Showing papers on "Open-loop controller published in 2003"

Scaling and bandwidth-parameterization based controller tuning

Abstract: A new set of tools, including controller scaling, controller parameterization and practical optimization, is presented to standardize controller tuning. Controller scaling is used to frequency-scale an existing controller for a large class of plants, eliminating the repetitive controller tuning process for plants that differ mainly in gain and bandwidth. Controller parameterization makes the controller parameters a function of a single variable, the loop-gain bandwidth, and greatly simplifies the tuning process. Practical optimization is defined by maximizing the bandwidth subject to the physical constraints, which determine the limiting factors in performance. Collectively, these new tools move controller tuning in the direction of science.

Direct repetitive control of SPWM inverter for UPS purpose

Abstract: A novel repetitive controller directly combined with an open loop SPWM inverter is presented in this paper. To cope with the high-resonant peak of the open loop inverter that may cause instability, a zero-phase-shift notch filter other than the inverse transfer function of the inverter or a conventional second-order filter is incorporated in the controller. The proposed method has good harmonic rejection and large tolerance to parameter variations. To further reduce the steady-state error, a low-pass-filter Q(z) algorithm is applied. The DC bias problem is also taken into consideration and solved with the repetitive controller itself. The method is implemented with a digital signal processor and achieves low THD% (1.4%-1.7%) with nonlinear loads and fast error convergence (3-5 fundamental periods). It proves to be a cost-effective solution for common UPS products where high-quality output voltage is more stressed than fast dynamic response.

Robust nonlinear model predictive control of batch processes

Abstract: NMPC explicitly addresses constraints and nonlinearities during the feedback control of batch processes. This NMPC algorithm also explicitly takes parameter uncertainty into account in the state estimation and state feedback controller designs. An extended Kalman filter estimates the process noise covariance matrix from the parameter uncertainty description and employs a sequential integration and correction strategy to reduce biases in the state estimates due to parameter uncertainty. The shrinking horizon NMPC algorithm minimizes a weighted sum of the nominal performance objective, an estimate of the variance of the performance objective, and an integral of the deviation of the control trajectory from the nominal optimal control trajectory. The robust performance is quantified by estimates of the distribution of the performance index along the batch run obtained by a series expansion about the control trajectory. The control and analysis approaches are applied to a simulated batch crystallization process with a realistic uncertainty description. The proposed robust NMPC algorithm improves the robust performance by a factor of six compared to open loop optimal control, and a factor of two compared to nominal NMPC. Monte Carlo simulations support the results obtained by the distributional robustness analysis technique.

Automated lane change controller design

Abstract: The primary focus of study in this paper is the background control theory for automated lane change maneuvers. We provide an analytic approach for the systematic development of controllers that will cause an autonomous vehicle to accomplish a smooth lane change suitable for use in an Automated Highway System. The design is motivated by the discontinuous availability of valid preview data from the sensing systems during lane-to-lane transitions. The task is accomplished by the generation of a virtual yaw reference and the utilization of a robust switching controller to generate steering commands that cause the vehicle to track that reference. In this way, the open loop lane change problem is converted into an equivalent virtual reference trajectory tracking problem. The approach considers optimality in elapsed time at an operating longitudinal velocity. Although the analysis is performed assuming that the road is straight, the generalization of the proposed algorithm to arbitrary road segments is rather straightforward. The outlined lane change algorithm has been implemented and tested on The Ohio State University test vehicles. Some of the experimental results are presented at the conclusion of this paper.

Analysis and design of direct power control (DPC) for a three phase synchronous rectifier via output regulation subspaces

Abstract: In this paper, the authors present a controller that directly regulates the active and instantaneous reactive power in a synchronous three-phase boost-type rectifier. The controller ensures a good regulation of the output voltage, and guarantees the power factor close to one. The controller builds upon the ideas of the well known direct torque control (DTC) for induction motors. In their case, the active and reactive powers replace the torque and flux amplitude used as the controlled outputs in DTC, thus motivating the name DPC-control. They show that a simple modification to the original algorithm makes the selection of the control inputs more accurate. To formalize this technique, they utilize the concept of output regulation subspaces. A modification is added to the basic controller to deal with disturbances such as unbalance and distortion in the source voltage. Finally, the proposed controller was tested both in simulations and experimentally, and illustrative results are presented.

Simulink implementation of induction machine model - a modular approach

Abstract: In this paper, a modular Simulink implementation of an induction machine model is described in a step-by-step approach. With the modular system, each block solves one of the model equations; therefore, unlike black box models, all of the machine parameters are accessible for control and verification purposes. After the implementation, examples are given with the model used in different drive applications, such as open-loop constant V/Hz control and indirect vector control are given. Finally, the use of the model as an Induction generator is demonstrated.

Self-learning fuzzy sliding-mode control for antilock braking systems

Abstract: The antilock braking system (ABS) is designed to optimize braking effectiveness and maintain steerability; however, the ABS performance will be degraded in the case of severe road conditions. In this study, a self-learning fuzzy sliding-mode control (SLFSMC) design method is proposed for ABS. The SLFSMC ABS will modulate the brake torque for optimum braking. The SLFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to compensate for the approximation error between the ideal controller and the fuzzy controller. The tuning algorithms of the controller are derived in the Lyapunov sense; thus, the stability of the system can be guaranteed. Also, the derivation of the proposed SLFSMC ABS does not need to use a vehicle-braking model. Simulations are performed to demonstrate the effectiveness of the proposed SLFSMC ABS in adapting to changes for various road conditions.

Mixed-sensitivity approach to H/sub /spl infin// control of power system oscillations employing multiple FACTS devices

Abstract: This paper demonstrates the enhancement of inter-area mode damping by multiple flexible AC transmission systems (FACTS) devices. Power system damping control design is formulated as an output disturbance rejection problem. A decentralized H/sub /spl infin// damping control design based on the mixed-sensitivity formulation in the linear matrix inequality (LMI) framework is carried out. A systematic procedure for selecting the weights for shaping the open loop plant for control design is suggested. A 16-machine, five-area study system reinforced with a controllable series capacitor (CSC), a static VAr compensator (SVC), and a controllable phase shifter (CPS) at different locations is considered. The controllers designed for these devices are found to effectively damp out inter-area oscillations. The damping performance of the controllers is examined in the frequency and time domains for various operating scenarios. The controllers are found to be robust in the face of varying power-flow patterns, nature of loads, tie-line strengths, and system nonlinearities, including device saturations.

Neural Network Control

Abstract: This thesis addresses two neural network based control systems. The first is a neural network based predictive controller. System identification and controller design are discussed. The second is a direct neural network controller. Parameter choice and training methods are discussed. Both controllers are tested on two different plants. Problems regarding implementations are discussed. First the neural network based predictive controller is introduced as an extension to the generalised predictive controller (GPC) to allow control of non-linear plant. The controller design includes the GPC parameters, but prediction is done explicitly by using a neural network model of the plant. System identification is discussed. Two control systems are constructed for two different plants: A coupled tank system and an inverse pendulum. This shows how implementation aspects such as plant excitation during system identification are handled. Limitations of the controller type are discussed and shown on the two implementations. In the second part of this thesis, the direct neural network controller is discussed. An output feedback controller is constructed around a neural network. Controller parameters are determined using system simulations. The control system is applied as a single-step ahead controller to two different plants. One of them is a path-following problem in connection with a reversing trailer truck. This system illustrates an approach with step-wise increasing controller complexity to handle the unstable control object. The second plant is a coupled tank system. Comparison is made with the first controller. Both controllers are shown to work. But for the neural network based predictive controller, construction of a neural network model of high accuracy is critical especially when long prediction horizons are needed. This limits application to plants that can be modelled to sufficient accuracy. The direct neural network controller does not need a model. Instead the controller is trained on simulation runs of the plant. This requires careful selection of training scenarios, as these scenarios have impact on the performance of the controller.

Implementation of an artificial-neural-network-based real-time adaptive controller for an interior permanent-magnet motor drive

Abstract: This paper presents the implementation of an artificial-neural-network (ANN)-based real-time adaptive controller for accurate speed control of an interior permanent-magnet synchronous motor (IPMSM) under system uncertainties. A field-oriented IPMSM model is used to decouple the flux and torque components of the motor dynamics. The initial estimation of coefficients of the proposed ANN speed controller is obtained by offline training method. Online training has been carried out to update the ANN under continuous mode of operation. Dynamic backpropagation with the Levenburg-Marquardt algorithm is utilized for online training purposes. The controller is implemented in real time using a digital-signal-processor-based hardware environment to prove the feasibility of the proposed method. The simulation and experimental results reveal that the control architecture adapts and generalizes its learning to a wide range of operating conditions and provides promising results under parameter variations and load changes.

Robust Tracking Control of a Piezoactuator Using a New Approximate Hysteresis Model

Abstract: A new approximate model, which consists of a variable gain and a variable time-delay, is proposed to describe the hysteresis behavior of a piezoactuator. The variable gain is assumed to be a function of the magnitude of the input command, while the time-delay is assumed to be a function of the frequency of the input command. The ranges of these two variable parameters are determined through open loop tests. According to the proposed approximate model, a Smith predictor-based robust H x controller is developed to achieve high-precision tracking control of a piezoactuator. Analytical simulation and experimental results on tracking several types of reference inputs demonstrate that the maximum trucking error can be reduced to be less than 2% of the traveling path by utilising the proposed controller design.

Method and system for power management including device controller-based device use evaluation and power-state control

Abstract: A method and system for power management including device controller-based device use evaluation and power-state control provides improved performance in a power-managed processing system. Per-device usage information is measured and evaluated during process execution and is retrieved from the device controller upon a context switch, so that upon reactivation of the process, the previous usage evaluation state can be restored. The device controller can then provide for per-process control of attached device power management states without intervention by the processor and without losing the historical evaluation state when a process is switched out. The device controller can control power-saving states of connected devices in conformity with the usage evaluation without processor intervention and across multiple process execution slices. The device controller may be a memory controller and the controlled devices memory modules or banks within modules if individual banks can be power-managed. Local thresholds provide the decision-making mechanism for each controlled device. The thresholds may be history-based, fixed or adaptive and are generally set initially by the operating system and may be updated by the memory controller adaptively or using historical collected usage evaluation counts or alternatively by the operating system via a system processor.

Feedback controller design for a spatially distributed system: the paper machine problem

Abstract: This paper reports on the development and implementation of an algorithm for the design of spatially distributed feedback controllers for the wide variety of physical processes that are included in cross-directional (CD) control of industrial paper machines. The spatial and temporal structure of this class of process models is exploited in the use of the 2D frequency domain for analysis and 2D loop shaping design of feedback controllers. This algorithm forms the basis of a software tool that has recently been implemented in a commercial product and its use is illustrated for tuning CD controllers on two different industrial paper machines. The first example describes the use of the tool in stabilizing an unstable closed-loop system by retuning the distributed controller. The second paper machine example exposes an underperforming controller. Subsequent retuning of the controller resulted in a dramatic performance improvement.

Fuzzy gain scheduling: controller and observer design based on Lyapunov method and convex optimization

Abstract: Addresses model-based fuzzy control. A constructive and automated method for the design of a gain-scheduling controller is presented. Based on a given Takagi-Sugeno fuzzy model of the plant, the controller is designed such that stability and prescribed performance of the closed loop are guaranteed. These properties are valid in a wide working range around an equilibrium without restrictions to slowly varying trajectories. The synthesis is based on linear matrix inequalities and convex optimization techniques. If required, a fuzzy state estimator and an extended controller can be included, providing a zero steady-state error in the presence of disturbances and modeling errors. The proposed method has been applied to a control of a laboratory liquid-level process. Hence, the performance has been evaluated in simulations as well as in real-time control.

Template based control of hexapedal running

Abstract: In this paper, we introduce a hexapedal locomotion controller that simulation evidence suggests will be capable of driving our RHex robot at speeds exceeding five body lengths per second with reliable stability and rapid maneuverability. We use a low dimensional passively compliant biped as a "template" - a control target for the alternating tripod gait of the physical machine. We impose upon the physical machine an approximate inverse dynamics within-stride controller designed to force the true high dimensional system dynamics down onto the lower dimensional subspace corresponding to the template. Numerical simulations suggest the presence of asymptotically stable running gaits with large basins of attraction. Moreover, this controller improves substantially the maneuverability and dynamic range of RHex's running behaviors relative to the initial prototype open-loop algorithms.

Design and implementation of industrial neural network controller using backstepping

Abstract: In this paper, a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed at The University of Texas at Arlington. The implementation results show that the NN backstepping controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN controller gives better results on actual systems than a standard backstepping controller developed assuming full knowledge of the dynamics. Moreover, the NN controller does not require the linear-in-the-parameters assumption or the computation of regression matrices required by standard backstepping.

Mixed-sensitivity approach to H/sub /spl infin// control of power system oscillations employing multiple FACTS devices

Abstract: This paper demonstrates the enhancement of inter-area mode damping by multiple flexible AC transmission systems (FACTS) devices. Power system damping control design is formulated as an output disturbance rejection problem. A decentralized H/sub /spl infin//, damping control design based on the mixed-sensitivity formulation in the linear matrix inequality (LMI) framework is carried out. A systematic procedure for selecting the weights for shaping the open loop plant for control design is suggested. A 16-machine, 5-area study system reinforced with a controllable series capacitor (CSC), a static VAr compensator (SVC) and a controllable phase shifter (CPS) at different locations is considered. The controllers designed for these devices are found to effectively damp out inter-area oscillations. The damping performance of the controllers is examined in the frequency and time domains for various operating scenarios. The controllers are found to be robust in the face of varying power-flow patterns, nature of loads, tie-line strengths and system nonlinearities, including device saturations.

Dissipativity-based adaptive and robust control of UPS in unbalanced operation

Abstract: In this paper, we investigate the output voltage control for three phase uninterruptible power supply (UPS) using controllers based on ideas of dissipativity. To provide balanced sinusoidal output voltages even in the presence of nonlinear and unbalanced loads, we first derive a dissipativity-based controller using a conventional /spl alpha//spl beta/ (fixed frame) representation of system dynamics and a frequency-domain representation of system disturbances. Adaptive refinements have been added to the controller to cope with parametric uncertainties. Second, based on the structure of the first adaptive controller, we propose another controller that leads to a linear time-invariant (LTI) closed loop system which is directly connected to synchronous frame harmonic voltage control. This controller, denoted as robust, avoids the most computationally demanding parameter estimation during adaptation, and offers important advantages for implementation. For the proposed robust controller, a sufficient condition in terms of the design parameters is presented to guarantee stability of the desired equilibrium and robustness against certain parametric uncertainties. Finally, simulation and experimental results on a three-phase prototype show effectiveness and advantages of the proposed class of controllers.

Auto-disturbances-rejection Controller and it′s Application in Fast Following Synchronizer of Generators

Abstract: Introduces the auto disturbance rejection controller and its application on a Fast Following Synchronizer of Generator The numerical simulation shows that the controller ensures very good robustness under modeling uncertainty,and it is concluded that the proposed controller produces better dynamic performance such as small overshoot than the classic PID controller

Observer-based output feedback controller design of piecewise discrete-time linear systems

Abstract: This brief presents an output feedback controller design method for piecewise discrete-time linear systems based on a piecewise Lyapunov function and a state observer. It is shown that the resulting closed-loop system is globally stable and the controller can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software. In other words, it is shown that the separation principle of the controller and observer design is still maintained when the piecewise Lyapunov function is used. A simulation example is also given to illustrate the advantage of the proposed approach.

A controller architecture for high bandwidth active power filters

Abstract: This paper presents a novel architecture for a unit-delay digital deadbeat current controller for a shunt active power filter (APF). The APF is based on a fixed frequency pulsewidth modulated voltage-sourced converter (VSC). The proposed controller increases the APF current-tracking bandwidth without increasing the VSC switching frequency. Previous APF digital deadbeat controllers have a current-tracking delay of two or more sample-periods. One delay is due to current controller computation, a second sample delay represents VSC actuation time. The paper presents a new controller architecture employing both asynchronous programmable logic and a small microprocessor. Current-tracking feedback control calculations are executed in asynchronous programmable logic to effectively eliminate the controller computation delay. The microprocessor executes fundamental frequency disturbance rejection computations and all other supervisory functions. The proposed architecture retains all high-level functions in the microprocessor to minimize controller development time without compromising APF performance.

Development of an Internal Model Sliding Mode Controller

Abstract: This paper shows the synthesis of a robust predictive controller from a process model that represents a good approximation for nonlinear chemical processes. The controller is designed using the internal model and sliding mode control concepts. This approach results in a fixed controller structure that depends on the characteristic parameters of the model. The controller performance is compared with internal model control and sliding mode control for different linear examples. All linear models present a controllability relationship (t0/U) of greater than 1. Simulation results indicate that the proposed controller can work for processes with elevated deadtime, despite modeling errors.

Dynamic modeling and input shaping of thermal bimorph MEMS actuators

Abstract: Thermal bimorphs are a popular actuation technology in MEMS (micro-electro-mechanical systems). Their operating principle is based on differential thermal expansion induced by Joule heating. Thermal bimorphs, and other thermal flexure actuators have been used in many applications, from micro-grippers, to micro-optical mirrors. In most cases open-loop control is used to difficulties in fabricating positioning sensors together with actuator. In this paper we present several methods for extracting reduced-order thermal flexure actuator models based on experimental data, physical principles, and FEA simulation. We then use the models to generate optimal driving signals using input shaping techniques. Both simulation and experimental results are included to illustrate the efficacy of our approach. This framework can also be applied to other types of MEMS actuators, including electrostatic comb drives.

Modeling and control of the magnetic suspension system.

Abstract: A fuzzy logic based controller applied to a simple magnetic suspension is presented in this paper. The simple electromagnet-ball system and the contactless optical position measurement system are developed as a physical model of the magnetic suspension. A nonlinear mathematical model is presented and linearized. This model has been used to design a discrete linear PID controller with optimal parameters. The physical real-time model was constructed in order to compare the performance of the linear discrete PID controller and the proposed fuzzy logic based PID controller. The decomposed fuzzy PID controller has proportional, integral, and derivative separate parts which are tuned independently. When testing it becomes clear that the decomposed fuzzy PID controller gives better performance over a typical operational range than a traditional linear PID controller.

A robust adaptive fuzzy position/force control scheme for cooperative manipulators

Abstract: We examine the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive fuzzy controller scheme is proposed here. The controller makes use of a multi-input-multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed algorithm requires neither a precise mathematical model of the system's dynamics nor a linear parameterization of the system's uncertain physical parameters. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The payload position/orientation error and that of the internal forces are also shown to asymptotically converge to zero under such conditions. The performance of the controller proposed is then compared with that of a well-known conventional adaptive controller.

Feedforward, feedback wafer to wafer control method for an etch process

Abstract: A method of using a run-to-run (R2R) controller to provide wafer-to-wafer (W2W) control in a semiconductor processing system is provided. The R2R controller includes a feed-forward (FF) controller, a process model controller, a feedback (FB) controller, and a process controller. The R2R controller uses feed-forward data, modeling data, feedback data, and process data to update a process recipe on a wafer-to-wafer time frame.

A Neural Network Inverse Model for a Shape Memory Alloy Wire Actuator

Abstract: Tracking control of shape memory alloy (SMA) actuators is essential in many applications such as vibration controls. Due to the hysteresis, an inherent nonlinear phenomenon associated with SMAs, open-loop control design has proven inadequate for tracking control of these actuators. Aimed at eliminating the position sensor to reduce cost of an SMA actuator system, in this paper, a neural network open loop controller is proposed for tracking control of an SMA actuator. A test stand, including a titanium-nickel (TiNi, or Nitinol) SMA wire actuator, a position sensor, bias springs, and a programmable current amplifier, is used to generate training data and to verify the neural networks open loop controller. A digital data acquisition and real-time control system was used to record experimental data and to implement the control strategy. Based on the training data obtained from the test stand, two neural networks are used to respectively model the forward and inverse hysteresis relations between the applied voltage and the displacement of the SMA wire actuator. To control the SMA actuator without using a position sensor, the neural network inverse model is used as a feedforward controller. The experimental results demonstrate the effectiveness of the neural network open loop controller for tracking control of the SMA wire actuator.

Digital controller chip set for isolated DC power supplies

Abstract: This paper describes a digital controller chip set for high-frequency DC-DC power converters with galvanic isolation. The secondary-side controller includes an A/D converter and a transmitter that sends a digital error signal as serial data through an opto-coupler. The primary-side controller includes a serial-data receiver, a programmable digital PID regulator, and a high-resolution (10-bit) digital pulse-width modulator. The digital error signal transmission through the isolation boundary eliminates the problem of gain variation when the opto-coupler is used in linear mode. The chip set is tested as a replacement for a conventional analog current-mode controller in a 3.3 V, 20 A, 400 KHz DC power supply. Experimental results with the digital controller show improved dynamic responses compared to the responses obtained with the analog controller.