A design method of a generalized predictive control system based on parametrization of two‐degree‐of‐freedom integral controllers

A new design method for a generalized predictive control (GPC) system based on parametrization of two‐degree‐of‐freedom integral controllers has been proposed. The objective is to guarantee stability of the control system without depending on the design parameters and to achieve low sensitivity against the plant perturbation and the disturbance. The design procedure consists of two steps. First, we design a basic integral controller for a nominal plant using the linear quadratic Gaussian (LQG) method and parametrize a class of two‐degree‐of‐freedom stabilizing controllers. Next, we tune the feedforward controller to incorporate the GPC method into our control structure. A numerical example is presented to show the effectiveness of the proposed method by comparing it with the conventional GPC method. © 1999 Scripta Technica, Electr Eng Jpn, 129(2): 62–70, 1999     

Improvement of tracking performance in designing a GPC‐based PID controller using a time‐varying proportional gain

An effective design method of a proportional‐integral‐derivative (PID) controller is proposed. The PID parameters of the PID controller are designed on the basis of a generalized predictive control (GPC) law. The PID controller has a time‐varying proportional gain, and the PID parameters are designed using the future reference trajectory of the GPC. Finally, numerical examples are shown for illustrating the effectiveness of the proposed method. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Robust output tracking of uncertain nonlinear systems with completely unknown dead‐zone input: A self‐tuning design approach

The main purpose of this paper is to propose a direct and simple approach, called a self‐tuning design approach, to dealing with any nonsymmetric dead‐zone input nonlinearity where its information is completely unknown. In order to describe the approach, the output tracking problem is considered for a class of uncertain nonlinear systems with any nonsymmetric dead‐zone input. First, a dead‐zone input is represented as a time‐varying input‐dependent function such that the considered dynamical system with dead‐zone input can be transfered into an uncertain nonlinear dynamical system subject to a linear input with time‐varying input coefficient. Then, by making use of the self‐tuning design approach, a class of adaptive robust output tracking control schemes with a rather simple structure is synthesized. Thus, the proposed direct and simple self‐tuning design approach can be easily understood by the engineering designers, and the resulting simple adaptive robust control schemes can be well implemented in most practical engineering control problems. By combining the proposed self‐tuning design approach with other control methods, one may expect to obtain a number of interesting results for a rather large class of uncertain nonlinear dynamical systems with dead‐zone in the actuators. Finally,the simulations of some numerical examples are provided to demonstrate the validity of the theoretical results. Copyright © 2016 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     

Generalized predictive control for improving intersample performance

This paper proposes a design method of the Generalized Predictive Control (GPC) strategy which can take account of intersample behavior as well as sampled behavior. To this end, a discrete‐time equivalent plant model and cost function for a continuous‐time plant incorporated with zero‐order hold and discrete‐time controller are derived, and the modified discrete‐time GPC is designed. To verify the effectiveness of our proposed method, the controller is applied to a benchmark problem of a hard‐disk drive. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Automatic tuning of robust constrained cross‐direction controllers

The paper machine cross‐directional (CD) process is a large‐scale spatially distributed system. It is known to be severely ill‐conditioned as the gain rolls down to zero for some of the process directions. Model uncertainties in the process are inevitable resulting in a challenging robust control design problem. CD actuators are subject to min–max constraints while slice lip actuators are subject to additional bending moment limits. Because of the large number of input constraints, the industrial practice is to tune the CD controller assuming inactive constraints. The robustness of CD feedback loops to model uncertainties under constrained internal model control satisfies an integral quadratic inequality. This work develops an automatic tuning algorithm that guarantees robust stability and performance of the constrained CD feedback loop. Spatial response models are identified in a prediction error frame delivering bounds on the CD process pseudo‐singular values. The CD controller is synthesized online through a linear matrix inequalities feasibility problem taking into consideration the modal space uncertainty rising from the uncertainties in the estimated parameters and the expected variations in the dynamic response. The developed tuning technique is suitable for paper machines producing different grades of paper as the CD process spatial and dynamic responses change for each grade. The performance of the tuned constrained internal model control controller is validated through comparing it to an industrial CD controller that has been implemented in paper mills as part of a commercial product. Copyright © 2015 John Wiley & Sons, Ltd.     

A design of multivariable,self‐tuning PID controllers with an internal model structure

Self‐tuning control schemes (STC) are useful for systems with unknown or slowly time‐varying parameters. Some single‐input/single‐output PID control schemes based on STCs have been proposed for such systems. However, there are a lot of multivariable systems in real process industries. And these systems often have relatively large time delays. In this paper, a design scheme of self‐tuning PID control system is proposed for multivariable systems with unknown parameters and time delays. The controlled object is equipped with an internal model in order to compensate the time delay and also unstable zeros. Subsequently, a multivariable PID controller is designed for the augmented or compensated system. The PID parameters are calculated recursively based on the relationship between the minimum variance control law and the PID control law. A simulation example is presented to demonstrate the effectiveness of the proposed scheme. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 146(4): 58–64, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10241     

A dual filtering scheme for nonlinear active noise control

Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered‐x least mean squares (LMS) or filtered‐u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness. Copyright © 2013 John Wiley & Sons, Ltd.     

On identification methods for direct data‐driven controller tuning

In non‐iterative data‐driven controller tuning, a set of measured input/output data of the plant is used directly to identify the optimal controller that minimizes some control criterion. This approach allows the design of fixed‐order controllers, but leads to an identification problem where the input is affected by noise, and not the output as in standard identification problems. Several solutions that deal with the effect of measurement noise in this specific identification problem have been proposed in the literature. The consistency and statistical efficiency of these methods are discussed in this paper and the performance of the different methods is compared. The conclusions offer a guideline on how to solve the data‐driven controller tuning problem efficiently. Copyright © 2010 John Wiley & Sons, Ltd.     

基于ITAE指标的PID控制器参数优化设计

研究了比例一微分一积分（PID）控制器参数自整定问题,提出一种基于改进粒子群优化算法的PID控制器参数自整定方法,并以时间乘以误差绝对值积分（ITAE）的性能指标为适应度函数,对具有实参数不确定性的热工对象进行PID控制器参数整定,所设计的PID控制系统的ITAE性能指标比极点法,GPM法,Cohen法,IMC法效果更好,并通过超临界锅炉过热汽温控制的仿真实例验证了方法的有效性。     

Data‐driven model reference control with asymptotically guaranteed stability

This paper presents a data‐driven controller tuning method that includes a set of constraints for ensuring closed‐loop stability. The approach requires a single experiment and can also be applied to nonminimum‐phase and unstable systems. The tuning scheme generates an estimate of the closed‐loop output error that is used to minimize an approximation of the model reference control problem. The correlation approach is used to deal with the influence of measurement noise. For linearly parameterized controllers, this leads to a convex optimization problem. A sufficient condition for closed‐loop stability is introduced, which can be included in the optimization problem for control design. As the data length tends to infinity, closed‐loop stability is guaranteed. The quality of the estimated controller is analyzed for finite data length. The effectiveness of the proposed method is demonstrated in simulation as well as experimentally on a laboratory‐scale mechanical setup. Copyright © 2010 John Wiley & Sons, Ltd.     

Practical aspects of self‐tuning controllers

The contribution presents a class of Single Input Single Output (SISO) discrete self‐tuning controllers suitable for industrial applications. The proposed adaptive controllers can be divided into three groups. The first group covers PID adaptive algorithms with using of traditional methods. The second group is based on polynomial solutions of control problems and the third group is derived from the use of the minimization of linear quadratic criterion. All types of algorithms were unified and incorporated into a Matlab ‐ like Toolbox for self‐tuning control. Copyright © 1999 John Wiley & Sons, Ltd.     

Output‐feedback model‐reference adaptive calibration for map‐based anti‐jerk control of electromechanical automotive clutches

It is well known that the map‐based control can reduce the computational burden of the automotive on‐board controller. This paper proposes an output‐feedback model‐reference adaptive control algorithm to calibrate the map‐based anti‐jerk controller for electromechanical clutch engagement. The algorithm can be used to adaptively construct a data‐driven fuzzy rule base without resorting to manual tuning, so that it can overcome the problem of conventional knowledge‐based fuzzy logic design, which involves strenuous parameter‐tuning work in the construction of calibration maps. To accurately define the consequent of each fuzzy rule for anti‐jerk control, an output feedback law for computing the reference trajectory of clutch engagement is developed to eliminate the discontinuous slip‐stick transition, whereas an adaptive controller is designed to track the reference trajectory and compensate the nonlinearity. The convergence of the proposed output‐feedback model‐reference adaptive control algorithm is analyzed. Simulation results indicate that the proposed method can successfully reduce the excessive vehicle jerk and frictional energy dissipation during clutch engagement as compared with the conventional knowledge‐based fuzzy logic controller without fine tuning.     

Optimal tuning of linear controllers for power electronics/power systems applications

This paper presents a new method for tuning various linear controllers such as Proportional-Integral (PI), Proportional-Integral-Derivative (PID) and Proportional-Resonant (PR) structures which are frequently used in power electronics and power system applications. The linear controllers maintain a general structure defined by the Internal Model Principle (IMP) of control theory. The proposed method in this paper is twofold. The first perspective uses the well-known concept of the Linear Quadratic Regulator (LQR) to address the problem as a regulation problem. The Q matrix of the LQR design is then finely adjusted in order to assure the desired transient response for the system. The second perspective redefines the LQR in order to add capability to address the optimal tracking problem and is then generalized to systems with more than two states. These methods are then applied to two specific examples, one in an uninterruptible power supply (UPS) inverter system and the other one in a distributed generation (DG) system. In these examples, the tuning of PR and PI controllers is studied in great detail. These proposed design methods provide an easy and algorithmic procedure without jeopardizing stability or robustness. These tuning methods can also be utilized for linear state-space realization of any power converters. Both examples are supported via simulation and the results, which confirm analytical derivations, are presented and discussed.     

Design of an Implicit GMV‐PID Controller Using Closed Loop Data

In industrial processes, PID control has been applied in many real systems. The control performance strongly depends on the PID parameters. Although some schemes for tuning PID parameters have been proposed, these schemes need system parameters which are estimated by system identification in order to calculate the PID parameters. On the other hand, model‐free controller design schemes represented by virtual reference feedback tuning (VRFT) or FRIT have received much attention in the last few years. These methods can calculate control parameters using closed loop data and are expected to reduce computational costs. In this paper, a type of implicit PID controller using closed loop data is proposed. In the proposed method, the PID parameters are calculated on the basis of the implicit generalized minimum variance control. The control performance can be suitably adjusted by means of only a user‐specified parameter. The effectiveness of the proposed method is numerically and experimentally evaluated.     

Data‐Driven Controller Tuning for Nonminimum Phase Plants with Stability Constraints

This paper addresses the model reference control problem, which is a typical control problem found in data‐driven controller tuning methods. For nonminimum phase plants, the unstable zeros of the plant should be included in the reference to avoid destabilization of the resulting closed‐loop system and improve tracking performance. First, we propose a data‐driven controller tuning method with closed‐loop stability taken into consideration and with the tuned controller parameters in the time domain. If the plant has unstable zero(s), the proposed method would not lead to destabilizing controller in the worst case. Closed‐loop stability is checked using linear inequalities described with input/output data. This contributes to reducing computation in the proposed method. Moreover, this paper proposes a data‐driven controller tuning method for nonminimum phase plants estimating the unstable zero(s) using a flexible reference model at each parameter update and reflecting them into the resulting reference model. The effectiveness of the proposed method is confirmed through numerical experiments.     

A linear,ultra wideband,low‐power, 2.1–5 GHz,VCO

A linear, Ultra Wideband, low‐power VCO, suitable for UWB‐FM applications is proposed, forming the main part of a UWB‐FM transmitter. The VCO is designed in TSMC 90thinspacenm digital CMOS process and includes a Source‐Coupled Multivibrator, used as current‐controlled oscillator (CCO) which generates output frequencies between 2.1 and 5 GHz and a voltage‐to‐current (V‐to‐I) converter which translates the VCO input voltage modulation signal to current. Two single‐ended inverter buffers are employed to drive either a differential or a single‐ended UWB antenna. The presented VCO is designed for 1 V power supply and exhibits a linear tuning range of 2.1–5 GHz, a differential output power of ?7.83 dBm±0.78 dB and low power consumption of 8.26 mW, including the output buffers, at the maximum oscillation frequency. It is optimized for a very high ratio of tuning range (81.69%) over power consumption equal to 9.95 dB. The desired frequency band of 3.1–5 GHz for UWB‐FM applications is covered for the entire industrial temperature range (?40 to 125^°C). Copyright © 2010 John Wiley & Sons, Ltd.     

PID自动整定和阶梯式GPC在大型电站锅炉中的应用

将PID自动整定和阶梯式广义预测控制（GPC）同时运用到大型工业锅炉的重要控制回路（如汽包水位和主蒸汽压力）中。而阶梯式GPC的应用则突破了以往GPC等方案只用于小型锅炉的控制、大型锅炉仅进行仿真或仅对过热蒸汽温度等回路进行控制的状况。针对在线计算的快速性要求,对GPC进行修改,得到阶梯式GPC方法,使计算量比GPC的递推算法大为减少,使之满足实际应用的要求。2种方法均在某420 t/h锅炉中取得了很好的实际控制效果。     

A practical design of multiloop robust PID control systems

In this paper, a new design scheme of multiloop predictive self‐tuning PID controllers is proposed for multivariable systems. The proposed scheme first uses a static precompensator as an approximately decoupling device in order to roughly reduce the interaction terms of the controlled object. The static matrix precompensator is adjusted by an on‐line estimator. Furthermore, by regarding the approximately decoupled system as a series of single‐input single‐output subsystems, a single‐input single‐output PID controller is designed for each subsystem. The PID parameters are calculated on‐line based on the relationship between the PID control and the generalized predictive control laws. The proposed scheme is numerically evaluated on a simulation example. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 147(3): 63–71, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10284     

基于改进广义预测控制的PMSM速度控制

在机器人控制领域中,对永磁同步电机(PMSM)响应的快速性及稳定性要求较高,同时还需要其控制方法有较强的适应性。但传统的PID控制参数只适用于特定场合,应用受到限制。本文结合广义预测控制(GPC)理论与Luenberger观测器,形成一种新的控制方法来对PMSM进行速度闭环控制。该算法与传统GPC算法相比计算量更少,并通过负载转矩观测器对负载扰动进行测量反馈,提高了系统控制性能。仿真及试验结果表明,该算法与PID控制比超调量小,响应速度快,适用于要求较高的工程应用。