神经模糊控制在船舶自动舵中的应用

针对常规模糊自动舵由于受船舶控制过程的非线性、时变性以及风浪干扰等因素影响,模糊控制规则和隶属函数需要校正,利用神经网络的自学习能力,用神经网络去实现模糊控制,设计自动舵神经模糊控制器,采用BP算法和最小二乘算法的混合学习算法实现对模糊规则和隶属函数的参数训练,提高控制器的自适应能力。仿真实验表明所设计的控制器有效可行,适应船舶在风浪干扰环境下的控制性能要求。     

基于演化计算的自校正模糊控制自动舵研究

本文提出了一种自校正模糊控制自动舵,该自 动舵采用模糊控制器对航向进行控制,通过演化计算对模糊控制器的加权因子和量化因子进 行调整．仿真证明,该算法与普通模糊控制器相比具有较好的控制性能．     

H∝控制器应用于船舶自动舵

应用Ｈ∝控制的Ｓ／Ｔ混合灵敏度方法于船舶自动舵设计。按机理估算模型乘性摄动上限，据此确定优化指标中的权函数Ｗ２。利用设计的控制器对闭环系统在多种环境条件下进行了仿真研究，结果是满意的。     

H_∞控制器应用于船舶自动舵

应用Ｈ∞控制的Ｓ／Ｔ混合灵敏度方法于船舶自动舵设计。按机理估算模型乘性摄动上限，据此确定优化指标中的权函数Ｗ２。利用设计的控制器对闭环系统在多种环境条件下进行了仿真研究，结果是满意的。     

船舶自动舵控制技术发展研究

介绍与比较了船舶操纵的各种自动舵控制方法,船舶自动舵可分为四个发展阶段,却机械舵、PID舵、自适应舵和智能舵,其中智能舵为目前最先进的自动舵,它又分为专家系统、模糊舵和神经网络舵。     

PC104工控机在航迹自动舵中的应用

用ＰＣ１０４工控机开发船舶航迹舵，给出了系统设计的结构，系统软件用ＴＣ２．０开发，详细介绍了系统的软件设计及工作流程。     

船舶自动舵控制技术发展研究

介绍与比较了船舶操纵的各种自动舵控制方法 ,船舶自动舵可分为四个发展阶段 ,即机械舵、PID舵、自适应舵和智能舵 ,其中智能舵为目前最先进的自动舵 ,它又分为专家系统、模糊舵和神经网络舵。     

模糊控制在列车自动驾驶中的应用

模糊控制在工业领域得到了广泛的应用。本文介绍了模糊控制在列车自动驾驶中的应用,应用结果同ＰＩＤ控制相比,具有控制自由度好,停车平均误差低,节电性能优良等特点。     

模糊控制在自动定位APC中的应用

针对轧钢企业中上料和卸料自动定位过程影响因素较多、控制系统存在不确定性、难于控制的特点，以鞍钢冷轧清洗线入口钢卷小车的自动定位过程为例，对钢卷小车自动定位APC提出了一种新的控制方法。采用模糊控制器对钢卷小车由于质量不同而引起的惯性滑差进行补偿控制，应用PLC技术与智能算法相结合，通过大量的现场数据进行模糊规则的调试修订，形成了一套可靠的控制系统，解决了轧钢工业中关于类似的上料和卸料存在的定位不准的问题。控制方案和设计方法经现场运行表明是正确可靠的。     

基于RLS自适应逆控制理论的船舶自动舵研究

本文探讨了自适应逆控制理论中的对象动态控制问题和对象扰动控制问题，并且针对船舶模型特性，用最小二乘法对船舶进行模型参数辩识和控制器的设计。仿真结果表明。与PID控制相比，自适应逆控制方法具有动态响应快，抗扰动性好等特点。     

Application of a neuro-fuzzy system to control a mobile vehicle

We describe a fuzzy control based on a neural network, which is obtained by merging the advantages of a neural network, a competitive algorithm, and fuzzy control. This adaptive fuzzy control system can deal with data sampled by a neural network. From such training data, it can produce more reasonable fuzzy rules by a competitive (clustering) algorithm, and finally control the object by the optimized fuzzy rules. This is not a simple combination of the three methods, but a merger into one control system. Some experiments and future considerations are also given.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

自适应神经元--模糊推理系统在污水曝气控制中的应用

曝气控制是保证污水处理厂水质和降低能耗的重要环节。本文考虑污水处理厂出水水质和曝气量的非线性、大滞后、时变性等特点，利用反向传播(Back Propagatio——BP)算法对隶属度函数进行优化，建立模糊推理规则，并设计了一个自适应神经元——模糊控制系统，该系统对某SBR小型污水处理厂曝气进行了仿真分析，结果表明设计的控制系统的有效性。     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

Nonlinear optimal tracking control with application to super-tankers for autopilot design

A new method is introduced to design optimal tracking controllers for a general class of nonlinear systems. A recently developed recursive approximation theory is applied to solve the nonlinear optimal tracking control problem explicitly by classical means. This reduces the nonlinear problem to a sequence of linear-quadratic and time-varying approximating problems which, under very mild conditions, globally converge in the limit to the nonlinear systems considered. The converged control input from the approximating sequence is then applied to the nonlinear system. The method is used to design an autopilot for the ESSO 190,000-dwt oil tanker. This multi-input-multi-output nonlinear super-tanker model is well established in the literature and represents a challenging problem for control design, where the design requirement is to follow a commanded maneuver at a desired speed. The performance index is selected so as to minimize: (a) the tracking error for a desired course heading, and (b) the rudder deflection angle to ensure that actuators operate within their operating limits. This will present a trade-off between accurate tracking and reduced actuator usage (fuel consumption) as they are both mutually dependent on each other. Simulations of the nonlinear super-tanker control model are conducted to illustrate the effectiveness of the nonlinear tracking controller.     

船舶航向非线性系统的多滑模自适应模糊控制

针对参数未知的船舶航向非线性控制系统数学模型,在考虑舵机伺服机构特性的情况下,船舶航向控制问题就成为一个虚拟控制系数未知的非匹配不确定非线性控制问题.基于多滑模设计方法和模糊逻辑系统的逼近能力,提出了一种多滑模自适应模糊控制算法,通过引入非连续投影算法和积分型Lyapunov函数,提高了系统在抑制参数漂移、控制器奇异等方面的能力.借助Lyapunov函数证明了所设计控制器使最终的闭环非匹配不确定船舶运动非线性系统中的所有信号有界,且跟踪误差收敛到零.仿真研究表明:该算法与传统的PID控制相比,具有较好的跟踪能力和自适应能力.     

基于Policy Gradient的自动驾驶仪控制参数设计

针对目前工程上通过在弹道上选取特征点,利用系数冻结法去研究和设计,这样一系列的假设会使设计的导弹模型与实际的弹体模型存在一定的差异,因此,提出了基于强化学习的过载自动驾驶仪在线调整PID参数,研究飞行器的控制问题,该方法将导弹作为智能体,读取飞行状态信息并建立动作策略和奖惩机制;其次,智能体根据算法给出的随机动作指令执...     

Some control strategies for a high-angle-of-attack missile autopilot

Recent interest in high maneuverability and stealth in missiles has triggered new investigations in estimating the potential and applicability of recent theoretical control methods as well as how they can meet industrial demands. Since the work presented here is closely related to an industrial application it is desirable to deal primarily with classical regulators. In this paper, two nonlinear methods are considered: `Approximate feedback linearization’ and the `asymptotic output tracking’ approach. Simulation results are given.     

Robust adaptive neuro-fuzzy control of uncertain nonholonomic systems

In this paper, we present an adaptive neuro-fuzzy controller design for a class of uncertain nonholonomic systems in the perturbed chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive neuro-fuzzy control laws are developed using state scaling and backstepping. Semiglobal uniform ultimate bound-edness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neigh-borhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. By using fuzzy logic approximation, the proposed control is free of control singularity problem. An adaptive control-based switching strategy is proposed to overcome the uncontrollability problem associated with x 0 (t 0 ) = 0.     

Applying neuro-fuzzy model dFasArt in control systems

This paper presents the neuro-fuzzy dFasArt (dynamic FasArt) architecture as an extension of the FasArt model including a dynamic algorithm formulation. This allows dFasArt to deal with identification and clustering problems using the temporal information of the signals. The focus is placed on the application of dFasArt to the control systems field for monitoring the controller performance. It is presented through two selected experiments covering some interesting control issues. The first one shows the use of dFasArt to decide when the parameters adaption is needed in a classic adaptive control scheme. The second one analyzes the behaviour of closed-loop controlled systems to establish a classification of the system operational states, starting from the measured data. Digital signal processing is used to represent the temporal signals with spatial patterns and dFasArt is proposed to classify these patterns on-line. Real scale plants have been used to carry out several experiments with good results. This shows dFasArt as a feasible tool to deal with control loop performance monitoring and controller performance assessment in industrial processes.     

A reinforcement neuro-fuzzy combiner for multiobjective control

This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch; it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as "success (good)" or "failure (bad)" for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme.