Model-reference Control Systems Research Articles

Mixed H2/H∞ model reference control of linear parameter‐varying discrete‐time systems with real‐time application to aeropendulum system

AbstractThe investigation of this paper revolves around the problem of designing mixed model reference control (MRC) systems of linear parameter‐varying discrete‐time systems. In this paper, two types of mixed MRC systems are considered. These two types are the mixed state‐feedback and the mixed dynamic output‐feedback MRC systems that are designed using a parameter‐dependent Lyapunov function. The experimental results of the aeropendulum system are used to evaluate and show the effectiveness of the proposed controllers. The results indicate the satisfactory performances of the closed‐loop systems.

Wireless stepper motor control and optimization based on robust control theory

<img src="blob:http://ijra.iaescore.com/f3842e5c-715f-4e1e-9ffd-952309f5ccb1" alt="" />Stepper motors are broadly utilized in actual systems, which are marked by non-linear parameters such as internal, external noises and uncertainties from wireless network. As well, a suitable controller is required when the problem is to track the target signal. In this paper, robust controller based on model reference are investigated to wireless control and optimize position and time in stepper motors. The core impression to build a robust controller is to use a model reference control system. Furthermore, simulations are implemented to control stepper motor position and time in two cases: first, when the wireless network without any delay and packet dropout. Second, uncertain equations when the wireless network with time delays and packet dropout. Simulation results demonstrate that proposed controller has achieved and enhanced the performance in tracking and robustness.

Robust model reference control of linear parameter‐varying systems with disturbances

This paper deals with the design and analysis of robust model reference control systems for a class of linear parameter-varying (LPV) systems with disturbances. Two types of robust model reference control systems are proposed to achieve a prescribed tracking performance. The first type is the LPV H ∞ state-feedback model reference control system and the second type is the LPV H ∞ output-feedback model reference control system. Based on the notion of quadratic stability, necessary and sufficient conditions for the existence of LPV reference models are given. Also, a parameterisation of LPV reference models is derived in this study. A practical example of a single-link flexible-joint robot is given to illustrate the design steps and to show the effectiveness of the proposed LPV H ∞ model reference controllers.

Learning vector quantization neural network–based model reference adaptive control method for intelligent lower-limb prosthesis

This article focuses on the design of a control system for intelligent prostheses. Learning vector quantization neural network–based model reference adaptive control method is employed to implement real-time trajectory tracking and damp torque control of intelligent lower-limb prosthesis. The method is then analyzed and proposed. A model reference control system is first built with two learning vector quantization neural networks. One neural network is used for output prediction, and the other is used for input control. The angle information of the prosthetic knee joint is utilized to train these two neural networks with the given learning algorithm. The testing results of different movement patterns verify the effectiveness of the proposed method and its suitability for intelligent lower-limb prostheses.

Output-feedback MRAC of networked control systems with packet dropout

In this article, a technique of output-feedback model reference adaptive control for networked control systems is developed. The key issues of networked control systems such as channel bandwidth and data-packets dropout induced by the insertion of data networks in the feedback adaptive control loops are considered. The advantage of this article over earlier ones is that the combination of different aspects in networked control systems, output-feedback model reference control of systems with unknown parameters, and unknown data-packets dropout. Error models, adaptive laws, and stability analysis are derived in the case of uncertainty due to data-packets dropout. The applicability of the approach is demonstrated in a practical numerical example of a ship-steering adaptive system.

Fuzzy Control of a Novel Type of Translational Meshing Motors

Owing to the nonlinear characteristic of a novel type of translational meshing motor with model uncertainties, a model reference control system which consists of a neural network and a fuzzy controller is used. The torque model is identified based on BP neural network, and then Fuzzy controller works as the controller. The description of the control system and training procedure of the neural network are given. The test results obtained for a torque control scheme suitable for the control of the motor are also presented to verify the effectiveness of the proposed nonlinear control scheme. It has been found that the fuzzy control system is able to work reliably.

Development of Power-Assisted Wheelchair with H.INF. Control to Maintain Robustness Against Load Uncertainty

In this paper, a power-assisted wheelchair to support helpers is presented. The model reference control system is proposed such that the wheelchair comfortably moves at the ideal speed with helper's weak force, and H infinity controller is implemented in order to highly maintain the control quality regardless of the disturbance such as the variation of a passenger's mass or the slope of the road. The effectiveness of the proposed system is validated through various experiments such as the comparison with the PI controller, change of patient, the up-and down-slope, and the rotary motion.

Model-reference control of chaotic systems

Abstract In this paper, the problem of controlling chaotic systems is studied. A control law is introduced for a chaotic system to follow a desired reference system. The control strategy is developed within the general framework of the nonlinear model-reference control systems. Lyapunov stability is used to ensure the global stability of the error dynamics represents the difference between the desired and chaotic systems.

An Adaptive Tuning Strategy of PID Attitude Control System

This paper describes a procedure for tuning PID attitude control system of a launch vehicle. The key idea is a method based on a model reference adaptive system to estimate PID control gains. The launch vehicle is considered a rigid body. Since the performance specification is considered as a second order system and the close loop of the launch vehicle PID attitude control system has fourth order with two zeros, there is no matching between model reference and launch vehicle control system. The simulation results of the proposed tuning method are compared with a LQR method.

Model Reference Adaptive Tension Control in a Papermaking Machine

Presented is an adaptive tension control system design study. Model reference adaptive control designs using MIT rule and PI methods to yield the control parameter adjustment mechanism. Lyapunov techniques are used to develop a model-reference control system that stabilizes the tension. A novel model of changing the length of spans by employing magnetic bearing is developed to allow fast and accurate adjustment of tension using mechatronics concepts. Simulation results compare the tension setpoint regulation provided by the proposed control strategy with PID tension feedback control.

Robust Model Reference Control Systems with Online Adjustable Parameters

In this paper, we consider a design of the model reference control systems with adjustable parameters for linear systems with time-varying parametric uncertainties. To discuss this problem, we focus on the output error between the plant and the reference model, and adopt the reachable domain of this error as a performance index. This reachable domain is known as an output reachable set. Based on the analysis technique of output reachable sets, we propose a new design technique of the model reference controller, and demonstrate its efficacy by numerical case studies.

モデル規範制御系における外乱除去法の過渡特性とロバスト性

モデル規範制御系における外乱除去法の過渡特性とロバスト性

Variable structure control for MRAC systems with perturbations in input and output channels

A design scheme of variable structure model reference control systems using only input and output measurements is presented for the systems with unmodeled dynamics and disturbances in input and output channels. The modeled part of the systems has relative degree greater than one and unknown upper bound of degree. By introducing some auxiliary signals and normalized, signals with memory functions and appropriate choice of controller parameters, the developed variable structure controller guarantees the global stability of the closed-loop system and the arbitrarily small tracking error.

Adaptive Hysteresis Model for Model Reference Control with Actuator Hysteresis

When working with active materials which exhibit profound hysteresis, such as shape memory alloys, the “ perfect” mathematicalrepresentationofthehysteresisdoesnotexist.However,wecanrepresentmanyhysteretictrends by means of operator models that vary in their theoretical, physical, and computational complexity, depending on how precisely they modelthehysteresis. In previousstudies by theauthors,generalized Preisach representations of the hysteresis phenomena by use of Krasnosel’ skii and Pokrovskii (KP) operators have been represented in linear parametric form. This parameterized KP model has been successfully implemented with a gradient-adaptive law foron-lineidentie cationand adaptivecompensation when thehysteresisoutputcanbemeasured. Theapplicability of the parameterized KP model is extended to model reference control systems with hysteresis actuators whose output cannot be measured.

EXPONENTIAL STABILITY AND GUARANTEED TRACKING TIME FOR A CLASS OF MODEL REFERENCE CONTROL SYSTEMS VIA COMPOSITE FEEDBACK CONTROL

In this note the concept of the tracking time, a measure of the transient behaviour, for model reference control systems is introduced. A model reference composite state feedback control is proposed to guarantee the global exponential stability with any pre-specified convergence rate for a class of model reference control systems with time-varying delay, matched uncertainties and input non-linearity. Moreover, an estimate of the tracking time is derived for such systems. A numerical example is also given to illustrate our main result.

Nonlinear control of optical fiber diameter variations

This paper presents a simulation-based investigation of nonlinear control of optical fiber diameter variations. A model of the fiber drawing process is presented and model reference control (MRC) and quasi-nonlinear control (QNC) systems are developed, based on a nonlinear dynamical model of the fiber drawing process. Results of numerical simulation indicate that the control schemes developed in this paper provide the potential for the manufacture of optical fibers at higher speeds. Numerical experiments also indicate that the QNC methodology is robust in the presence of significant model uncertainties and external disturbances.

Fast neural learning and control of discrete-time nonlinear systems

The problem of learning control for a general class of discrete-time nonlinear systems is addressed in this paper using multilayered neural networks (MNNs) with feedforward connections. A suitable extension of the concept of input-output linearization of discrete-time nonlinear systems is used to develop the control schemes for both output tracking and model reference control purposes. The ability of MNNs to model arbitrary nonlinear functions is incorporated to approximate the unknown nonlinear input-output relationship and its inverse using a new weight learning algorithm. In order to overcome the difficulties associated with simultaneous online identification and control in neural networks based adaptive control systems, the new learning control architectures are developed for both adaptive tracking and adaptive model reference control systems with online identification and control ability. The potentials of the proposed methods are demonstrated by simulation examples.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A New Approach to the Design of Digital Model Reference Control Systems

A New Approach to the Design of Digital Model Reference Control Systems

Model Reference Control of Non-Linear Plants in the Presence of Plant Uncertainties

A model reference control (MRC) scheme based on the hyperstability theory for a class of input-output linearizable non-linear plants is proposed. It is shown that a switching slate feedback gain in this algorithm can reject the effect of the plant uncertainties, if the bounds of the plant uncertainties are known. Hence the boundedness of all signals in the proposed MRC system can be ensured.

Stability regions of a model reference control system

Introduction I NSTABILITIES in linear systems can be generated by changing the parameters of the system periodically. This phenomenon is called parametric excitation. Perhaps the best known example is the Mathieu equation (see McLachlan). Parametric excitation can also occur in adaptive systems, Even the simplest cases are very hard to analyze analytically. Attempts to compute the stability regions have been made earlier (see James). These results have been used as examples in, e.g., Mareels and Astrom. However, the results presented in James' paper are not completely accurate, as a new investigation has shown.