Digital adaptive flight controller development

A design study of adaptive control logic suitable for implementation in modern airborne digital flight computers was conducted. Two designs are described for an example aircraft. Each of these designs uses a weighted least squares procedure to identify parameters defining the dynamics of the aircraft. The two designs differ in the way in which control law parameters are determined. One uses the solution of an optimal linear regulator problem to determine these parameters while the other uses a procedure called single stage optimization. Extensive simulation results and analysis leading to the designs are presented

Process: program for research on operator control in an experimental simulated setting

An experimental tool for the investigation of human control behavior of slowly responding dynamic systems is described. Process (Program for Research on Operator Control in an Experimental Simulated Setting) is a simulation of a dynamic water-alcohol distillation system that is especially useful in research on operator training. In particular, Process was developed to conduct research on fault management skill

Investigations into implementation of an iterative feedback tuning algorithm into microcontroller

Includes abstract.Includes bibliographical references (leaves 73-75).Implementation of an Iterative Feedback Tuning (IFT) and Myopic Unfalsified Control (MUC) Algorithm into microcontroller is investigated in this dissertation. Motivation in carrying out this research emanates from successful results obtained in application of IFT algorithm to various physical systems since the method was originated in 1995 by Hjalmarsson [4]. The Motorola DSP56F807C microcontroller is selected for use in the investigations due to its matching characteristics with the requirements of IFT algorithm. Speed of program execution, large memory, in-built ADC & DAC and C compiler type are the key parameters qualifying for its usage. The Analog Devices ARM7024 microcontroller was chosen as an alternative to the DSP56F807C where it is not available. Myopic Unfalsified Control (MUC) is noted to be similar to IFT since it also employs ‘myopic’ gradient based steepest descent approach to parameter optimization. It is easier to implement in that its algorithm is not as complex as the IFT one, meaning that successful implementation of IFT algorithm in a microcontroller would obviously permit the implementation of MUC into microcontroller as well

Vibration Suppression Controller of Multi-Mass Resonance System Using Fuzzy Controller

Vibration suppression control of the mechanical system is a very important technology for realizing high precision, high speed response and energy saving. In general, the mechanical system is modeled with a multi-mass resonance system, and vibration suppression control is applied. This chapter presents a novel controller design method for the speed control system to suppress the resonance vibration of two-mass resonance system and three-mass resonance system. The target systems are constructed by a motor, finite rigid shafts, and loads. The control system consists of a speed fuzzy controller and a proportional-integral (PI) current controller to realize precise speed and torque response. In order to implement the experimental system, the system is treated as the digital control. This chapter also utilizes a differential evolution (DE) to determine five optimal controller parameters (three scaling factors of the fuzzy controller and two controller gains of PI current controller. Finally, this chapter verified the effectiveness to suppress the resonance vibrations and the robustness of the proposed method by the computer simulations and the experiments by using the test experimental setup

Data-driven fault diagnosis and robust control: Application to PEM fuel cell systems

A data-driven methodology that includes the unfalsified control concept in the framework of fault diagnosis and isolation (FDI) and fault-tolerant control (FTC) is presented. The selection of the appropriate controller from a bank of controllers in a switching supervisory control setting is performed by using an adequate FDI outcome. By combining simultaneous online performance assessment of multiple controllers with the fault diagnosis decision from structured hypothesis tests, a diagnosis statement regarding what controller is most suitable to deal with the current (nominal or faulty) mode of the plant is obtained. Switching strategies that use the diagnosis statement are also proposed. This approach is applied to a nonlinear experimentally validated model of the breathing system of a polymer electrolyte membrane fuel cell. The results show the effectiveness of this FDI–fault-tolerant control data-driven methodologyPeer ReviewedPostprint (author's final draft

Energy Efficient Speed Control of Interior Permanent Magnet Synchronous Motor

In this chapter, methods for the structural realization of a speed control system for the interior permanent magnet synchronous motor (IPMSM) using the “maximum torque per ampere” (MTA) and “maximum torque per volt” (MTV) optimal control strategies are considered. In the system in constant torque region, is a technique for adapting the speed controller to the presence of the reactive motor torque component, which improves the quality of the transient processes, is proposed. It is also recommended to approximate the dependence of the flux-forming current component on the motor torque by the “dead zone” nonlinearity, which will simplify the optimal control algorithm and avoid solving the fourth-degree algebraic equation in real time. For the speed control with field weakening technique, a novel system is recommended. In this system, the control algorithms are switched by the variable of the direct stator current component constraint generated in accordance with the MTA law: the upper limit is calculated in accordance with the “field weakening control” (FWC) strategy, and the lower limit in accordance with the MTV strategy. The steady-state stator voltage constraint is implemented through the variable quadrature stator current component limitation. The effectiveness of the proposed solutions is confirmed by the simulation results

PKM mechatronic clamping adaptive device

This study proposes a novel adaptive fixturing device based on active clamping systems for smart micropositioning of thin-walled precision parts. The modular architecture and the structure flexibility make the system suitable for various industrial applications. The proposed device is realized as a Parallel Kinematic Machine (PKM), opportunely sensorized and controlled, able to perform automatic error-free workpiece clamping procedures, drastically reducing the overall fixturing set-up time. The paper describes the kinematics and dynamics of this mechatronic system. A first campaign of experimental trails has been carried out on the prototype, obtaining promising results

Parameter incremental learning algorithm for neural networks

In this dissertation, a novel training algorithm for neural networks, named Parameter Incremental Learning (PIL), is proposed, developed, analyzed and numerically validated.;The main idea of the PIL algorithm is based on the essence of incremental supervised learning: that the learning algorithm, i.e., the update law of the network parameters, should not only adapt to the newly presented input-output training pattern, but also preserve the prior results. A general PIL algorithm for feedforward neural networks is accordingly derived, using the first-order approximation technique, with appropriate measures of the performance of preservation and adaptation. The PIL algorithms for the Multi-Layer Perceptron (MLP) are subsequently derived by applying the general PIL algorithm, augmented with the introduction of an extra fictitious input to the neuron. The critical point in obtaining an analytical solution of the PIL algorithm for the MLP is to apply the general PIL algorithm at the neuron level instead of the global network level. The PIL algorithm is basically a stochastic learning algorithm, or on-line learning algorithm, since it adapts the neural weights each time a new training pattern is presented. Extensive numerical study for the newly developed PIL algorithm for MLP is conducted, mainly by comparing the new algorithm with the standard (on-line) Back-Propagation (BP) algorithm. The benchmark problems included in the numerical study are function approximation, classification, dynamic system modeling and neural controller. To further evaluate the performance of the proposed PIL algorithm, comparison with another well-known simplified high-order algorithm, i.e., the Stochastic Diagonal Levenberg-Marquardt (SDLM) algorithm, is also conducted.;In all the numerical studies, the new algorithm is shown to be remarkably superior to the standard online BP learning algorithm and the SDLM algorithm in terms of (1) the convergence speed, (2) the chance to get rid of the plateau area, which is a frequently encountered problem in standard BP algorithm, and (3) the chance to find a better solution.;Unlike any other advanced or high-order learning algorithms, the PIL algorithm is computationally as simple as the standard on-line BP algorithm. It is also simple to use since, like the standard BP algorithm, only a single parameter, i.e., the learning rate, needs to be tuned. In fact, the PIL algorithm looks just like a minor modification of the standard on-line BP algorithm, so it can be applied to any situations where the standard on-line BP algorithm is applicable. It can also replace the standard on-line BP algorithm already in use to get better performance, even without re-tuning of the learning rate.;The PIL algorithm is shown to have the potential to replace the standard BP algorithm and is expected to become yet another standard stochastic (or on-line) learning algorithm for MLP due to its distinguished features