High Performance Controller Design Research Articles

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

CRONE control system design toolbox for the control engineering community: tutorial and case study

Fractional-order differentiation offers new degrees of freedom that simplify the design of high-performance dynamic controllers. The CRONE control system design (CSD) methodology proposes the design of robust controllers by using fractional-order operators. A software toolbox has been developed based on this methodology and is freely available for the international scientific and industrial communities. This paper presents both the CRONE CSD methodology and its implementation using the toolbox. The design of two robust controllers for irrigation canals shows how to use the toolbox.

Hybrid Modeling of a DC-DC Series Resonant Converter: Direct Piecewise Affine Approach

A dc-dc resonant converter has the advantage of overcoming switching losses and electromagnetic interference which are the main limitations of high frequency power converters. Nevertheless, the modeling and stability analysis of dc-dc resonant converters are considerably more complex than pulsewidth modulation counterparts. The conventional averaged linearized model of the resonant converter has limitations due to averaging and linearization. First of all, the linearized model has large modeling error in presence of large variations of reference voltage and input voltage. Furthermore, Converging area for stabilizing controllers is smaller in the averaged model. In order to overcome these limitations, this paper presents a novel framework for modeling of dc-dc resonant converters. As dc-dc resonant converters are naturally switched systems with affine subsystems, a piecewise affine model is derived directly from the converter model. The new model is based on analysis of the resonant converter on state plane trajectories. It is also suitable for precise simulation and high performance controller design of resonant converters. In addition, a stability analysis theorem is provided for the proposed model. The simulation and experimental results on a dc-dc series resonant converter show the effectiveness of this modeling approach.

Sawtooth period control strategies and designs for improved performance

The sawtooth instability is associated with the triggering of neo-classical tearing modes, core fuelling, α-confinement and the exhaust of thermal helium. Sawtooth control is therefore important for optimal reactor performance in ELMy H-modes. Control schemes for the sawtooth period have been published in the literature, but the systematic design of high-performance controllers (yielding accurate and fast convergent responses) has not been addressed. In this work, three control strategies for high-performance sawtooth control are presented using electron cyclotron current drive (ECCD). Both degrees of freedom of the ECCD actuator will be explored and combined with advanced controller designs. First, the ECCD deposition location is used as a control variable, for which a gain-scheduled feedback controller and static feedforward control is derived. Second, the use of the driven current as a control variable is explored, and a simple controller is designed based on the identified dynamics. In the third approach both control variables are joined in an overall controller design, which enables the combination of high-performance control of the sawtooth period and control of the gyrotron power. Time-domain simulations with a combined Kadomtsev–Porcelli sawtooth model show that each strategy obtains a better closed-loop performance than standard linear feedback techniques on merely the deposition location.

상태공간 모델링에 의한 공작기계용 수냉각기의 최적제어기 설계

ABSTRACT:Typical temperature control methods of a cooler for machine tools are hot-gas bypass and compressor variable speed control. The hot-gas bypass system has been widely used to control the cooler temperature in many general industrial fields. On the contrary, the compressor variable speed control is focused on special fields such as aerospace and high precision machine tools which need high precision control. The variable speed control system usually has two control variables such as target temperature and superheat. In other words, the variable speed control sys-tem is basically multi-input multi-output(MIMO) system. In spite of MIMO system, the proportional integral derivative(PID) feedback control methodology that based on single-input single-output (SISO) system is generally used for designing the variable speed control system. Therefore, it is inevitable to describe transfer functions for dynamic behaviors of every controlled variables and decide the PID gains with tremendous iteration process. Moreover, the designed PID gains do not provide optimum system performances. To solve these problems, high performance controller design method based on a state space model is suggested in this paper. An optimum controller is designed to minimize both control errors and energy inputs. This method was more simple to describe dynamic behaviors and easier to design the cooler controller which is MIMO system.Keywords:Water cooler(수냉각기), Optimum control(최적제어), State space model(상태공간 모델), Compressor variable speed control(압축기 가변속 제어), Multi-input multi- output system(다입력 다출력 시스템), State variable(상태변수)

Integral Resonant Control for Vibration Damping and Precise Tip-Positioning of a Single-Link Flexible Manipulator

In this paper, we propose a control design method for single-link flexible manipulators. The proposed technique is based on the integral resonant control (IRC) scheme. The controller consists of two nested feedback loops. The inner loop controls the joint angle and makes the system robust to joint friction. The outer loop, which is based on the IRC technique, damps the vibration and makes the system robust to the unmodeled dynamics (spill-over) and resonance frequency variations due to changes in the payload. The objectives of this work are: 1) to demonstrate the advantages of IRC, which is a high-performance controller design methodology for flexible structures with collocated actuator-sensor pairs and 2) to illustrate its capability of achieving precise end-point (tip) positioning with effective vibration suppression when applied to a typical flexible manipulator. The theoretical formulation of the proposed control scheme, a detailed stability analysis and experimental results obtained on a flexible manipulator are presented.

Control-oriented model of a complex irrigation main canal pool

AbstractA control-oriented model of a complex irrigation main canal pool (plant), which is characterized by the exhibition of large variations in their dynamic parameters when the discharge regime changes in the operating range [Qmin, Qmax], has been developed using system identification for control procedure and also using real-time data. The control-oriented model is comprised by the nominal model of the true plant and for its uncertainty region bounded by the true plant models under low and high operating discharge regimes (limit operating models). The model is obtained from the use of both experimental data, which correspond to main operating discharge regimes of the true plant, and the Prediction Error framework. The results are very encouraging since control-oriented models facilitate the design of high-performance robust controllers, which allow the operability and efficiency of irrigation main canal pools to be increased and service to the users to be improved.

Dynamic modelling and simulation of a hot strip finishing mill

Hot rolling is an essential industrial process in the production of sheet steel, a widely used product in manufacturing and construction. A finishing mill performs a set of operations in a hot strip rolling mill, and is a complex unit including many processes and control loops. Its modelling is a challenging task due to the variety of phenomena that occur within the mill, and variable transport delays. Model validation is also challenging due to a scarcity of measurements. On the other hand, a dynamic model that adequately reflects the numerous interactions between the mill units can be very useful for tasks such as high performance control design or vibration analysis. In this study, a one-dimensional model has been developed and validated against real plant data. The end use of the model is intended to be looper control analysis, but the model is kept sufficiently general so that it can be used or easily extended for other applications.

Closed-Loop System Identification with Genetic Algorithms

High-performance control design for a flexible space structure is challenging as highfidelity plant models are difficult to obtain a priori. Uncertainty in the control design models typically require a very robust, low-performance control design, which must be tuned onorbittoachievetherequiredperformance.Closed-loopsystemidentificationisoftenrequired to obtain a multivariable open-loop plant model based on closed-loop response data. To provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is used to mitigate the nonuniqueness and overparameterization of general state space realizations. This controlrelevant system identification procedure stresses the joint nature of the system identification and control design problem by seeking to obtain a model that minimizes the difference between the predicted and actual closed-loop performance.

High-Purity Distillation

Distillation is one of the most common separation techniques in chemical manufacturing. This multi-input, multi-output staged separation process is strongly interactive, as determined by the singular value decomposition of a linear dynamic model of the system. Process dynamics associated with the low-gain direction are critical to the design of high-performance controllers for high-purity distillation but are difficult to estimate from conventional experimental test signals for identification. As a result, high-purity distillation columns are considered challenging cases for multivariable system identification and robust control system design. High-purity distillation is a challenging process application for system identification because of its nonlinear and strongly interactive dynamics. This article has described several constrained-optimization-based formulations for multisine input signal design that allow users to simultaneously specify the essential frequency- and time-domain properties of these signals. Because constraints are explicitly part of the design procedure, the approach is useful for accomplishing plant-friendly identification testing in the process industries. The problem formulations were evaluated for a highly nonlinear methanol-ethanol distillation column. Introducing directional sinusoids in the multisine signal, applying a closed-loop signal design, and minimizing an objective function based on Weyl's theorem enhanced the information content of the low-gain direction in the identification experiment.

Design framework for high-performance optimal sampled-data control with application to a wafer stage

Control design for high-performance sampled-data systems with continuous time performance specifications is investigated. Direct optimal sampled-data control design explicitly addresses both the digital controller implementation and the intersample behaviour. The model that is required for direct optimal sampled-data control should evolve in continuous time. Accurate models for control design, however, generally evolve in discrete time since they are obtained by means of system identification techniques. The purpose of this paper is the development of a control design framework that enables the usage of models delivered by system identification techniques, while explicitly addressing both the digital controller implementation and the intersample behaviour aspects. Thereto, the incompatibility of the models delivered by system identification techniques and the models used in sampled-data control is analysed. To use models delivered by system identification techniques in conjunction with optimal sampled-data control, tools are employed that stem from multirate system theory. For the actual control design, key theoretical issues in sampled-data control, which include the linear periodically time-varying nature of sampled-data systems, are addressed. The control design approach is applied to the -optimal feedback control design of an industrial high-performance wafer scanner. Experimental results illustrate the necessity of addressing the intersample behaviour in high-performance control design.

Loop-shaping controller design from input-output data: application to a paper machine simulator

The main motivation for this work was the need for a systematic design of high-performance controllers for paper machines. An integrated identification and controller design approach, based on loop-shaping principles, is presented. Starting with time-domain input-output data, a coprime factorization of the plant is identified using linear least-squares techniques. The residual error from the identification is then used to obtain estimates of the coprime factor uncertainty that are translated to sensitivity and complementary sensitivity bounds for the closed-loop system. These bounds serve as a guide for the selection of a target loop and the controller is designed using H-infinity tools. The application of this approach is demonstrated on Honeywell's high-fidelity simulator. The simulations demonstrate the suitability of the approach and illustrate that the technique can be used to reliably provide high performance for both servo and regulatory control.

Identification and control — Closed-loop issues

An overview is given of some current research activities on the design of high-performance controllers for plants with uncertain dynamics, based on approximate identification and model-based control design. In dealing with the interplay between system identification and robust control design, some recently developed iterative schemes are reviewed and special attention is given to aspects of approximate identification under closed-loop experimental conditions.

Integrator backstepping techniques for the tracking control of permanent magnet brush DC motors

An integrator backstepping technique is introduced for the design of high-performance motor controllers. The approach is applied to the design of embedded computed torque and output feedback controllers for permanent magnet brush DC (BDC) motors. The proposed controllers are simulated and experimentally verified on a user-developed digital signal processor (DSP) based data acquisition and control (DAC) system. Although the BDC motor is well researched, the results motivate extensions of the proposed techniques to the development of similar controllers for more complex electromechanical systems, such as separately excited DC, permanent magnet stepper, brushless DC, switched reluctance, and AC induction servo motors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Identification and Control - Closed Loop Issues

In this paper an overview is given of some current research activities on the design of high performance controllers for plants with uncertain dynamics, based on approximate identification and model-based control design. In dealing with the interplay between system identification and robust control design, some recently developed iterative schemes are reviewed and special attention is given to aspects of approximate identification under closed loop experimental conditions.

Integrated modeling and controller design with application to flexible structure control

It is well known that modeling and control problems are not independent. Hence, the final step in any practical control design is a ‘try and see’ procedure. There will always be a need for on-line tuning by the test engineer. The purpose of mathematical theories of modeling and control is therefore not to replace the ‘try and see’ requirement of the engineer, but to help reduce the number of iterations the engineer must use to find a suitable solution to the real physical problem (which defies exact mathematical description). This paper presents an iterative method to integrate the two problems of modeling and control and demonstrates the procedure in a physical application. Specifically, we present a controller design scheme integrating a Q-Markov Covariance equivalent realization (QMC) identification algorithm, a Modal Cost Analysis (MCA) model reduction algorithm, and an Output Variance Constraint (OVC) controller design algorithm. The identified model is used as a truth model for subsequent (off-line) controller evaluation. In the first step of the integrated procedure, this model is reduced for controller design. Closed-loop evaluation provides information to improve the reduced model (this is the integration of the two model reduction and control disciplines that is contributed by this paper). The process repeats iteratively for low order and high performance controller design. This procedure is applied to NASA's ACES flexible structure at the Marshall Space Flight Center. The experimental results demonstrate the effectiveness of the procedure for large flexible structure control.

Integrating computer architectures into the design of high-performance controllers

Modern control systems must typically perform real-time identification and control, as well as coordinate a host of other activities related to user interaction, on-line graphics, and file management. This paper discusses five global design considerations that are useful to integrate array processor, multimicroprocessor, and host computer system architectures into versatile, high-speed controllers. Such controllers are capable of very high control throughput, and can maintain constant interaction with the non-real-time or user environment. As an application example, the architecture of a high-speed, closed-loop controller used to actively control helicopter vibration will be briefly discussed. Although this system has been designed for use as the controller for real-time rotorcraft dynamics and control studies in a wind-tunnel environment, the controller architecture can generally be applied to a wide range of automatic control applications.

A Model of Full Penetration Arc-Welding for Control System Design

A model of the dynamics of full penetration welding is presented that relates the heat input to the resulting width of the backbead. It is developed from a heat balance using the pool as a control volume, and is applicable primarily to autogenous gas-tungsten arc welding (GTAW). Two means of modulating the input are considered: 1) varying the torch current and 2) varying the torch travel velocity. The proposed model in both cases is first order, but it has non-constant) parameters (i.e., gain and time constant). Regardless of which input is used, the gain and time constant of the system are shown to depend strongly upon the thickness of the material, the preheat temperature, and the nominal torch velocity. These trends were confirmed in a series of open-loop step tests, where gain and time constant were directly measured, and in closed-lop tests, where step and frequency response methods were used. The resulting model permits rational high performance controller design, and the variable parameters of the system suggest the need for parameter adaptive control.

High-Gain Error-Actuated Controllers for Linear Multivariable Plants with Explicit Actuator Dynamics

Simple closed-form formulas are presented for the transfer function matrices of controllers and associated phase-lead compensators which greatly facilitate the design of high-performance high-gain error-actuated controllers for a practically important class of linear multivariable plants with explicit actuator dynamics.