Damping of Torsional Vibrations in Two-Mass System Using Adaptive Sliding Neuro-Fuzzy Approach

In this paper, a new robust control system with the adaptive sliding neuro-fuzzy speed controller for the drive system with the flexible joint is proposed. A model reference adaptive control structure (MRAC) is used in this drive system. The torsional vibrations are successfully suppressed in the control structure with only one basic feedback from the motor speed. The damping ability of the proposed system has been confirmed for a wide range of the system parameters and compared with the other control concepts, like the adaptive Pi-type neuro-fuzzy controller and the classical cascade PI structure.     

Adaptive decoupling control of induction motor drives

A novel control approach for a robust induction motor drive system with a voltage source inverter has been developed. In the scheme, the induction motor and its corresponding inverter gating signal are controlled using the decoupling control theory. In addition, an adaptive optimal speed regulator employing the model reference adaptive control (MRAC) is incorporated into the drive system to compensate for unfavorable errors. The principles and special features of the control scheme are discussed, and the configuration of the drive system is presented. Comparison is made between conventional proportional plus integral (PI) control and the MRAC. Test results show the robustness and superior dynamic performance of the proposed control system     

A discrete adaptive field-oriented induction motor drive

A discrete model reference adaptive controller (MRAC) is designed and implemented. This MRAC makes the performance of the field-oriented induction motor drives insensitive to parameter changes. Only the information of the reference model and the plant output are required. Hence, the proposed controller is easy to implement practically. For designing the proposed adaptive controller, the dynamic model of the drive system is estimated from the sampled input-output data using the stochastic modeling technique. The theoretical basis of the adaptive control is derived and simulation is made. The hardware of the drive system and the microprocessor-based adaptive controller are discussed. Some experimental results are given to demonstrate the effectiveness of the proposed controller     

An MRAC-based nonlinear speed control of an interior PM synchronous motor with improved maximum torque operation

A model reference adaptive control (MRAC)-based nonlinear speed control strategy of an interior permanent magnet (IPM) synchronous motor with an improved maximum torque operation is presented. In most servo systems, the controller is designed under the assumption that the electrical dynamics are neglected by the field-oriented control. This requires a high-performance inner-loop current control strategy. However, the separate designs for a high-performance current regulator and a robust speed controller need considerable effort. To overcome this limitation, an MRAC-based nonlinear speed control strategy for the IPM synchronous motor is presented, considering the whole nonlinear dynamics. Nonlinear speed control is achieved by an input–output linearization scheme. This scheme, however, gives an unsatisfactory performance under the mismatch of the system parameters and load conditions. For the robust output response, the controller parameters are estimated by an MRAC technique in which the disturbance torque and flux linkage are estimated. The adaptation laws are derived from Lyapunov stability theory. In view of the drive efficiency, the motor has to provide the maximum torque for a given input. To drive the IPM synchronous motor under improved maximum torque operation, the estimated flux linkage is employed for the generation of the d-axis current command. The robustness and output performance of the proposed control scheme are verified through simulation results.     

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     

Model Reference Adaptive Control Design for a Shunt Active-Power-Filter System

In this paper, model reference adaptive control (MRAC) is proposed for a single-phase shunt active power filter (APF) to improve line power factor and to reduce line current harmonics. The proposed APF controller forces the supply current to be sinusoidal, with low current harmonics, and to be in phase with the line voltage. The advantages of using MRAC over conventional proportional-integral control are its flexibility, adaptability, and robustness; moreover, MRAC can self-tune the controller gains to assure system stability. Since the APF is a bilinear system, it is hard to design the controller. This paper will solve the stability problem when a linearization method is used to solve the nonlinearity of the system. Moreover, by using Lyapunov's stability theory and Barbalat's lemma, an adaptive law is designed to guarantee an asymptotic output tracking of the system. To verify the proposed APF system, a digital signal controller (dsPIC30F4012) is adopted to implement the algorithm of MRAC, and a 1-kVA laboratory prototype is built to test feasibility. Experimental results are provided to verify the performance of the proposed APF system.     

一种新颖的基于内模电流控制的矢量控制系统

无速度传感器矢量控制系统中存在两个关键性的问题，即速度辨识的稳定性问题和控制器结构问题。本文提出一种新型的基于模型参考自适应的内模电流控制的矢量控制系统。首先，利用MRAS理论，保证了速度辨识的全局稳定性。然后通过内模控制实现动态解耦，克服了PI控制不能动态解耦和其他解耦方法中对参数敏感的问题。仿真结果表明：系统对参数变化具有较强的鲁棒性，取得较好的解耦效果和系统控制性能。     

Adaptive on-line parameter identification of a steer-by-wire system

Adaptive compliance control strategy can be a significant advantage for control of steer-by-wire systems. Initially the method is proposed for robotic applications where the main concern is the interaction forces between the robot and its environment. There are several studies about cooperative working of a robot and a human. As long as the steering system is a part of the vehicle where driver interaction is involved, it is reasonable to think that compliance control strategies can be adapted to steer-by-wire systems. Compliance control is a model reference control (MRC) strategy where the measured external force/torque is used as an input to a reference model to calculate its output and where the real system is controlled appropriately to track the reference system output. If a sensor is available to measure the external force/torque, system parameters need not to be estimated. A constant gain feedback controller can be used in such a case. However, if the parameter variations of the system are not within certain bounds, a model reference adaptive controller (MRAC) is needed. In addition to this, examining the change in the dynamics of the system due to the compliance of the driver arms is not possible by direct MRAC, because the driver effect is considered as a disturbance in this strategy. Therefore, in this study, instead of estimating controller parameters using direct MRAC where the main concern is the tracking performance, it is considered to use indirect MRAC in which the system parameters are estimated to observe their variations in the presence of parametric uncertainty and disturbances and to further examine the change in the dynamics of the system due to the compliance of the driver arms forming a closed kinematic structure by constraining the steering wheel. Hence, a steer-by-wire experimental setup including driver interaction and vehicle directional control units has been developed and three well-known adaptive on-line estimation methods, which are output-error method, equation-error method and modified recursive least squares method are evaluated on the driver interaction unit. These three methods are compared in terms of computational complexity, convergence, stability and applicability to real vehicles.     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

Control of the Drive System With Stiff and Elastic Couplings Using Adaptive Neuro-Fuzzy Approach

In the paper a robust control system with the fuzzy-neural network is proposed. A model reference adaptive control system is applied to the one- and two-mass systems. Different aspects of application of the examined control structure are discussed. The influence of the number of neuro-fuzzy controller (NFC) rules to the drive system performance is shown. The impact of the electromagnetic torque limit to the adaptive structure stability is discussed. Further, the comparison of the dynamical characteristics of the different NFC structures is done. The control structure with constant and changeable parameters of the adaptive rule is also examined. The torsional vibration suppression in the two-mass system is obtained in the developed adaptive structure with only one basic feedback from the motor speed     

自适应比例因子模糊控制器在异步电动机调速系统中的应用

为了降低转子电阻变化对异步电动机矢量控制系统性能的影响，本文把自适应比例因子模糊控制器应用到了矢量控制系统中。介绍了自适应比例因子模糊控制方法的綦本原理，给出了使用自适应比例因子模糊控制器的异步电动机矢量控制系统的结构框图，对主模糊控制器和辅助模糊控制器进行了设计。仿真实验结果表明，采用这种控制方法的调速系统不仅具有优良的动态性能，而且对转子电阻变化具有很强的鲁棒性。     

Model Reference Adaptive Controller-Based Rotor Resistance and Speed Estimation Techniques for Vector Controlled Induction Motor Drive Utilizing Reactive Power

In this paper, a detailed study on the model reference adaptive controller (MRAC) utilizing the reactive power is presented for the online estimation of rotor resistance to maintain proper flux orientation in an indirect vector controlled induction motor drive. Selection of reactive power as the functional candidate in the MRAC automatically makes the system immune to the variation of stator resistance. Moreover, the unique formation of the MRAC with the instantaneous and steady-state reactive power completely eliminates the requirement of any flux estimation in the process of computation. Thus, the method is less sensitive to integrator-related problems like drift and saturation (requiring no integration). This also makes the estimation at or near zero speed quite accurate. Adding flux estimators to the MRAC, a speed sensorless scheme is developed. Simulation and experimental results have been presented to confirm the effectiveness of the technique.     

A DSP-based implementation of a nonlinear model reference adaptive control for a three-phase three-level NPC boost rectifier prototype

In this paper, the design and the implementation of a model reference adaptive control (MRAC) applied to a three-phase three-level neutral-point-clamped (NPC) boost rectifier are presented. This control strategy is developed with a view to regulate dc output and neutral point voltages and to reduce the influence of parameter variations while maintaining unity power factor. A nonlinear multiple-input multiple-output (MIMO) state space model of the rectifier is then developed in dq0 reference frame. The proposed controller is based on the use of a feedback linearization technique followed by a robust MRAC scheme allowing the design of a suitable controller applied to the plant. The control law is designed in Simulink/Matlab and applied to the converter via a 1920-Hz pulse width modulator both executed in real time using the DS1104 DSP of dSPACE. A 1.25 kW laboratory prototype is developed for validation. The experimental results are given for different operating conditions: nominal power operation, balanced and unbalanced dc load steps, boost inductor variation, and reactive power control. The proposed control law performs perfectly in a wide operation range giving low output voltage ripple, low line-current THD, a small overshoot and a fast settling time under system parameters variation.     

Parameter sensitivity of MRAC models employed in IFO-controlled ACmotor drive

An investigation of the parameter sensitivities of the d-axis voltage, q-axis voltage, and the reactive power models used in model reference adaptive control (MRAC) strategy for tuning the indirect field-oriented (IFO) controller is presented. These reference models are derived from the dynamic model of the machine under the assumption that the IFO control conditions are valid. In this paper, the machine has been considered to operate under variable speed and load conditions. To improve the robustness of the MRAC strategy, a cascade parameter estimation technique has been used to track the variations in r_s and σl_s. The experimental results obtained with the proposed estimation technique, as well as with the MRAC strategy, are presented     

Model-reference adaptive control based on neurofuzzy networks

Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems.     

Fieldbus based distributed servo control using LonWorks/IP gateway/web servers

Virtual device network (VDN) is an intelligent integrated form of a device (control) network and IP network. If a fieldbus based distributed control system (DCS) is implemented on a global VDN, efficiency and flexibility can be significantly improved. In this study DCS implemented on the LonWorks/IP VDN is investigated with an example of model reference adaptive control (MRAC) of a geared DC motor. In order to compensate for the network induced uncertain time delay inherently present on VDN, the modified Smith predictor based internal model controller was integrated to MRAC (MRAC–IMC). The effectiveness of the proposed control scheme was tested through experiment. The proposed control scheme exhibited the robustness to noise and external disturbances and the good tracking performance around zero velocity point occurring due to overshoot and stick friction. Result of this study suggests that sophisticated servo control of dynamic systems is possible from a remote client PC on VDN by properly compensating the network delay.     

Adaptive neural network feedback control of a passive line-of-sight stabilization system

An adaptive neural network full-state feedback controller has been designed and applied to the passive line-of-sight (LOS) stabilization system. Model reference adaptive control (MRAC) is well established for linear systems. However, this method cannot be utilized directly since the LOS system is nonlinear in nature. Utilizing the universal approximation property of neural networks, an adaptive neural network controller is presented by generalizing the model reference adaptive control technique, in which the gains of the controller are approximated by neural networks. This removes the requirement of linearizing the dynamics of the system, and the stability properties of the closed-loop system can be guaranteed.     

Model reference adaptive predictive control for a variable-frequency oil-cooling machine

This paper develops methodologies and techniques for the design, analysis, and implementation of a model reference adaptive predictive temperature controller for a variable-frequency oil-cooling machine, suited for cooling high-speed machine tools. The oil-cooling process is modeled experimentally as a first-order system model with a time delay and its system parameters are identified using the recursive least-square method. Based on this model, a model reference adaptive predictive controller is proposed for achieving set-point tracking and robustness. A real-time model reference adaptive predictive control algorithm is then presented and implemented utilizing a stand-alone digital signal processor TMS320F243 from Texas Instruments Incorporated. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads, and load changes.     

A Decentralized Model Reference Adaptive Controller for Large-Scale Systems

A decentralized model reference adaptive controller (MRAC) for a class of large-scale systems with unmatched interconnections is developed in this paper. A novel reference model is proposed for the class of large-scale systems considered and a decentralized, full-state feedback adaptive controller is developed for each subsystem of the large-scale system. It is shown that with the proposed decentralized adaptive controller, the states of the subsystems can asymptotically track the desired reference trajectories. To substantiate the performance of the proposed controller, a large web processing line, which mimics most of the features of an industrial web process line, is considered for experimental study. Extensive experiments were conducted with the proposed decentralized adaptive controller and an often used decentralized industrial proportional-integral (PI) controller. A representative sample of the comparative experimental results is shown and discussed     

Robust adaptive control: a unified approach

A complete tutorial review of the entire field is presented, beginning with simple instability examples to identify the causes of nonrobust behavior in adaptive control. Some of the mathematical groundwork is presented, and the theory for the design and analysis of adaptive laws is developed. Commonly used adaptive controller structures are discussed, highlighting their particular robustness properties. Particular attention is paid to model reference, pole placement, and linear quadratic controller structures. Designs and analyses of model reference, pole placement, and linear quadratic controllers, based on combining the corresponding controller structures with the various robust adaptive laws, are presented. Suggestions for future research are given