Model-following Control Research Articles

A Study on the SELENE-B Model-Following Control System Using the Flying Test Bed.

The future SELENE (SELenological and ENgineering Explorer) mission is planned to make a soft-landing on the lunar surface. The lunar lander is called "SELENE-B." For preliminary terrestrial landing experiments, the flying test bed (FTB) was developed and the first flight experiment finished, which was aimed at validation of the whole FTB flight system. Other experiments are planned for validation of the SELENE-B onboard hardwares, such as sensor systems, and softwares for control systems. In order to validate those systems, it is desirable for the FTB to have the same dynamic characteristics as the SELENE-B. In this paper we are concerned with the controller of the FTB which achieves the SELENE-B model-following system using the FTB. Some numerical simulations are provided to show the effectiveness of the proposed SELENE-B model-following system.

Position Control of a LBDCM Drive

In this paper, the dynamic model estimation and quantitative position control of a linear brushless DC motor (LBDCM) drive are studied. First, the inverter-fed LBDCM drive is established and its dynamic model is estimated from measurements. Then, according to the estimated model at nominal case, a two-degree-of-freedom controller (2DOFC) is designed to meet the prescribed tracking and load regulation position control responses. As the system parameter and operating condition changes occur, an output feedback linear model following controller (MFC) is added to reduce the effects of perturbations, and hence the performance degradation can be reduced. Finally, a ramp position command with suitable changing rate is generated to avoid the occurrence of velocity limitation, and thus the overshoot of position response due to large-step command change can be eliminated.

Model-following Control of Nonlinear Servo Systems Using Neural Networks

The most useful property of neural networks for system identification and control is their ability to approximate arbitrary nonlinear mappings. In this paper, a new neural network model-following control method is proposed for nonlinear servo systems. First, three-layer artificial neural net works are trained to describe the forward and in verse dynamic characteristics of one type of speci fied nonlinear servo systems by using the backpropagation algorithm. Then the neural net work inverse model is used as a feedforward con troller for model-following control of the nonlin ear servo systems. Computer simulation results obtained from MATLAB verify the applicability of neural network identification and control for nonlinear servo systems.

A VSS speed controller with model reference response for induction motor drive

This paper is mainly concerned with the development of a variable-structure system (VSS) controller with model reference speed response for an induction motor drive. An indirect-field-oriented (IFO) induction motor drive is first implemented, and its dynamic model at a nominal operating condition is estimated from measured data. Then, a two-degrees-of-freedom linear model-following controller (2DOFLMFC) is designed to meet the prescribed tracking and load regulation speed responses at the nominal case. As the variations of system parameters and operating condition occur, the prescribed control specifications may not be satisfied further. To improve this, a VSS controller is developed to generate a compensation control signal to reduce the control performance degradation. The proposed VSS controller is easy to implement, since only the output variable is sensed. The existence condition of sliding-mode control is derived, and the chattering suppression during the static period is also considered. Good model-following tracking and load regulation speed responses are obtained by the designed VSS controller. Effectiveness of the proposed controller and the performance of the resulting drive system are confirmed by some simulation and measured results.

Robust fuzzy model-following control of robot manipulators

A robust fuzzy model-following control system is proposed for the control of robot manipulators. The application field to n-link robot manipulators with torque disturbance and measurement noise is addressed. The control objective is obtained by tailoring a nominal adaptation process of parameters to implement appropriate function approximation and facilitating a self-tuning mechanism on the consequent membership functions to overcome the equivalent uncertainty. A novel fuzzy system with self-tuning mechanism provides robust property and the rule-base in the form of "IF situation THEN the control input". The proposed multilayer fuzzy logic controller can improve both transient and stability margins without a priori knowledge about the dynamic model or parameters of the robotic system. Using the Lyapunov stability method, the uniform ultimate boundedness of tracking error has been proved. The performance is demonstrated by simulating the control of a two-link robot in various situations.

Linear Structure Vibration Control Using MFT and Robust Kalman-Filter-Based OMFC Method

ABSTRACTThis paper proposes a robust Kalman-filter-based optimal model-following control (OMFC) methodology for actively suppressing the vibration of the mechanical structure system, which is modeled by the modal force technique (MFT) and subjects to both disturbance/noise uncertainties and linear structured time-varying parameter perturbations. The proposed method can not only avoid the problem of how to choose appropriate weighting matrices in the quadratic cost function of the linear quadratic Gaussian (LQG) control but also make the controlled closed-loop system to have desired system response characteristics. Besides, this paper also presents a robust stability criterion to guarantee that the designed controller can keep the controlled mechanical structure system from the possibility of time-varying-parameter-perturbation-induced instability. An example of L-shaped cantilever beam structure system is employed to demonstrate the application of the proposed method and the use of the robust stability criterion. Numerical simulation is performed to evaluate the improvement of the vibration response.

Neurofuzzy-model-following control of MIMO nonlinear systems

A neurofuzzy logic controller with a compensating neural network and a fine-tuning mechanism in the consequent membership functions is proposed to design the model-following control of MIMO nonlinear systems. The control strategy is developed to facilitate interconnection compensation among subsystems by the compensating neural network and to realise feedback linearisation by online function approximation. By tailoring the fine-tuning mechanism to overcome the equivalent uncertainty appearing within subsystems or as a result of plant uncertainty, function approximation error, external disturbances, or measurement noise, the system is robust to some extent. The overall neurofuzzy control system is proved to be uniform ultimate bounded by using Lyapunov stability theory. Simulation results of a two-link manipulator demonstrate the effectiveness and robustness of the proposed controller.

DSP-based minmax speed sensorless induction motor drive with sliding mode model-following speed controller

A speed sensorless induction motor (IM) drive with a sliding mode model-following speed controller is introduced. First, the speed estimation algorithm, based on the minmax control theory, and its stability analysis are described. An integral-proportional (IP) speed controller using the estimated speed signal is designed to obtain the desired control performance of command tracking. Moreover, to increase the robustness of the IM drive, a sliding mode model-following controller is added, where the state-space equation of the nominal designed IP speed control loop is selected as the reference model. To increase the accuracy of the estimator, the speed estimation algorithm is implemented using a digital signal processor.

A toggle mechanism servo system with variable-structure model-following control

A toggle mechanism, which is driven by a permanent-magnet synchronous servo motor, with variable-structure model-following control (VSMFC) is proposed in this study. The rod and crank of the toggle mechanism are assumed to be rigid. The Hamilton principle and L agrange multiplier methods are applied to formulate the equation of motion. Based on the principles of the adaptive model-following control and the variable-structure control, a VSMFC system is developed to control the position of a slider of the toggle mechanism servo system. Numerical and experimental results show that the dynamic behaviour of the proposed controller-motor-mechanism system is robust to parametric variations and external disturbances.

A novel model-following scheme with simple structure for electrical position servo systems

A novel model-following scheme with a simple structure for electrical position servo systems is proposed. It is quite different from and much simpler than existing model-following control (MFC) schemes. The nominal model of a plant is used as the reference model in this scheme. The common external input to the reference model and to the plant is not fed from the reference command but from a model controller which is designed for the nominal model of the plant to ensure a satisfactory reference output trajectory. The following of the plant to the reference model with the expected output trajectory is guaranteed using a correction signal from an adaptive mechanism which has a simple linear PID structure, taking only the generalized output error as its input. Conditions for asymptotic model-following are first developed rigorously and then implemented. The system design method is described. Simulation and experimental results show that the proposed MFC scheme is effective and robust against both parameter variations and load disturbance of the actual plant. The comparison of the MFC system with position PI and PID control systems illustrates the superiority of the proposed MFC scheme over the conventional PI/PID control scheme.

Active control for installation of underwater structures

The trend towards deepwater development requires a new approach to underwater installation of offshore structures. The present method using crane vessels has some drawbacks in operations at more than 2000 m depth. The natural period of the coupled system of the rigged structure and the crane vessel becomes longer, so that it is no longer possible to manipulate the cranes to achieve the desired positioning accuracy. This paper examines the application of an active control technique for underwater installations as one of the solutions to the present problems. An active control technique also has the advantage that it can deal with the structural flexibility which allows the structure to be large and light-weight. This structural flexibility imposes problems of suppressing the elastic responses and securing the stability of the control system. In this paper, anH ∞ controller combined with a low authority control/high authority control (LAC/HAC) feedback controller is designed for cases where structural flexibility cannot be ignored. A robust model-following controller is examined for cases where the structure can be treated as rigid. In order to confirm the control algorithm and verify the possibility of the active control installation method, basin tests are executed using two types of neutrally buoyant flexible models with ultrasound ranging systems and thrusters.

Robustness enhancement of an optimal fuzzy control electrical position servo system by a novel model-following approach

In this paper, an optimal fuzzy model-following control (OFMFC) scheme for position servo systems is presented. A fuzzy model controller (FMC) is used to generate an optimal reference output trajectory and a proportional-integral-derivative adaptation mechanism is included to force the plant output to follow the optimal model output. Both design of the FMC and that of the PID adaptation mechanism are addressed. Numerical results illustrate that the proposed OFMFC scheme can retain optimal system performance even in the presence of significant parameter variations and load disturbance. The comparison between an OFMFC system and an optimal fuzzy control (OFC) system shows that the OFMFC scheme is much more robust than the pure OFC scheme.

Robust observer-based OMF vibration control of flexible linkage mechanisms using piezoelectric films

Based on the state-space model of the flexible linkage mechanism equipped with piezoelectric films, a robust control methodology for actively suppressing the elastodynamic responses of the high-speed flexible linkage mechanism with linear structured time-varying parameter perturbations by employing an observer-based optimal model-following (OMF) controller is presented. The advantage of the proposed robust observer-based OMF control methodology is that it not only can avoid the problem of how to choose the appropriate weighting matrices in the quadratic cost function of the linear-quadratic/linear-quadratic-Gaussian (LQ/LQG) control method but also can make the controlled closed-loop system to have the specified system response characteristics. Besides, in order to guarantee that the designed observer-based OMF controller can make the controlled flexible linkage mechanism system to avoid the possibilities of both spillover-induced instability and time-varying-parameter-perturbation-induced instability, a robust stability criterion is also presented in this paper. The control method, presented in this paper, can not only make the controlled closed-loop system to have the specified system response characteristics, but also guarantee the controlled closed-loop system to have robust stability properties by using the proposed robust stability criterion; while those control methods reported recently do not have these above-mentioned merits. Finally, an active robust vibration control problem of a slider-crank mechanism is provided for illustrating the applicability of the proposed method.

Quantitative design and implementation of PI-D controller with model-following response for motor drive

The authors present the quantitative design and implementation of a PI-D 2-degrees-of-freedom controller (2DOFC) for motor drives. The proposed 2DOF controller consists of a PI-D feedback (also called the PI plus rate feedback) controller and a command feedforward controller. A quantitative design procedure is derived to find the parameters of a 2DOF controller systematically according to the given motor drive specifications. In addition to the tracking and regulation speed control specifications, the effects of command change rate as well as control effort are also considered in the proposed design procedure. As the operation condition changes occur, a model-following controller (MFC) is added to preserve the control specifications. The analysis and design of the proposed controller for the speed control of an induction motor drive are described in detail, and some simulation and measured results are provided to show its performance.

Traction control of electric vehicle: basic experimental results using the test EV "UOT electric march"

The most distinct advantage of the electric vehicle is its quick and precise torque generation. However, most electric vehicles developed to date have not yet utilized this feature. In this paper, two novel traction control techniques of an electric vehicle using this advantage are proposed. One is the model-following control and the other is the optimal slip ratio control. The basic effectiveness of the proposed methods is demonstrated by real experiments using the DC-motor-driven test vehicle "UOT (University of Tokyo) Electric March".

A fuzzy neural network controller for parallel-resonant ultrasonic motor drive

A newly designed driving circuit for the traveling-wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a current-source two-phase parallel-resonant inverter, is presented in this study. Moreover, since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a fuzzy neural network (NN) controller is proposed to control the USM drive system. In the proposed controller, a fuzzy model-following controller is implemented to control the rotor position of the USM, and an online trained NN with variable learning rates is implemented to tune the output scaling factor of the fuzzy controller. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the desired variable learning rates. From the experimental results, accurate tracking response can be obtained by the proposed controller, and the influences of parameter variations and external disturbances on the USM drive also can be reduced effectively.

Induction motor servo drive with adaptive rotor time-constant estimation

An indirect filed-oriented induction motor (IM) position servo drive with adaptive rotor time-constant estimation and an on-line trained neural network controller is presented. First, the rotor time-constant is estimated real-time on the basis of the model reference adaptive system (MRAS) theory. Next, a linear model-following controller (LMFC) is designed according to the estimated plant model to allow the state responses of the plant to follow the reference model. Then an augmented signal generated from the proposed neural network controller, whose connective weights are trained on-line according to the model-following error of the states, is added to the LMFC system to preserve a favorable model-following characteristic under various operating conditions.

Ultrasonic motor servo-drive with online trained neural-network model-following controller

An ultrasonic motor (USM) servo-drive with an online trained neural-network model-following controller is proposed. First, the driving circuit for the USM, which is a two-phase chopper-inverter combination, is introduced. Since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, an online trained neural-network model-following controller is proposed to control the rotor position of the USM. An accurate tracking response can be obtained by random initialisation of the weights and biases of the network owing to the powerful online learning capability. Moreover, the influences of parameter variations and external disturbances of the USM servo-drive can be effectively reduced by the neural-network controller.

Advances in rotorcraft system identification

System identification can best be described as the extraction of system characteristics from measured flight test data. Therefore it provides an excellent tool for determining and improving mathematical models for a wide range of applications. The increasing need for accurate models for the design of high bandwidth control systems for rotorcraft has initiated a high interest in and a more intensive use of system identification. This development was supported by the AGARD FVP Working Group 18 on ‘Rotorcraft System Identification’, which brought together specialists from research organisations and industry, tasked with exploring the potential of this tool. In the Group, the full range of identification approaches was applied to dedicated helicopter flight-test-data including data quality checking and the determination and verification of flight mechanical models. It was mainly concentrated on the identification of six degrees of freedom rigid body models, which provide a realistic description of the rotorcraft dynamics for the lower and medium frequency range. The accomplishment of the Working Group has increased the demand for applying these techniques more routinely and, in addition, for extending the model order by including explicit rotor degrees of freedom. Such models also accurately characterize the higher frequency range needed for high bandwidth control system designs. In the specific case of the DLR In-Flight Simulator BO 105 ATTHeS, the application of the identified higher order models for the model-following control system was a major prerequisite for the obtained high simulation quality.

Driving circuit for ultrasonic motor servo drive with variable-structure adaptive model-following control

A novel driving circuit is proposed to provide two-phase balance voltage for the travelling-wave type ultrasonic motor (USM). Moreover, the variable-structure adaptive model-following control is applied for the position control of the USM. First, the single-phase equivalent model of the USM is described. Next, the novel driving circuit, which is a two-phase chopper–inverter combination, is designed. Since the lumped dynamic model of the USM is difficult to obtain and the motor parameters are time varying, the variable-structure adaptive model-following control is applied to design the position controller of the USM to reduce the influence of parameter variations and external disturbances. The effectiveness of the proposed controller is demonstrated by experimental results.