Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.     

New recursive-least-squares algorithms for nonlinear active controlof sound and vibration using neural networks

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.     

Fast Affine Projection Algorithms for Filtered-x Multichannel Active Noise Control

In recent years, affine projection algorithms have been proposed for adaptive system applications as an efficient alternative to the slow convergence speed of least mean square (LMS)-type algorithms. Whereas much attention has been focused on the development of efficient versions of affine projection algorithms for echo cancellation applications, the similar adaptive problem presented by active noise control (ANC) systems has not been studied so deeply. This paper is focused on the necessity to reduce even more the computational complexity of affine projection algorithms for real-time ANC applications. We present some alternative efficient versions of existing affine projection algorithms that do not significantly degrade performance in practice. Furthermore, while in the ANC context the commonly used affine projection algorithm is based on the modified filtered-x structure, an efficient affine projection algorithm based on the (nonmodified) conventional filtered-x structure, as well as efficient methods to reduce its computational burden, are discussed throughout this paper. Although the modified filtered-x scheme exhibits better convergence speed than the conventional filtered-x structure and allows recovery of all the signals needed in the affine projection algorithm for ANC, the conventional filtered-x scheme provides a significant computational saving, avoiding the additional filtering needed by the modified filtered-x structure. In this paper, it is shown that the proposed efficient versions of affine projection algorithms based on the conventional filtered-x structure show good performance, comparable to the performance exhibited by the efficient approaches of modified filtered-x affine projection algorithms, and also achieve meaningful computational savings. Experimental results are presented to validate the use of the algorithms introduced in the paper for practical applications.      

一种非线性PID控制算法的仿真研究

研究PID控制系统优化问题,工业控制被控对象均具有非线性、时变和大时滞性,引起系统的品质性能差,传统的线性控制难以达到所要求精度。为了提高系统控制精度,利用PID控制器各增益参数与偏差信号间的非线性关系,提出一种非线性PID控制算法。首先将PID参数转化为优化问题,然后采用粒子群算法的全局、并行搜索能力对非线性控制参数进行求解,得到一组最优的PID控制参数。仿真结果表明,相对于传统线性PID控制,非线性PID控制器超调小,调节时间短,并提高了控制精度,有效解决了传统PID难以准确控制非线性对象的难题。     

Functional link artificial neural network applied to active noise control of a mixture of tonal and chaotic noise

Many practical noises emanating from rotating machines with blades generate a mixture of tonal and the chaotic noise. The tonal component is related to the rotational speed of the machine and the chaotic component is related to the interaction of the blades with air. An active noise controller (ANC) with either linear algorithm like filtered-X least mean square (FXLMS) or nonlinear control algorithm like functional link artificial neural network (FLANN) or Volterra filtered-X LMS (VFXLMS) algorithm shows sub-optimal performance when the complete noise is used as reference signal to a single controller. However, if the tonal and the chaotic noise components are separated and separately sent to individual controller with tonal to a linear controller and chaotic to a nonlinear controller, the noise canceling performance is improved. This type of controller is termed as hybrid controller. In this paper, the separation of tonal and the chaotic signal is done by an adaptive waveform synthesis method and the antinoise of tonal component is produced by another waveform synthesizer. The adaptively separated chaotic signal is fed to a nonlinear controller using FLANN or Volterra filter to generate the antinoise of the chaotic part of the noise. Since chaotic noise is a nonlinear deterministic noise, the proposed hybrid algorithm with FLANN based controller shows better performance compared to the recently proposed linear hybrid controller. A number of computer simulation results with single and multitone frequencies and different types of chaotic noise such as logistic and Henon map are presented in the paper. The proposed FLANN based hybrid algorithm was shown to be performing the best among many previously proposed algorithms for all these noise cases including recorded noise signal.     

基于多模型的非线性系统自适应最小方差控制

对于一类典型的离散时间非线性系统, 提出了一种基于多模型的自适应最小方差控制方法. 通过在平衡点附近建立线性模型, 用径向基函数神经元网络来补偿建模误差和未建模动态, 形成了非线性系统的多模型表示. 采用了具有积分性质的切换指标函数作为切换法则和最小方差的控制方法构成了多模型自适应控制器. 仿真实验的结果表明了这种方法的有效性.     

A Study on Kinematics and Workspace Determination of a General 6-<Emphasis Type="Underline">P</Emphasis> US Robot

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.     

基于动态递归神经网络的自适应PID控制

提出一种基于动态递归神经网络的自适应PID控制方案,该控制系统由神经网络辨识器和神经网络控制器组成。辨识器采用单隐层的动态递归神经网络,网络结构为2-4-1;辨识算法为动态BP算法;控制器采用两层线性结构的神经网络,输入为系统偏差及其一阶、二阶微分,因此具有增量型PID控制结构。应用该控制系统对一非线性时变系统进行仿真研究,仿真结果表明该控制方案不仅具有良好的跟踪特性,而且对系统参数变化具有较强的鲁棒性。     

Multi-model predictive control of an industrial C3/C4 splitter

In this paper, the application of a linear predictive controller to an industrial distillation column that presents a nonlinear behavior is described. The system is represented by a set of linear approximating models, where each model corresponds to a possible operating point of the system. The control sequence computed by the control algorithm is based on a min–max optimization problem where the controller cost is minimized for the worst process model. The control algorithm makes use of a particular form of the state-space model, which preserves the structure of conventional model predictive control controllers that are based on the step response model. The performance of the proposed controller applied to an industrial system is illustrated with results of the real system at typical plant conditions with the controller performing as a regulator and as an output reference tracker.     

Synthesis of the sliding-mode neural network controller for unknown nonlinear discrete-time systems

This paper develops a sliding-mode neural network controller for a class of unknown nonlinear discrete-time systems using a recurrent neural network (RNN). The control scheme is based on a linearized expression of the nonlinear system using a linear neural network (LNN). The control law is proposed according to the discrete L yapunov theory. With a modified real-time recurrent learning algorithm, the RNN as an estimator is used to estimate the unknown part in the control law in on-line fashion. The stability of the control system is guaranteed owing to the on-line learning ability of the RNN algorithm. The proposed control scheme is applied to numerical problems and simulation results that it is very effective.     

Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

Multiple model adaptive control for a class of nonlinear systems with unknown control directions

This study introduces an improved multiple model adaptive control (MMAC) algorithm for a class of nonlinear discrete-time systems. The controller consists of a linear direct adaptive controller, a neural network-based nonlinear direct adaptive controller and a switching mechanism. The assumption of the nonlinear term is relaxed by incorporating a parameter estimator with an augmented error. The control direction of the system is not required to be known by employing a linear direct adaptive controller with the discrete Nussbaum gain and future output predictions. The stability of the closed-loop systems applying the proposed MMAC method is proved and the improved transient performance of the system is illustrated by the simulation results.     

基于多模型的非线性系统广义预测控制

对于复杂的离散时间非线性系统，提出一种基于多模型的广义预测控制方法．通过在平衡点附近建立线性模型，并用径向基函数神经网络来补偿匹配误差，形成了非线性系统的多模型表示，然后采用模糊识别方法作为切换法则，并结合广义预测控制构成了多模型广义预测控制器．通过对连续发酵过程的计算机仿真，表明了该方法的有效性．     

线性时变系统的分散自校正控制*

本文对由存在有界扰动和非线性关联的子系统组成的线性时变离散大系统提出了一种多项式逼近的分散自校正控制算法。也可用神经网络去实现自校正控制，采用神经网络作为预报和控制器，网络学习算法用Ｗｉｄｒｏｗ－Ｈｏｆｆ规则。证明了这种分散自校正控制系统的稳定性，仿真结果表明系统是稳定的，可以很好地跟踪期望输出。     

一种新型发动机主动悬置研究

该文介绍了一种采用压电技术的新型发动机主动悬置(ACM),采用基于同步滤波-xLMS算法的自适应算法进行控制。计算机仿真结果表明,主动悬置的应用对车辆怠速振动和轰鸣噪声有明显的抑制作用。     

控制面故障下的飞机运动建模与重构控制能力分析及设计

对控制面故障影响飞机运动的机理进行了推导,系统地阐述了控制面故障下的飞机运动建模方法。在建模的基础上,对故障系统的可重构性进行研究。分别从线性系统运动和物理运动两个方面,给出了可重构能力的评定方案。推导了误差反向传播的前向神经网络用于控制系统设计时满足Lyapunov稳定性的学习算法,提出了一种新型的采用反向传播神经网络补偿常规控制器的重构飞行控制设计方案。采用非线性飞机运动模型对控制器进行了仿真,验证了重构飞行控制器的性能。     

基于遗传算法的半导体制冷器非线性PID设计

针对常规线性PID对具有非线性特征的半导体制冷器温度控制系统存在快速性和超调量难以兼得、抗干扰能力差的问题,提出将非线性PID控制用于半导体制冷器温度控制的策略.通过对线性PID存在问题以及PID各增益参数与偏差信号之间非线性关系的分析,构建了增益参数的非线性函数,针对非线性函数中参数较多问题提出了自适应遗传寻优的求解方法.仿真和实验结果表明,基于此遗传算法寻优的非线性PID控制器相比线性PID控制器,调节时间缩短,超调量减小,抗干扰能力更强.     

A parallel neuro-controller for DC motors containing nonlinear friction

This paper presents an application of a parallel neuro-controller for compensating the effects induced by the friction in a DC motor system. A back-propagation neural network based on a gradient descent algorithm is employed, and a bound on the tracking error is derived from the analysis of the tracking error dynamics. The parallel neuro-controller is a combination of a linear controller and a neural network controller which compensates for nonlinear friction. The proposed scheme is implemented and tested on an IBM PC-based DC motor control system. The algorithm, simulations, and experimental results are described. The results are relevant for many precision drives, such as those found in industrial robots.     

倒立摆系统的高精度控制器设计与实现

对线性二次调节器LQR算法中Q和R矩阵的参数与控制系统反馈矩阵Ⅸ之间的关系进行了实验研究,并将所获得的最佳反馈矩阵作为所设计的神经网络控制器的权值初始值。该神经网络控制器是带有局部递归神经网络并具有PID结构的控制器,因而设计简单,尤其适合用于多变量非线性时变系统。通过对一级和二级直线倒立摆系统的具体控制器的设计以及实验,将LQR控制器与神经网络控制器分别在无干扰和有干扰情况下的控制效果进行了对比分析,设计并实现了具有控制精度以及鲁棒性比最优线性二次调节器更高的一级和二级直线倒立摆系统。     

基于神经网络与多模型的非线性自适应广义预测解耦控制

针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性.