Adaptive B‐spline neural network‐based vector control for a grid side converter in wind turbine‐DFIG systems

This paper presents a novel control system design for the grid‐side converter of doubly fed induction generator wind power generation systems. The control method proposed in this work is a vector control based on adaptive B‐spline neural network by using a simple fixed‐gain stabilizing control topology. The adaptive control is designed both for inner current loops and an outer DC‐link voltage loop of the grid side converter control system. To guarantee the control stability, the weights updating rule for the B‐spline neural network is synthesized by utilizing Lyapunov's direct method. To verify the effectiveness of the proposed control system, extensive simulations are performed using MATLAB/Simulink. Based on the simulation results, it is concluded that the proposed controller has improved performance compared to an optimum proportional integral control system. It is also relatively robust against external disturbances and variations of the control parameters. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Decentralized guaranteed cost control for uncertain large‐scale systems using additive control gain

This paper investigates an application of additive control gain to the guaranteed cost control (GCC) problem of decentralized robust control for a class of discrete‐time uncertain large‐scale systems. Based on the Linear Matrix Inequality (LMI) design approach, a class of decentralized local fixed state feedback controllers with additive control gain is established. The novel contribution of this paper is that multiobjective control is attained by using the additive control gain. Although the additive control gain is included in the uncertain large‐scale systems, the closed‐loop system is asymptotically stable. In order to demonstrate the efficiency of our proposed controller, using the fuzzy logic control as the additive control gain, the simple numerical example is given. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 169(3): 18–32, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20726     

A new decoupling design of self‐tuning multivariable generalized predictive control

A novel robust decoupling method with multivariable generalized predictive control (MGPC) for a class of nonlinear systems is presented in an adaptive version. The cross‐coupling action and the non‐linear actors of the system are identified on‐line by a neural network. A feedforward compensation based on generalized predictive control, is proposed for decoupling control. A modified recursive least‐squares (RLS) algorithm can be used to estimate the linear parameters for time‐varying systems. Simulations are carried out and the results show the effectiveness of the proposed algorithm. Copyright © 1999 John Wiley & Sons, Ltd.     

Neural network‐based adaptive attitude tracking control for flexible spacecraft with unknown high‐frequency gain

Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state‐dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high‐frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L₂ ‐gain performance. The resulting closed‐loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.     

Observer‐based adaptive neural control of uncertain MIMO nonlinear systems with unknown control direction

This paper investigates adaptive neural network output feedback control for a class of uncertain multi‐input multi‐output (MIMO) nonlinear systems with an unknown sign of control gain matrix. Because the system states are not required to be available for measurement, an observer is designed to estimate the system states. In order to deal with the unknown sign of control gain matrix, the Nussbaum‐type function is utilized. By using neural network, we approximated the unknown nonlinear functions and perfectly avoided the controller singularity problem. The stability of the closed‐loop system is analyzed by using Lyapunov method. Theoretical results are illustrated through a simulation example. Copyright © 2012 John Wiley & Sons, Ltd.     

Design approach for high‐bandwidth low‐power three‐stage operational amplifiers

A new design approach to optimize the frequency compensation network of three‐stage operational amplifiers (op‐amps) is presented. The proposed criterion is aimed at maximizing the bandwidth of well‐established three‐stage op‐amps using Nested‐Miller Compensation with feedforward tranconductance stage and nulling resistor (NMCFNR). As shown by design examples in a commercial 0.35‐µm CMOS technology, the proposed approach allows the amplifier bandwidth to be enhanced significantly with respect to that resulting from using existing design strategies for NMCFNR op‐amps. It is also demonstrated that NMCFNR op‐amps, designed according to the proposed method, even guarantee larger values of the gain‐bandwidth product than three‐stage amplifiers using more complicated frequency compensation techniques, such as AC boosting compensation or damping‐factor control frequency compensation. Copyright © 2010 John Wiley & Sons, Ltd.     

Differential amplifier with improved gain‐accuracy and linearity

A novel circuit design technique is presented which improves gain‐accuracy and linearity in differential amplifiers. The technique employs negative impedance compensation and results demonstrate a significant performance improvement in precision, lowering sensitivity, and wide dynamic range. A theoretical underpinning is given together with the results of a demonstrator differential input/output amplifier with gain of 12 dB. The simulation results show that, with the novel method, both the gain‐accuracy and linearity can be improved greatly. Especially, the linearity improvement in IMD can get to more than 23 dB with a required gain. Copyright © 2009 John Wiley & Sons, Ltd.     

Achievement method of high‐speed response of error prediction tracking control for optical disk system

This paper proposes the new design method of error‐prediction control systems combining the ZPET control and the robust feedback control. The error‐prediction control system based on ZPET control can reduce the tracking error caused by periodic disturbances more efficiently than the conventional repetitive control technique. However, the proposed tracking servo system does not reduce the residual tracking error caused by the low‐pass filter of the feedforward compensation and by the long sampling time. In order to overcome this problem, this paper proposes the new structure of the feedforward tracking control system. The modified ZPET control tracking servo system for optical disk recording system does not include the low‐pass filter and its sampling time becomes faster. On the other hand, the sudden disturbance observer reduces the influence of nonperiodic disturbances. The experimental results point out that the proposed tracking servo system has a precise tracking response against both the periodic disturbances and the nonperiodic disturbances. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 170(4): 51–59, 2010; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.20800     

Neural network adaptive robust control with application to precision motion control of linear motors

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance‐oriented control laws for a class of single‐input–single‐output (SISO) nth‐order non‐ linear systems. Both repeatable (or state dependent) unknown non‐linearities and non‐repeatable unknown non‐linearities such as external disturbances are considered. In addition, unknown non‐linearities can exist in the control input channel as well. All unknown but repeatable non‐linear functions are approximated by outputs of multi‐layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on‐line with no prior training needed. In order to avoid the possible divergence of the on‐line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non‐repeatable non‐linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on‐line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high‐performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non‐linearities due to the position dependent friction and electro‐magnetic ripple forces. Comparative experiments verify the high‐performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low‐speed back‐and‐forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.     

PFC Design via FRIT Approach for Adaptive Output Feedback Control of Discrete‐Time Systems

This paper deals with a design problem of an adaptive output feedback control for discrete‐time systems with a parallel feedforward compensator (PFC), which is designed for making the augmented controlled system “Almost Strictly Positive Real” (ASPR). A PFC design scheme by a fictitious reference iterative tuning (FRIT) approach with only using an input/output experimental data set will be proposed for discrete‐time systems in order to design an adaptive output feedback control system. Furthermore, the effectiveness of the proposed PFC design method will be confirmed through numerical simulations by designing an adaptive control system with adaptive NN (neural network) for an uncertain discrete‐time system. © 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 187(1): 24–32, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22456     

Iterative adaptive robust control of multivariable CD processes

In this paper we present a novel adaptive robust control approach to the multivariable cross‐directional (CD) process of continuous web manufacturing. The common assumption of spatial frequency decomposition (SFD) is used to allow the process analysis in terms of a family of single‐input single‐output (SISO) transfer functions across the spatial frequencies. We then apply discretized Windsurfing adaptive robust control to each individual separated spatial frequency, starting with a stable initial model and a robust stabilizing controller at each spatial frequency. This approach allows the 2D bandwidth of the closed‐loop system to be increased progressively at each spatial frequency through an iterative relevant system identification and control design procedure. The method deals with both model uncertainty and measurement noise issues. Simulation results are given to illustrate the performance of the applied method. Copyright © 2009 John Wiley & Sons, Ltd.     

Common‐mode gain reduction technique and its applications

This paper proposes a common‐mode gain reduction technique and a new approach for a balanced‐type system design. Two design examples of a balanced‐type operational transconductance amplifier and a balanced‐type filter are given. The proposed scheme employs the proposed common‐mode gain reduction technique together with the common‐mode feedback (CMFB) network, which is used only to set a bias, to meet requirements of common‐mode rejection. Compared with the conventional method, which uses the CMFB that has a higher gain than the one used in the proposed scheme, the proposed method shows reduction in design complexities and relaxation of the stability conditions. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Robust 2DOF fuzzy gain scheduling control for DC servo speed controller

This paper proposes a new design method called “robust 2DOF fuzzy gain scheduling control” for a DC servo speed control system. The proposed technique utilizes the basic concept of 2DOF robust loop shaping, whose time‐domain specifications are combined during the controller design using a reference model. In addition, the local controllers are fixed‐ structure robust controllers whose structure can be specified as for a simple controller. A fuzzy approach is adopted in both system identification process and global control structure to accomplish an entirely robust system. Although the design of robust control in a fuzzy system is not easy, genetic algorithms (GAs) simplify the control design problem to design the fuzzy controller such that the average stability margin is minimized. Implementation of a DC servo speed control was adopted to investigate the effectiveness of the proposed controller. As seen from the results, the proposed controller has more robust performance and can be adopted in applications with a wide operating range. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Neural adaptive robust output feedback control of wheeled mobile robots with saturating actuators

This paper addresses the output feedback tracking control problem of electrically driven wheeled mobile robots subjected to actuator constraints. The main drawback of previously proposed controllers is the actuator saturation problem, which degrades the transient performance of the closed‐loop control system. In order to alleviate this problem, a saturated tracking controller has been proposed using the hyperbolic tangent function. A new nonlinear observer is introduced in order to leave out the velocity sensors in the robot system to decrease the cost and weight of the system for practical applications. A dynamic surface control strategy is effectively used to reduce the design complexity when considering actuator dynamics. In addition, neural network approximation capabilities and adaptive robust techniques are also adopted to improve the tracking performance in the presence of uncertain nonlinearities and unknown parameters. Semi‐global stability of the closed‐loop system is presented using direct Lyapunov method. Simulation results are provided to illustrate the effectiveness of the proposed control system for a differential drive mobile robot in practice. Copyright © 2014 John Wiley & Sons, Ltd.     

Stochastic stability of a neural‐net robot controller subject to signal‐dependent noise in the learning rule

We consider a neural network‐based controller for a rigid serial link manipulator with uncertain plant parameters. We assume that the training signal to the network is corrupted by signal‐dependent noise. A radial basis function network is utilized in the feedforward control to approximate the unknown inverse dynamics. The weights are adaptively adjusted according to a gradient descent plus a regulation term (Narendra's e‐modification). We prove a theorem that extends the Yoshizawa D‐boundedness results to the stochastic setting. As in the deterministic setting, this result is particularly useful for neural network robot control when there exists bounded torque disturbances and neural net approximation errors over a known compact set. Using this result, we establish bounds on the feedback gains and learning rate parameters that guarantee the origin of the closed‐loop system is semi‐globally, uniformly bounded in expected value. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive PI secondary control for smart autonomous microgrid systems

In this paper, the authors present a neural‐network‐based distributed secondary control to regulate the output voltage and frequency of a smart autonomous microgrid system. Generally, the secondary controller is implemented in a centralized manner using a fixed‐gain proportional‐plus‐integral controller which may perform well under certain operating conditions only. Also the failure of centralized controller implies no secondary control action for the entire system. The control technique proposed in this paper is a distributed one and makes use of neural network (NN) concept to improve the performance of system. A well‐trained NN supplies the controller with suitable gains according to each operating point. Before training the NN, evolutionary optimization technique, differential evolution, is employed to obtain the optimal gains of controller at each operating load condition which forms the training set for NN. Simulation results show that the proposed controller damps the oscillations caused by load changes, restores the output voltage and frequency of the system to their nominal values, and maintains proper load sharing property of the baseline controller. The performance of the controller is also compared with fixed‐gain controller. Copyright © 2015 John Wiley & Sons, Ltd.     

Design of high‐gain wide‐band harmonic self‐oscillating mixers

In this work, a new design method is presented for the design of wide‐band harmonic self‐oscillating mixers (HSOM) with high conversion gain. The optimum design for the HSOM circuit is obtained using bifurcation analysis‐ and control‐techniques in combination with nonlinear optimization techniques based on the use of an auxiliary generator. The design method is illustrated through the design of an 11.25–1.5 GHz third harmonic self‐oscillating mixer (3HSOM) with a 6.5 dB down‐conversion gain over a 1.6 GHz bandwidth. It is also demonstrated how the frequency variation of the conversion gain can be shaped by means of the use of bifurcation control techniques. A good agreement between the simulated and experimental results has been found. Copyright © 2009 John Wiley & Sons, Ltd.     

Neural‐network‐based finite‐time H∞ control for extended Markov jump nonlinear systems

This paper presents a neural‐network‐based finite‐time H_∞ control design technique for a class of extended Markov jump nonlinear systems. The considered stochastic character is described by a Markov process, but with only partially known transition jump rates. The sufficient conditions for the existence of the desired controller are derived in terms of linear matrix inequalities such that the closed‐loop system trajectory stays within a prescribed bound in a fixed time interval and has a guaranteed H_∞ noise attenuation performance for all admissible uncertainties and approximation errors of the neural networks. A numerical example is used to illustrate the effectiveness of the developed theoretic results. Copyright © 2009 John Wiley & Sons, Ltd.     

Experimental verification for stability improvement of sensorless vector control system of induction motor using real‐time tuning of observer gain

This paper proposes a method for the design of the adaptive rotor flux observer gain to improve stability at low speed and in regenerating mode. The method is based on stability analysis, which utilizes a linearized model considering all systems, including each control loop. Therefore, the proposed method considers the effects of motor constants and control circuit constants. The stability analysis using the transfer function for the rotor speed considers the arrangement of poles and zeros and the steady‐state error. The rotor flux observer gain which improves the stability for each operating condition is ascertained. This paper also proposes a real‐time tuning method for the observer gain. The validity of the proposed method was confirmed by simulation using Matlab Simulink and by experiment. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(1): 67–81, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20715     

Global output‐feedback control of systems with dynamic nonlinear input uncertainties through singular perturbation‐based dynamic scaling

A general class of uncertain nonlinear systems with dynamic input nonlinearities is considered. The system structure includes a core nominal subsystem of triangular structure with additive uncertain nonlinear functions, coupled uncertain nonlinear appended dynamics, and uncertain nonlinear input unmodeled dynamics. The control design is based on dual controller/observer dynamic high‐gain scaling with an additional dynamic scaling based on a singular perturbation‐like redesign to address the non‐affine and uncertain nature of the input appearance in the system dynamics. The proposed approach yields a constructive global robust adaptive output‐feedback control design that is robust to the dynamic input uncertainties and to uncertain nonlinear functions allowed throughout the system structure. Copyright © 2015 John Wiley & Sons, Ltd.