A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators

We examine in this paper the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed controller does not require a precise dynamical model of the system's dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the system's dynamics. The ability to incorporate the already acquired knowledge about the system's dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal force errors are also shown to asymptotically converge to zero under such conditions     

Fuzzy adaptive speed control of a permanent magnet synchronous motor

A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.     

Design of incremental fuzzy PI controllers for a gas-turbine plant

In this paper, incremental fuzzy proportional integral (PI) speed and temperature controllers for a heavy-duty gas-turbine plant are presented. To improve performance, an analysis of incremental fuzzy PI control is provided, and new fuzzy control rules are proposed. In applying the fuzzy PI control to a gas-turbine plant, all gains are optimized by an adaptive genetic algorithm. We show the performance improvement of the proposed controller compared with conventional PI controller via simulations.     

Design of a fuzzy traffic controller for ATM networks

This paper presents the design of a fuzzy traffic controller that simultaneously manages congestion control and call admission control for asynchronous transfer mode (ATM) networks. The fuzzy traffic controller is a fuzzy implementation of the two-threshold congestion control method and the equivalent capacity admission control method extensively studied in the literature. It is an improved, intelligent implementation that not only utilizes the mathematical formulation of classical control but also mimics the expert knowledge of traffic control. We appropriately choose input linguistic variables of the fuzzy traffic controller so that the controller is a closed-loop system with stable and robust operation. We extract knowledge of conventional control methods from numerous analytical data using a clustering technique and then use this knowledge to set parameters of the membership functions and fuzzy control rules via fuzzy set manipulation (linguistically stated but mathematically treated) with the aid of an optimization technique named genetic algorithm (GA). Simulation results show that the proposed fuzzy admission control improves system utilization by a significant 11%, while maintaining the quality of service (QoS) contract comparable with that of the conventional equivalent capacity method. The performance of the proposed fuzzy congestion control method is also 4% better than that of the conventional two-threshold congestion control method     

Fuzzy adaptive vector control of induction motor drives

This paper deals with the design and experimental realization of a model reference adaptive control (MRAC) system for the speed control of indirect field-oriented (IFO) induction motor drives based on using fuzzy laws for the adaptive process and a neuro-fuzzy procedure to optimize the fuzzy rules. Variation of the rotor time constant is also accounted for by performing a fuzzy fusion of three simple compensation strategies. A performance comparison between the new controller and a conventional MRAC control scheme is carried out by extensive simulations confirming the superiority of the proposed fuzzy adaptive regulator. A prototype based on an induction motor drive has been assembled and used to practically verify the features of the proposed control strategy     

Payload trajectory tracking of a 5-DOF tower crane with a varying-length hoist cable: A passivity-based adaptive control approach

This paper presents a passivity-based adaptive control method for a 5 degree-of-freedom (DOF) tower crane that guarantees robust payload trajectory tracking. The 5-DOF tower crane system considered in this work features three actuated degrees of freedom (including a varying-length hoist cable) and two unactuated degrees of freedom in the hoist cable sway. The proposed controller includes an adaptive feedforward-like control input that is used to ensure that the tower crane features an output strictly passive input–output mapping. The Passivity Theorem is invoked to guarantee closed-loop input–output stability for any output strictly passive negative feedback controller. A novel approach is developed to bound the time derivative of the system’s mass matrix, which is a critical aspect of the proof of passivity. Experimental tests are performed, which demonstrate the effectiveness of the control law on a small-scale three-dimensional tower crane.     

Design and implementation of an adaptive neural-network compensatorfor control systems

Recently, many studies have been made for intelligent controls using the neural-network (NN). These NN approaches for control strategies are based on the concept of replacing the conventional controller with a new NN controller. However, it is usually difficult and unreliable to replace the factory-installed controller with another controller in the workplace. In this case, it is desirable to install an additional outer control loop around the conventional control system to compensate for the control error of the preinstalled conventional control system. This paper presents an adaptive NN compensator for the outer loop to compensate for the control errors of conventional control systems. The proposed adaptive NN compensator generates a new command signal to the conventional control system using the control error that is the difference between the desired reference input and the actual system response. The proposed NN-compensated control system is adaptable to the environment changes and is more robust than the conventional control systems. Experimental results for a SCARA-type manipulator show that the proposed adaptive NN compensator enables the conventional control system to have precise control performance     

变论域自适应模糊控制器的CMOS模拟电路芯片实现

为解决传统的自适应模糊控制器算法过于复杂难以用模拟电路实现的问题,本文研究了输入输出论域可随输入变量的变化而自适应变化的在线自适应模糊控制器及其在非线性系统控制中的应用,并制作了CMOS模拟电路芯片.提出了一种新的尖三角形隶属度函数实现输入变论域的功能,输出变论域部分采用对输入变量进行加权积分并求其绝对值的方法.控制器的其他部分为求小电路和重心法去模糊电路.以上各电路均为CMOS模拟电路,它们和集成的整体电路均在无锡上华(CSMC) 0.6μm工艺下流片,测试结果表明该芯片完成了变论域模糊控制器的功能.     

基于模糊自适应PID控制的速度调节器设计与仿真

针对异步电动机非线性的特点,考虑到传统PID控制难以对其进行有效的控制的问题。文中设计了一种模糊自适应PID控制器,并将这种控制器应用到系统的转速调节的环节中。利用Matlab的Simulink工具搭建了模糊自适应PID控制的异步电机的仿真模型。仿真实验结果证明了设计的优越性,模糊自适应PID控制下的系统超调变小,反应速度变快,与原先的PID控制方式相比提高了系统的稳定性、动态响应性能以及鲁棒性能。     

基于基因算法的模糊控制研究

本文提出一种基于基因算法优化的自学习模糊控制器的设计.研究基因算法理论对模糊控制参数的全局寻优.出于对硬件实现的考虑,集中讨论了控制规则后件的产生及对规则库的动态学习,并采用对传统基因算法进行变化后的计算方法.使用Matlab中的仿真工具Simulink,对倒立摆典型非线性系统进行了在线模拟,证实了所提出的控制算法的有效性和适用性,为进一步的硬件芯片实现的可行性提供了有价值的理论基础.     

圆柱-球体三自由度超声电机模糊自适应控制

针对圆柱-球体三自由度超声电机难以建立精确数学模型的特点,设计了模糊自适应定位控制系统。该控制系统由模糊控制器和在线参数自调整环节构成,在线参数自调整环节使该系统的动态特性、稳态性能更好地兼顾,克服了固定量化因子控制性能不理想的缺点。应用该控制器,实现了电机精确定位控制。结果表明,参数自调整模糊自适应控制器的性能优于传统PID控制器及固定量化因子的模糊控制器,得到了满意的控制效果。     

一种基于PLC的模糊自适应PID控制器设计

通过对模糊自适应PID控制器设计过程的详细分析,提出了一种基于PLC查表方式实现模糊自适应PID控制器的方法,实现了基于PLC的自适应模糊PID控制器的设计,并应用于实际的控制系统中。结果表明,用PLC实现模糊自适应PID控制简单实用,适于工业控制系统应用。     

Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences. The main functions of the proposed HFC are to drive the optical head unit (OHU) to the target track neighborhood as fast as possible and smoothly park the OHU in the least time in the target track neighborhood. An automatic learning approach based on genetic algorithms (GAs) is proposed for learning the fuzzy rules for both the intelligent time switch and driving force controller. Modulated orthogonal membership functions are utilized in both fuzzy controllers to improve the GA learning efficiency. The number of parameters needed to parameterize the fuzzy rule base is greatly reduced with the modulated orthogonal membership functions. Compared to the conventional track-seeking controller currently utilized in most ODDs that employ a speed profile as the reference signal for the track-seeking feedback control system, the proposed HFC outperforms the conventional track-seeking control schemes. Experiments are performed to justify the performance comparison.     

Fuzzy two-degrees-of-freedom speed controller for motor drives

A fuzzy two-degrees-of-freedom (2-DOF) controller and its application to the speed control of an induction motor drive are presented in this paper. The proposed controller is composed of two fuzzy controllers to obtain good tracking and regulating responses. Unlike the conventional fuzzy controller, the error between the outputs of a reference model and the controlled drive is used to drive the proposed fuzzy controller. The drive rotor speed response can closely follow the trajectory produced by the reference model, and good load speed regulating response can also be obtained simultaneously owing to the possession of two-degrees-of-freedom in structure. Moreover, these performances are rather insensitive to the operating condition changes. The dynamic signal analysis as well as the construction of fuzzy control algorithms are described in detail. Some simulated and measured results are provided to demonstrate the effectiveness of the proposed fuzzy controller     

Adaptive coupling control for overhead crane systems

Due to the requirements of high positioning accuracy, small swing angle, short transportation time, and high safety, both motion and stabilization control for an overhead crane system becomes an interesting issue in the field of control technology development. Since the overhead crane system is subject to underactuation with respect to the load sway dynamics, it is very hard to manipulate the crane system in a desired manner, namely, gantry position tracking and sway angle stabilization. Hence, in this paper, a nonlinear control scheme incorporating parameter adaptive mechanism is devised to ensure the overall closed-loop system stability. By applying the designed controller, the position error will be driven to zero while the sway angle is rapidly damped to achieve swing stabilization. Stability proof of the overall system is given in terms of Lyapunov concept. To demonstrate the effectiveness of the proposed controller, results for both computer simulation and experiments are also shown.     

A QoS-provisioning neural fuzzy connection admission controller formultimedia high-speed networks

This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract     

An optimal fuzzy PID controller

This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytical formulae. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization     

Fuzzy motion control of an auto-warehousing crane system

Fuzzy motion control of an auto-warehousing crane system is presented in this paper. Using the concept of linguistic variable, a fuzzy logic controller (FLC) can convert the knowledge and experience of an expert into an automatic control strategy. The designed FLC with a rule base and three sets of parameters is used to control the crane system in x, y, and z directions. The unloaded weight and the fully loaded weight of the crane system in discussion are 1.35×10⁴ kg and 1.5×10⁴ kg, respectively. For various loading conditions and varying distances, the FLC still controls the crane system very well with positioning accuracy less than 2×10 ^-3 m for all directions. The distance-speed reference curve for control of the crane system is designed to meet the engineering specifications of motion such as acceleration, deceleration, maximum speed, and creep speed in each direction, and is generated automatically according to varying distance. The method for designing the distance-speed reference curve can make the crane move at relatively high speed to approach the target position. Simulations of the motion control in the three directions are demonstrated     

Adaptive Fuzzy Control of Nonlinear in Parameters Uncertain Chaotic Systems Using Improved Speed Gradient Method

This paper presents an adaptive fuzzy controller for Nonlinear in Parameters (NLP) chaotic systems with parametric uncertainties. In the proposed controller, the unknown parameters are estimated by the novel Improved Speed Gradient (ISG) method, which is a modification of Speed Gradient (SG) algorithm. ISG employs the Lagrangian of two suitable objective functionals for on-line estimation of system parameters. The most significant advantage of ISG is that it is applicable to NLP systems and it results in a faster rate of convergence for the estimated parameters than the SG method. Estimated parameters are used to design the fuzzy controller and to calculate the Lyapunov exponents of the chaotic system adaptively. Furthermore, established on the well-known Takagi–Sugeno (T-S) fuzzy model, a LMI (Linear Matrix Inequality)-based fuzzy controller is designed and is tuned using estimated parameters and Lyapunov exponents. Throughout the controller design procedure, several important issues in fuzzy control theory including relaxed stability analysis, control input performance specifications, and optimality are taken into account. Combination of ISG parameter estimation method and T-S-based fuzzy controller yields an adaptive fuzzy controller capable to suppress uncertainties in parameters and initial states of NLP chaotic systems. Finally, simulation results are provided to show the effectiveness of the ISG and adaptive fuzzy controller on chaotic Lorenz system and Duffing oscillator.