Showing papers on "Open-loop controller published in 2000"

Adaptive tracking control of a nonholonomic mobile robot

Abstract: A mobile robot is one of the well-known nonholonomic systems. The integration of a kinematic controller and a torque controller for the dynamic model of a nonholonomic mobile robot has been presented (Fierro and Lewis, 1995). In this paper, an adaptive extension of the controller is proposed. If an adaptive tracking controller for the kinematic model with unknown parameters exists, an adaptive tracking controller for the dynamic model with unknown parameters can be designed by using an adaptive backstepping approach. A design example for a mobile robot with two actuated wheels is provided. In this design, a new kinematic adaptive controller is proposed, then a torque adaptive controller is derived by using the kinematic controller.

Power system damping controller design-using multiple input signals

Abstract: We have investigated the use of multiple input signals for the design of power system stabilizers and thyristor-controlled series-compensation controllers. These examples show the benefits of combining input signals to move the system zeros to more desirable locations, allowing the controller to be more effective in providing additional damping to the interarea modes. The multiple-input-signal controller can be used as the primary control system or as a backup system when some of the controllers are temporarily unavailable. In some cases, a multiple-input-signal controller structure may be more cost effective than installing a new control device. The choices of the input signals used for the controller designs in the example systems in the article have been based on good knowledge of the systems.

Stable neural controller design for unknown nonlinear systems using backstepping

Abstract: We propose, from an adaptive control perspective, a neural controller for a class of unknown, minimum phase, feedback linearizable nonlinear system with known relative degree. The control scheme is based on the backstepping design technique in conjunction with a linearly parametrized neural-network structure. The resulting controller, however, moves the complex mechanics involved in a typical backstepping design from off-line to online. With appropriate choice of the network size and neural basis functions, the same controller can be trained online to control different nonlinear plants with the same relative degree, with semi-global stability as shown by the simple Lyapunov analysis. Meanwhile, the controller also preserves some of the performance properties of the standard backstepping controllers. Simulation results are shown to demonstrate these properties and to compare the neural controller with a standard backstepping controller.

Frequency control of hysteretic power converter by adjusting hystersis levels

Abstract: A power converter is comprised of a hysteretic controller including a feedback circuit that monitors the output frequency of the controller, compares it to a reference generated either internally or externally by the user, and then adjusts the hysteresis of the controller accordingly. The adjusted hysteresis levels will then cause the switching frequency to either increase or decrease thereby controlling the switching frequency of the power supply controller and maintaining it at a desired level.

Pressure controller and method

Abstract: A closed loop pressure controller system that sets, measures and controls the process pressure within a semiconductor process is shown. The system is commonly composed of a pressure sensor to collect the pressure information, a controller box that hosts the control electronics, and a valve to physically affect the conductivity of the inlet or outlet gas line and accordingly the process pressure. The present invention differs from the prior art by using closed-loop motor control of the valve, rather than the method of the prior art, where the valve position is controlled by a stepper motor actuator driven in an open loop fashion. It is demonstrated that the utility of such prior art open-loop configurations is limited by the fact that the achievable precision of the valve position is hindered by static friction in the valve system, and the non-linear character of the torque versus shaft-angle of the motor (among other error components). The method of the present invention more accurately positions the valve, and accordingly enhances the overall precision and allowable loop-gain of the pressure control system by providing the valve drive with feedback as to the actual angular position of the valve in extremely high resolution.

3D controller with vibration

Abstract: A hand operated controller or converter structured for allowing hand inputs to be converted or translated into electrical outputs, the controller structured with a plate or platform moveable relative to a base or housing about two mutually perpendicular axes generally parallel to the platform to effect a plurality of sensors for defining output signal(s) based on movement of the platform The sensors each have an electrically active activator spatially separated from an electric contact surface A tactile feedback motor with shaft and offset weight is mounted as a component of the controller for providing vibration to be felt by a hand operating the controller In some embodiments the sensors are pressure sensitive variable output sensors

A radial basis function neural network controller for UPFC

Abstract: This paper presents the design of radial basis function neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either a single neuron or multi-neuron architecture and the parameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC injection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-machine power systems subjected to various transient disturbances. In the case of three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with a BP (backpropagation) algorithm based multi-layered ANN controller. Further it is seen that by using a multi-input multi-neuron RBF controller, instead of a single neuron one, the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation.

Nonlinear output feedback control of underwater vehicle propellers using feedback form estimated axial flow velocity

Abstract: Accurate propeller shaft speed controllers can be designed by using nonlinear control theory and feedback from the axial water velocity in the propeller disc. In this paper, an output feedback controller is derived, reconstructing the axial flow velocity from vehicle speed measurements, using a three-state model of propeller shaft speed, forward (surge) speed of the vehicle, and the axial flow velocity. Lyapunov stability theory is used to prove that a nonlinear observer combined with an output feedback integral controller provide exponential stability. The output feedback controller compensates for variations in thrust due to time variations in advance speed. This is a major problem when applying conventional vehicle-propeller control systems. The proposed controller is simulated for an underwater vehicle equipped with a single propeller. The simulations demonstrate that the axial water velocity can be estimated with good accuracy. In addition, the output feedback integral controller shows superior performance and robustness compared to a conventional shaft speed controller.

Adaptive fuzzy control for wind-diesel weak power systems

Abstract: This work is concerned with the development of an adaptive fuzzy logic controller for a wind-diesel system composed of a stall regulated wind turbine with an induction generator connected to an AC busbar in parallel with a diesel generator set having a synchronous generator. In this work we propose to use an adaptive network based inference system (ANFIS) in order to generate fuzzy membership functions and control rules for the controller. A feedback linearized proportional integral controller is used to provide the required expert knowledge. A controller design process is identified; it consists of generating input-output data pairs to identify the control variables range and initial fuzzy memberships, and then to tune or adapt them using an ANFIS network structure. The controller inputs are the frequency error and its integral for the governor part of the controller, and the voltage and frequency errors for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented.

Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives

Abstract: A brushless DC motor drive with a proposed adaptive fuzzy-neural-network controller is introduced in this paper. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. Then, the fuzzy rules and input/output of the system are tuned by the supervised gradient descent learning algorithm. Using an experimental setup, the performance of the proposed controller is evaluated under various operating conditions. Test results are presented and discussed in the paper. The presented controller is shown to be robust, adaptive and capable of learning. To demonstrate the effectiveness of the controller, a proportional-integral controller has been used to perform comparative studies with encouraging results.

State feedback controller for flexible joint robots: a globally stable approach implemented on DLR's light-weight robots

Abstract: Addresses the problem of controlling the joints of a flexible joint robot with a state feedback controller and proposes a gradual way of extending such a controller towards feedback linearization. The global asymptotic stability for the state feedback controller with gravity compensation is proven. Experimental results on the DLR light-weight robots validate the method.

Centralized control architecture for a plasma arc system

Abstract: The invention features a centralized control architecture for a closely-coupled plasma arc system, in which the “intelligence” of the system is integrated into a single controller. The closely-coupled plasma arc system includes a power source, an automatic process controller and a torch-height controller, where each of these components individually has a closed-loop dynamic relationship with the controller.

Minimum-time system-inversion-based motion planning for residual vibration reduction

Abstract: In this paper, we present a novel approach, based on system inversion, for the point-to-point motion planning of vibratory servosystems. The idea is to define a suitable parameterized motion law of the load which assures that no oscillations occurs during and at the end of the motion; then, by means of a noncausal system inversion, the command function of the system is determined with a continuous derivative of an arbitrary order. A procedure that minimizes the duration of the movement, taking into account actuator constraints, can then be performed. Comparisons with the well-known input shaping techniques have been performed via both a simulation example and an experimental setup. The proposed method, which is inherently robust to modeling errors, emerges as a very flexible and competitive technique.

Damping multimodal power system oscillation using a hybrid fuzzy controller for series connected FACTS devices

Abstract: The paper presents a simple hybrid fuzzy logic proportional plus conventional integral controller for FACTS devices in a multi-machine power system. This controller is designed by using an incremental fuzzy logic controller in place of a proportional term in a conventional PI controller and provides a wide variation of controller gains in a nonlinear manner. This controller is well suited to series connected FACTS devices like UPFC, TCSC and TCPST, etc., in damping multi-modal oscillations in a multi-machine environment. Digital simulations of a multi-machine power system subjected to a wide variety of disturbances validate the efficiency of the new approach.

An adaptive sliding-mode controller for discrete nonlinear systems

Abstract: This paper presents a sliding-mode controller for a class of nonlinear discrete-time systems. The proposed controller uses a modified switching function that produces a low-chattering control signal. In order to improve the controller performance, an adaptive term is added to the original sliding-mode algorithm. This new feature uses an artificial neural network for online identification of the modeling error. Simulations and experimental results illustrate the main characteristics and performance of this approach,.

Fuzzy neural network approaches for robotic gait synthesis

Abstract: In this paper, a learning scheme using a fuzzy controller to generate walking gaits is developed. The learning scheme uses a fuzzy controller combined with a linearized inverse biped model. The controller provides the control signals at each control time instant. The algorithm used to train the controller is "backpropagation through time". The linearized inverse biped model provides the error signals for backpropagation through the controller at control time instants. Given prespecified constraints such as the step length, crossing clearance, and walking speed, the control scheme can generate the gait that satisfies these constraints. Simulation results are reported for a five-link biped robot.

A radial basis function neural network controller for UPFC

Abstract: Summary form only given as follows. This paper presents the design of radial basis function neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either single neuron or multi-neuron architecture and the parameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC injection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-machine power systems subjected to various transient disturbances. In the case of a three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with BP (backpropagation) algorithm based multi-layered ANN controller. Further it is seen that by using a multi-input multi-neuron RBF controller, instead of a single neuron one, the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation.

Method and system for master to master communication in control systems

Abstract: A master controller (28) in a control area network system in a larger control area network (16, 18) may have a plurality of devices (33) coupled thereto. The devices generate data which the master controller directs to a destination device coupled to the same master controller, a different master controller in the same control area network or to a different master controller in a different control area network. The master controller directs the data using a connections table acquired from one or more other master controllers to which the master controller may communicate with.

Distributed energy neural network integration system

Abstract: A system that couples distributed power generators together as a collective unit for the purposes of selling or purchasing energy from the electrical power grid The apparatus includes a charge/discharge controller and an adaptive controller The charge/discharge controller transfers energy generated by the plurality of distributed power generators to the power grid The adaptive controller directs when the charge/discharge controller transfers energy generated by at least one of the plurality of distributed power generators to the electrical grid

Ultra-low power switching regulator method and apparatus

Abstract: A low-power controller for a discontinuous switched mode power converter. The controller has an inductor current sensing circuit to measure the inductor current flowing through an inductive charge storage element as well as an output voltage sensing circuit to monitor output voltage. The controller monitors both the converter output voltage and the inductor current and uses this information to modulate a peak inductor current trip point and controller switching frequency according to a control law curve in order to regulate converter output voltage. The controller prevents the switching frequency from falling below a predetermined minimum frequency. The control law curve is selectable to specify controller operation according to a desired combination of minimum switching frequency and maximum peak inductor current.

Virtual reference feedback tuning (VRFT): a new direct approach to the design of feedback controllers

Abstract: We discuss a new method for the data-based design of feedback controllers in a linear setting. The main features of the method are that it is a direct method (no model identification of the plant is needed) and that it can be applied using a single set of data generated by the plant, needing no specific experiments nor iterations. It is shown that the method searches for the global optimum of the design criterion and that, in the significant case of restricted complexity controller design, the achieved controller is a sensible approximation (under some reasonable hypotheses) of the restricted complexity global optimal controller. As an extra contribution it is also presented a controller validation test aiming at ascertain the closed-loop stability before the designed controller is applied to the plant. A numerical example is given.

Method and device for controlling data flow through an IO controller

Abstract: An IO controller device and method for controlling data flow, the method including determining a desired configuration for the IO controller, reprogramming the IO controller to allow for processing of one or more descriptor lists, modifying the configuration of the IO controller to reflect the addition or deletion of one or more virtual controllers, re-enumerating the IO controller, and processing a descriptor list for each of the IO controller and the one or more virtual controllers. The integrated circuit device for use as an IO controller includes a system bus interface, a programmable list processor and a port router. The integrated circuit device is adapted for reconfiguration to add or delete one or more virtual controllers. The virtual controllers provide substantially the full bandwidth supported by the integrated circuit device. The IO controller device and apparatus may be applied to personal computer systems, information appliances, set-top boxes, cable modems, game consoles, smart appliances, handheld computers, palm-sized computers, embedded control systems, workstations, servers and the like.

Embedded data acquisition and control system for non-invasive glucose prediction instrument

Abstract: An embedded data acquisition and control system for a precision instruments, such as a non-invasive glucose prediction instrument is disclosed. One feature of the invention provides synchronization of stepper motor position and analog-to-digital converters. An embedded controller is provided that controls the stepper motor driver directly to minimize the wavelength shift error due to the asynchronous condition. The controller synchronizes the event of reading the A/D converters with each stepper motor position. Because the stepper motor controls the wavelength of the monochromator optical output, the net result is that each A/D conversion recorded by the embedded controller is precisely tracked to a specific wavelength. Another feature of the invention provides closed loop motor position control for enhanced system performance. In the closed loop system, a position encoder is coupled to the stepper motor shaft. The encoded position signal is processed by a digital encoder at the bipolar stepper motor drive, resulting in an actual position feedback to the embedded controller. Because the embedded controller has the position feedback, it can initiate the next step command as soon as the position feedback reaches its target. The step rate is increased by reducing the time delay that was set by a conservative value as indicated in the open loop control mode. One advantage of this method is to achieve highest speed in all load conditions without missing steps. A further feature of the invention provides optical Isolation to minimize motor noise. Thus, optical isolation is provided at a bipolar stepper motor drive and embedded controller interface. To maximize system sensitivity, optical isolation is implemented between the embedded controller and the stepper motor drive.

Microcomputer implementation of optimal algorithms for closed-loop control of hybrid stepper motor drives

Abstract: This paper discusses optimal algorithms for closed-loop control of hybrid stepper motor drives and their microprocessor implementation. The torque characteristics and the optimal control angle of hybrid stepper motor drives with added series resistance and reluctant stepper motor drives have been described in detail in the literature. The specific contribution of the paper to this field of research consists of the analysis of the torque characteristics and the optimal control angle of hybrid stepper motor drives with a chopper amplifier and current controller. Analytical expressions for the average torque and the optimal control angle of a two-phase hybrid stepper motor with chopper amplifier and current controller have been developed. An actual hybrid stepper motor drive and the microcontroller-based implementation of the suboptimal and exact optimal control algorithms have been described. The experimental results obtained by positioning a two-phase hybrid stepper motor drive with chopper amplifier, phase current controller, and incremental encoder suggested that the derived optimal control algorithm provided maximum acceleration and minimum positioning time of the hybrid stepper motor drive.

A simple output feedback PD controller for nonlinear cranes

Abstract: A simple output feedback PD controller is proposed that stabilizes a nonlinear crane. Global asymptotic stability is achieved at any equilibrium point specified by the controller. The control scheme relies solely on the winches position and velocity and hence no cable angle measurement, or no direct measurement of the load position, is needed. The controller can be extended to many different kinds of existing cranes.