Showing papers on "Control theory published in 1999"

An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

Abstract: This paper describes an experiment on the “linguistic” synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

Stationary frame current regulation of PWM inverters with zero steady state error

Abstract: Current regulators for AC inverters are commonly categorised as hysteresis, linear PI or deadbeat predictive, with a further subclassification into stationary ABC frame and synchronous DQ frame implementations. Synchronous frame controllers are generally accepted to have a better performance than stationary frame controllers do, as they operate on DC quantities and hence can eliminate steady state errors. This paper establishes a theoretical connection between these two classes of regulators and proposes a new type of stationary frame controller, which achieves the same steady state performance as a synchronous frame controller. The new controller is applicable to both single phase and three phase inverters.

Dual current control scheme for PWM converter under unbalanced input voltage conditions

Abstract: Voltage unbalance in a three-phase system causes performance deterioration of PWM power converters by producing 120 Hz voltage ripples in the DC link and by increasing the reactive power. To eliminate the DC link voltage ripple and the DC component of the reactive power, both positive- and negative-sequence currents should be controlled simultaneously, according to the paper by Rioual et al (1996). The authors used two synchronous reference frames: a positive-sequence current regulated by a proportional integral (PI) controller in a positive synchronous reference frame (SRF); and a negative-sequence current regulated by a PI controller in a negative SRF. In the positive SRF, which rotates counterclockwise, the positive sequence appears as DC, while the negative sequence appears as 120 Hz. In contrast, in the negative SRF, which rotates clockwise, the negative sequence appears as DC, while the positive sequence appears as 120 Hz. By deleting 120 Hz components using a notch filter in each SRF, one can measure positive- and negative-sequence currents separately, and use them for constructing two feedback controllers. Since the negative-sequence current is also controlled in its own SRF by a DC command, this approach yields better performance without increasing the control gain. Note that, since the controller is implemented by a software routine in the digital signal professor chip, using two SRFs does not require additional hardware. The authors demonstrated the effectiveness of the proposed control scheme by using computer simulation and experiments.

Disturbance observer and feedforward design for a high-speed direct-drive positioning table

Abstract: Design and implementation of a discrete-time tracking controller for a precision positioning table actuated by direct-drive motors is considered. The table has acceleration capabilities in excess of 5 G, positioning accuracy at the micron level, and is used in applications such as semiconductor packaging. The controller proposed uses a disturbance observer and proportional derivative (PD) compensation in the feedback path and a zero phase error tracking controller and zero phase low-pass filter in the feedforward path. The existing disturbance observer design techniques are extended to account for time delay in the plant. Practical difficulties with excessive feedforward gains are examined and a low-order filter design method is proposed. Experimental results for quantized low-order position reference trajectories, which are commonly used in industrial systems, demonstrate the effectiveness of the approach.

A model-based algorithm for blood glucose control in Type I diabetic patients

Abstract: A model-based-predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump.

Stabilizing controller design for uncertain nonlinear systems using fuzzy models

Abstract: A Lyapunov-based stabilizing control design method for uncertain nonlinear dynamical systems using fuzzy models is proposed. The controller is constructed using a design model of the dynamical process to be controlled. The design model is obtained from the truth model using a fuzzy modeling approach. The truth model represents a detailed description of the process dynamics. The truth model is used in a simulation experiment to evaluate the performance of the controller design. A method for generating local models that constitute the design model is proposed. Sufficient conditions for stability and stabilizability of fuzzy models using fuzzy state feedback controllers are given. The results obtained are illustrated with a numerical example involving a four-dimensional nonlinear model of a stick balancer.

Feedback control of quantum systems using continuous state estimation

Abstract: We present a formulation of feedback in quantum systems in which the best estimates of the dynamical variables are obtained continuously from the measurement record, and fed back to control the system. We apply this method to the problem of cooling and confining a single quantum degree of freedom, and compare it to current schemes in which the measurement signal is fed back directly in the manner usually considered in existing treatments of quantum feedback. Direct feedback may be combined with feedback by estimation, and the resulting combination, performed on a linear system, is closely analogous to classical linear-quadratic-Gaussian control theory with residual feedback.

A control-based middleware framework for quality-of-service adaptations

Abstract: In heterogeneous environments with performance variations present, multiple applications compete for and share a limited amount of system resources and suffer from variations in resource availability. These complex applications are desired to adapt themselves and to adjust their resource demands dynamically. On one hand, current adaptation mechanisms built within an application cannot preserve global properties such as fairness; on the other hand, adaptive resource management mechanisms built within the operating system are not aware of data semantics in the application. In this paper, we present a novel middleware control framework to enhance the effectiveness of quality-of-service (QoS) adaptation decisions by dynamic control and reconfiguration of internal parameters and functionalities of a distributed multimedia application. Our objective is to satisfy both system-wide properties (such as fairness among concurrent applications) and application-specific requirements (such as preserving the critical performance criteria). The framework is modeled by the task control model and the fuzzy control model, based on rigorous results from the control theory, and verified by the controllability and adaptivity of a distributed visual tracking application. The results show validation of the framework, i.e., critical application quality parameters can be preserved via controlled adaptation.

Fractional-Order Systems and -Controllers

Abstract: Dynamic systems of an arbitrary real order (fractional- order systems) are considered. A concept of a fractional-order - controller, involving fractional-order integrator and fractional-order dif- ferentiator, is proposed. The Laplace transform formula for a new function of the Mittag-Leffler-type made it possible to obtain explicit analytical expressions for the unit-step and unit-impulse response of a linear fractional-order system with fractional-order controller both for the open and closed loop. An example demonstrating the use of the obtained formulas and the advantages of the proposed - controllers is given.

Adaptive neural network control of nonlinear systems by state and output feedback

Abstract: This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions.

The interline power flow controller concept: a new approach to power flow management in transmission systems

Abstract: The interline power flow controller (IPFC) proposed is a new concept for the compensation and effective power flow management of multi-line transmission systems. In its general form, the IPFC employs a number of inverters with a common DC link, each to provide series compensation for a selected line of the transmission system. Because of the common DC link, any inverter within the IPFC is able to transfer real power to any other and thereby facilitate real power transfer among the lines of the transmission system. Since each inverter is also able to provide reactive compensation, the IPFC is able to carry out an overall real and reactive power compensation of the total transmission system. This capability makes it possible to equalize both real and reactive power flow between the lines, transfer power from overloaded to underloaded lines, compensate against reactive voltage drops and the corresponding reactive line power, and to increase the effectiveness of the compensating system against dynamic disturbances. The paper explains the basic theory and operating characteristics of the IPFC with phasor diagrams, P-Q plots and simulated waveforms.

Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems

Abstract: In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.

Adaptive robust motion control of single-rod hydraulic actuators: Theory and experiments

Abstract: High performance robust motion control of single-rod hydraulic actuators is considered. In contrast to the double-rod hydraulic actuators studied previously, the two chambers of a single-rod hydraulic actuator have different areas. As a result, the dynamic equations describing the pressure changes in the two chambers cannot be combined into a single load pressure equation. This complicates the controller design since it not only increases the dimension of the system to be dealt with but also brings in the stability issue of the added internal dynamics. A discontinuous projection based adaptive robust controller (ARC) is constructed. The controller is able to take into account not only the effect of parameter variations coming from the inertia load and various hydraulic parameters but also the effect of hard-to-model nonlinearities such as uncompensated friction forces and external disturbances. Extensive experimental results are obtained for the swing motion control of a hydraulic arm. In comparison to a state-of-the-art industrial motion controller, the proposed ARC algorithm achieves more than a magnitude reduction of tracking errors. Furthermore, during constant velocity and regulation periods, the ARC controller reduces the tracking errors almost down to the measurement resolution level.

Inverse optimal stabilization of a rigid spacecraft

Abstract: The authors present an approach for constructing optimal feedback control laws for regulation of a rotating rigid spacecraft. They employ the inverse optimal control approach which circumvents the task of solving a Hamilton-Jacobi equation and results in a controller optimal with respect to a meaningful cost functional. The inverse optimality approach requires the knowledge of a control Lyapunov function and a stabilizing control law of a particular form. For the spacecraft problem, they are both constructed using the method of integrator backstepping. The authors give a characterization of (nonlinear) stability margins achieved with the inverse optimal control law.

PID tuning for improved performance

Abstract: In this paper, a simple PID controller design method that achieves high performance for a wide range of linear self-regulating processes is proposed. Satisfactory responses can be expected for processes with various dynamics, including those with low- and high-order, small and large dead time, and monotonic and oscillatory responses. The method is developed based on a second-order plus dead time modeling technique and a closed-loop pole allocation strategy through the use of root-locus. Simulation examples and real-time experiments are given to show the effectiveness and flexibility of the controller in handling processes of different characteristics.

Autotuning of PID Controllers: A Relay Feedback Approach

Abstract: Introduction.- Relay Feedback Approach.- Systems with Unstable Zeros or Poles.- Multiloop SISO Control.-. Multivariable Composition.- Load Disturbances.- Process Nonlinearity.- Controller Monitoring.-. Application to Plantwise Control.- Guidelines of Autotune Procedure.- Appendix .

Analysis of SVC and TCSC controllers in voltage collapse

Abstract: This paper presents detailed steady-state models with controls of two flexible AC transmission system (FACTS) controllers, namely, static VAr compensators (SVCs) and thyristor controlled series capacitors (TCSCs), to study their effect on voltage collapse phenomena in power systems. Based on results at the point of collapse, design strategies are proposed for these two controllers, so that their location, dimensions and controls can be optimally defined to increase system loadability. A European system is used to illustrate the application of all proposed models and techniques.

Neural network output feedback control of robot manipulators

Abstract: A robust neural network output feedback scheme is developed for the motion control of robot manipulators without measuring joint velocities. A neural network observer is presented to estimate the joint velocities. It is shown that all the signals in a closed-loop system composed of a robot, an observer, and a controller is uniformly ultimately bounded. This amounts to a separation principle for the design of nonlinear dynamic trackers for robotic systems. The neural network weights in both the observer and the controller are tuned online, with no off-line learning phase required. No exact knowledge of the robot dynamics is required so that the neural network controller is model-free and so applicable to a class of nonlinear systems which have a similar structure to robot manipulators. Simulation results on 2-link robot manipulator are reported to show the performance of the proposed output feedback control scheme.

Sliding mode measurement feedback control for antilock braking systems

Abstract: We describe a nonlinear observer-based design for control of vehicle traction that is important in providing safety and obtaining desired longitudinal vehicle motion. First, a robust sliding mode controller is designed to maintain the wheel slip at any given value. Simulations show that longitudinal traction controller is capable of controlling the vehicle with parameter deviations and disturbances. The direct state feedback is then replaced with nonlinear observers to estimate the vehicle velocity from the output of the system (i.e., wheel velocity). The nonlinear model of the system is shown locally observable. The effects and drawbacks of the extended Kalman filters and sliding observers are shown via simulations. The sliding observer is found promising while the extended Kalman filter is unsatisfactory due to unpredictable changes in the road conditions.

Trajectory tracking control of a four-wheel differentially driven mobile robot

Abstract: We consider the trajectory tracking control problem for a 4-wheel differentially driven mobile robot moving on an outdoor terrain. A dynamic model is presented accounting for the effects of wheel skidding. A model-based nonlinear controller is designed, following the dynamic feedback linearization paradigm. An operational nonholonomic constraint is added at this stage, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The controller is then robustified, using conventional linear techniques, against uncertainty in the soil parameters at the ground-wheel contact. Simulation results show the good performance in tracking spline-type trajectories on a virtual terrain with varying characteristics.

Experiments and simulations on the nonlinear control of a hydraulic servosystem

Abstract: This paper presents the derivation, simulation, and implementation of a nonlinear tracking control law for a hydraulic servosystem. An analysis of the nonlinear system equations is used in the derivation of a Lyapunov function that provides for exponentially stable force trajectory tracking. This control law is then extended to provide position tracking. The proposed controller is simulated and then implemented on an experimental hydraulic system to test the limits of its performance and the realistic effects of friction.

New methodology for analytical and optimal design of fuzzy PID controllers

Abstract: Describes a methodology for the systematic design of fuzzy PID controllers based on theoretical fuzzy analysis and, genetic-based optimization. An important feature of the proposed controller is its simple structure. It uses a one-input fuzzy inference with three rules and at most six tuning parameters. A closed-form solution for the control action is defined in terms of the nonlinear tuning parameters. The nonlinear proportional gain is explicitly derived in the error domain. A conservative design strategy is proposed for realizing a guaranteed-PID-performance (GPP) fuzzy controller. This strategy suggests that a fuzzy PID controller should be able to produce a linear function from its nonlinearity tuning of the system. The proposed PID system is able to produce a close approximation of a linear function for approximating the GPP system. This GPP system, incorporated with a genetic solver for the optimization, will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria. Two indexes, linearity approximation index (LAI) and nonlinearity variation index (NVI), are suggested for evaluating the nonlinear design of fuzzy controllers. The proposed control system has been applied to several first-order, second-order, and fifth-order processes. Simulation results show that the proposed fuzzy PID controller produces superior control performance to the conventional PID controllers, particularly in handling nonlinearities due to time delay and saturation.

Modeling and controlling variation propagation in mechanical assemblies using state transition models

Abstract: Presents algorithms to propagate and control variation in mechanical assemblies using the state transition model approach. It exploits the modeling environment and uses concepts from control theory to model variation propagation and control during assembly. The assembly process is modeled as a multistage linear dynamic system. Two types of assemblies are addressed: Type-1 where the assembly process puts together parts at their pre-fabricated mating features and Type-2 where the process can incorporate in-process adjustments to redistribute variation. Algorithms are developed to determine and control variation in final assembly propagated through the combined effect of individual part variations and choice of assembly methods. Algorithms to propagate variation in the presence of adjustments are also presented. In-process adjustments in Type-2 assemblies are determined by the type of interface features between parts being assembled which are modeled as control inputs to the dynamic system. An optimal control problem is formulated to design these interfaces. Variation associated with final assembly dimensions, cost of making adjustments, and assembly sequence effects are included in the optimization procedure.

Global adaptive output feedback control of induction motors with uncertain rotor resistance

Abstract: The authors design a global adaptive output feedback control for a fifth-order model of induction motors, which guarantees asymptotic tracking of smooth speed references on the basis of speed and stator current measurements, for any initial condition and for any unknown constant value of torque load and rotor resistance. The proposed seventh-order nonlinear compensator generates estimates both for the unknown parameters (torque load and rotor resistance) and for the unmeasured state variables (rotor flux); they converge to the corresponding true values under persistency of excitation which actually holds in typical operating conditions. The control algorithm generates references for the magnetizing flux component and for the torque component of stator current which lead to significant simplification for current-fed motors. Simulations show that the proposed controller is suitable for high dynamic performance applications.

Autotuning of PID Controllers

Abstract: Any books that you read, no matter how you got the sentences that have been read from the books, surely they will give you goodness. But, we will show you one of recommendation of the book that you need to read. This autotuning of pid controllers is what we surely mean. We will show you the reasonable reasons why you need to read this book. This book is a kind of precious book written by an experienced author.