAC. 它将大规模网络切割为多个子网络分别训练后再组合, 大大地提高了计算效率. 通过对复杂非线性函数建模的仿真研究表明, 该方案提高了系统建模的泛化能力和算法的收敛速度. 文章最后讨论了学习常数和泛化参数对该神经网络在线学习效果的影响.     

Hierarchically clustered adaptive quantization CMAC and its learning convergence.

The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC), learning convergence, nonuniform quantization.     

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.     

非线性系统的再励学习控制研究

研究了一种带有的CMAC神经网络的再励学习（RL）控制方法,以解决具有高度非线性的系统控制问题。研究的重点在于算法的简化以及具有连续输出的函数学习上。控制策略由两部分构成;再励学习控制器和固定增益常规控制器。前者用于学习系统的非线性,后者用于稳定系统。仿真结果表明,所提出的控制策略不仅是有效的,而且具有很高的控制精度。     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

Missile guidance law design using adaptive cerebellar model articulation controller

An adaptive cerebellar model articulation controller (CMAC) is proposed for command to line-of-sight (CLOS) missile guidance law design. In this design, the three-dimensional (3-D) CLOS guidance problem is formulated as a tracking problem of a time-varying nonlinear system. The adaptive CMAC control system is comprised of a CMAC and a compensation controller. The CMAC control is used to imitate a feedback linearization control law and the compensation controller is utilized to compensate the difference between the feedback linearization control law and the CMAC control. The online adaptive law is derived based on the Lyapunov stability theorem to learn the weights of receptive-field basis functions in CMAC control. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Then the adaptive CMAC control system is designed to achieve satisfactory tracking performance. Simulation results for different engagement scenarios illustrate the validity of the proposed adaptive CMAC-based guidance law.