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.     

An indirect model reference adaptive controller based on the multirate sampling of the plant output

The use of sampled-data multirate-output controllers for model reference adaptive control of possibly non-stably invertible linear systems with unknown parameters is investigated. Multirate-output controllers contain a multirate sampling mechanism with different sampling period at each system output. Such a control allows us to assign an arbitrary discrete-time transfer function matrix for the sampled closed-loop system and does not make assumptions on the plant other than controllability, observability and the knowledge of two sets of structural indices, namely the controllability and the observability indices. An indirect adaptive control scheme based on these sampled-data controllers is proposed which estimates the unknown plant parameters (and consequently the controller parameters) on-line from sequential data of the inputs and the outputs of the plant, which are recursively updated within the time limit imposed by a fundamental sampling period T₀. Using the proposed adaptive algorithm, the model reference adaptive control problem is reduced to the determination of a fictitious static state feedback controller owing to the merits of multirate-output controllers. Known indirect model reference adaptive control techniques usually resort to the direct computation of dynamic controllers. The controller determination reduces to the simple problem of solving a linear algebraic system of equations, whereas in known indirect model reference adaptive control techniques, matrix polynomial Diophantine equations usually need to be solved. Moreover, persistent excitation of the continuous-time plant is provided without making any special richness assumption on the reference signals.     

Artificial intelligence-based speed control of DTC induction motor drives—A comparative study

The design of the speed controller greatly affects the performance of an electric drive. A common strategy to control an induction machine is to use direct torque control combined with a PI speed controller. These schemes require proper and continuous tuning and therefore adaptive controllers are proposed to replace conventional PI controllers to improve the drive's performance. This paper presents a comparison between four different speed controller design strategies based on artificial intelligence techniques; two are based on tuning of conventional PI controllers, the third makes use of a fuzzy logic controller and the last is based on hybrid fuzzy sliding mode control theory. To provide a numerical comparison between different controllers, a performance index based on speed error is assigned. All methods are applied to the direct torque control scheme and each control strategy has been tested for its robustness and disturbance rejection ability.     

Minimum variance in fast,slow and dual‐rate control loops

In certain industrial applications, the control updating rate is faster than the output sampling rate by a certain factor, which leads to dual‐rate (DR) control problems. Generally speaking, a DR controller performs better than a slow single rate (SSR) controller but worse than a fast single rate (FSR) controller in the sense of minimum variance control. This conjecture is theoretically justified in this paper for a continuous linear time‐invariant (LTI) single‐input single‐output (SISO) system. The optimal FSR, DR and SSR controllers are designed under the same performance criterion: variance of the fast sampled output. The discretization of continuous stochastic disturbance models is investigated preserving certain basic statistical properties. A linear matrix inequality (LMI) approach is developed to calculate the optimal controllers for DR and SSR loops. The theoretical results are illustrated by two simulation examples. Copyright © 2005 John Wiley & Sons, Ltd.     

Fault estimation and accommodation for networked control systems with nonuniform sampling periods

This paper deals with the problem of fault estimation and accommodation for a class of networked control systems with nonuniform uncertain sampling periods. Firstly, the reason why the adaptive fault diagnosis observer cannot be applied to networked control systems is analyzed. Based on this analysis, a novel robust fault estimation observer is constructed to estimate both continuous‐time fault and system states by using nonuniformly discrete‐time sampled outputs. Furthermore, using the obtained states and fault information, a nonuniformly sampled‐data fault tolerant control law is designed to preserve the stability of the closed‐loop system. The proposed scheme can not only guarantee the impact of continuous‐time uncertainties and discrete‐time sampled estimation errors on the faulty system to satisfy a H _∞ performance index but also repress the negative effect of the unknown intersample behavior of continuous‐time fault by use of an inequality technique. Finally, simulation results are included to demonstrate the feasibility of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

改进的大纯滞后过程的内模控制器研究

常规内模控制器具有克服时滞的优点,但是当被控对象与模型严重失配情况下,常规内模控制器的控制往往难以达到满意的效果.针对具有此种情况的大纯滞后工业过程,提出了一种将内模控制与双口控制相结合的改进的内模控制器设计方法.这种结构包含两个独立的控制器,适当设计两个控制器可取得比常规内模控制更好的控制效果[1].仿真研究表明该控制器在动态性能、静态性能、鲁棒性及抗干扰性等方面具有很好的控制品质.     

Global hybrid modeling and control of a buck converter: A novel concept

Several attempts have been made to design suitable controllers for DC–DC converters. However, these designs suffer from model inaccuracy or their inability to desirably function in both continuous and discontinuous current modes. This paper presents a novel switching scheme based on hybrid modeling to control a buck converter using mixed logical dynamical (MLD) methodologies. The proposed method is capable of globally controlling the converter in both continuous and discontinuous current modes of operation by considering all constraints in the physical plant such as maximum inductor current and capacitor voltage limits. Different loads and input voltage disturbances are simulated in MATLAB and results are presented to demonstrate the suitability of the controller. The transient and steady‐state performance of the closed‐loop control over a wide range of operating points shows satisfactory operation of the proposed controller. Copyright © 2008 John Wiley & Sons, Ltd.     

Adaptive control of discrete‐time nonlinear systems by recurrent neural networks in quasi‐sliding mode like regime

The aim of this study was to design an adaptive control strategy based on recurrent neural networks (RNNs). This neural network was designed to obtain a non‐parametric approximation (identification) of discrete‐time uncertain nonlinear systems. A discrete‐time Lyapunov candidate function was proposed to prove the convergence of the identification error. The adaptation laws to adjust the free parameters in the RNN were obtained in the same stability analysis. The control scheme used the states of the identifier, and it was developed fulfilling the necessary conditions to establish a behavior comparable with a quasi‐sliding mode regime. This controller does not use the regular form of the switching function that commonly appears in the sliding mode control designs. The Lyapunov candidate function to design the controller and the identifier simultaneously requires the existence of positive definite solutions of two different matrix inequalities. As consequence, a class of separation principle was proven when the RNN‐based identifier and the controller were designed by the same analysis. Simulations results were designed to show the behavior of the proposed controller solving the tracking problem for the trajectories of a direct current (DC) motor. The performance of the proposed controller was compared with the solution obtained when a classical proportional derivative controller and an adaptive first‐order sliding mode controller assuming poor knowledge of the plant. In both cases, the proposed controller showed superior performance when the relation between the tracking error convergence and the energy used to reach it was evaluated. Copyright © 2016 John Wiley & Sons, Ltd.     

Precise position control of an ultrasonic motor using the PID controller combined with NN

The input‐output characteristic of an ultrasonic motor (USM) has nonlinear elements and changes with a temperature rise or fluctuation of load‐mass. Therefore, it is difficult to accomplish satisfactory control performance by using conventional PID controllers. In this paper, we propose a PID controller combined with a neural network (NN‐PID controller). In this design method, the controller gains consist of constant gains of the PID controller and variable gains of the NN controller. The weights of the NN are adjusted by the backpropagation method so that the control error can be minimized. This method does not require long learning time of the NN. The effectiveness of the proposed design method is confirmed by experiments using an existing USM. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 146(3): 46–54, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10199     

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     

Design of a PID Controller for a PCR Micro Reactor

Proportional-integral-derivative (PID) controllers are widely used in process control, and consequently they are described in most of the textbooks on automatic control. However, rather than presenting the overall design process, the examples given in such textbooks are intended to illuminate specific focused aspects of selection, tuning and implementation of the controller. This paper describes in detail the design of a PID controller for temperature control of a polymerase chain reaction (PCR) microreactor showing how different aspects, which necessarily are taught separately, interact in a real-world design. After setting the design targets by taking the hardware limitations into consideration, a continuous time controller, having two degrees of freedom, is designed by placing its dominant pair of poles using the root locus technique. Then, the integrator wind-up is addressed, the controller is translated into a discrete time version and, after implementation, the experimental performances are measured.      

A design method of generalized minimum variance control in a multi‐rate system

Generalized minimum variance control is designed in a multi‐rate system where the sampling interval of a plant output is longer than the holding interval of a control input. Because new design parameters are introduced, intersample ripples in the multi‐rate system are suppressed by weighting the variation in the control input. Furthermore, the proposed method is an extension of a conventional design method. Finally, simulation results are illustrated in order to show the effectiveness of the proposed method. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Sampled‐data adaptive control of a class of nonlinear systems

The paper considers the discrete‐time implementation of a class of backstepping adaptive controllers for uncertain nonlinear systems in the parametric strict‐feedback form. The stability of the resultant sampled‐data system cannot be guaranteed when the direct discretization of the continuous‐time backstepping controller is applied to the same system via a sampling and hold device. Therefore, the paper has presented a sampled‐data control scheme which involves first modifying the existing continuous‐time controller and then discretizing it using the forward Euler method. It has been shown that the proposed control guarantees in a semi‐global sense the boundedness of all the variables of the overall sampled‐data system under some conditions. Particularly, the state of the nonlinear system to be controlled can converge to any arbitrarily small neighbourhood of the origin. Copyright © 2011 John Wiley & Sons, Ltd.     

基于电压源换流器的高压直流输电系统离散化建模与仿真研究

基于电压源换流器的高压直流输电(voltage source converter based high voltage direct current transmission,VSC-HVDC)的工业控制系统通常为微机控制器,该控制器的控制对象是系统的离散化模型。为此提出了一种在dq0同步旋转坐标系下的VSC-HVDC离散化状态空间模型。在此基础上建立了VSC电流内环离散化控制器和外环控制器,并实现了有功电流和无功电流的解耦控制;为提高VSC-HVDC的外环控制器响应速度,在外环控制器中引入了前馈控制。基于电磁暂态仿真软件PSCAD/EMTDC建立了VSC-HVDC模型及其离散化控制器模型。仿真结果验证了离散模型的正确性以及控制策略的有效性。     

Design of self-tuning controllers for a two terminal HVDC link

This paper presents the design of self-tuning controllers for a two terminal HVDC link. The controllers are designed utilizing a novel discrete-time converter model based on multirate sampling. The nature of converter firing system necessitates the development of a two-step ahead self-tuning control strategy. A two terminal HVDC system study has been carried out to show the effectiveness of the control strategies proposed which include the design of minimum variance controller, pole assigned controller and PLQG controller. The coordinated control of a two terminal HVDC system has been established deriving the signal from inverter end current and voltage which has been estimated based on the measurements of rectifier end quantities only realized through the robust reduced order observer. A well known scaled down sample system data has been selected for studies and the controllers designed have been tested for worst conditions. The performance of self-tuning controllers has been evaluated through digital simulation.     

不稳定大时滞过程的串级PID控制

针对一类不稳定大时滞过程,提出了一种基于内模控制（IMC）框架下改进的串级PID控制结构,给出了参数设计的一般准则。系统双闭环均按照IMC原理设计为经典反馈控制结构,内环控制器主要用于镇定不稳定对象和抑制主要干扰,外环用于改善系统性能。内外环控制器均只用一个调节参数,调节参数和对象参数与闭环系统性能解析关系明确。仿真表明,该方法能有效控制滞后时间与时间常数之比大于1的大时滞过程,鲁棒性强,结构设计和调节简单,适合工程应用。     

Design of robust controllers for HVDC links in AC-DC power systems

The paper presents the design of a very simple fuzzy logic controller for HVDC transmission links for fast stabilization of transient oscillations. The performance of this controller is compared with variable-structure and adaptive controllers for a variety of short-circuits and system operational changes. Unlike both adaptive and variable-structure controllers which require, at least functionally, an accurate model of the system dynamics, the fuzzy controller does not require a mathematical model of the system to estimate the control input under disturbances. The transient simulation studies presented in this paper reveal the superior performance of the simple fuzzy controller in damping out system transients.     

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.     

Constrained MPC to track time‐varying reference signals: Online optimization of virtual reference signals and controller states

In this paper, we propose a model predictive control (MPC) algorithm for linear systems with constraints to track time‐varying reference signals. The proposed controller is assumed to have an integrator as a servo compensator. In the proposed control approach, the value of the integrator state is optimally initialized at each sampling time to improve tracking control performance until the cost function becomes sufficiently small. Moreover, while the cost function is sufficiently small, the integral action is used to achieve offset‐free tracking. The control algorithm is reduced to a convex optimization problem with linear matrix inequality constraints. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

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.