Challenges For Control Design Research Articles

Design considerations for a soft-switched modular 2.4-MVA medium-voltage drive

A new six-phase 2.4-MVA soft-switched medium-voltage drive system utilizing series-stacked modules with low-voltage devices has been developed. The drive system combines a new soft-switched DC-DC converter with resonant DC-link inverter technology to deliver extremely low total harmoic distortion sinusoidal output, high power density, and high efficiency. The series-stacked configuration with the associated single-phase loading lead to unique power and control design challenges. Device selection, control of parasitic elements, sensing methods for converter control, custom magnetic component design, and clamping techniques have lead to a substantial improvement in device voltage utilization. The DC-DC converter controls must regulate the intermediate DC-bus voltage under single-phase loading while balancing transformer excitation and maintaining zero-voltage switching, among other tasks. Proper control of the resonant DC-link inverter requires the selection and tuning of the appropriate modulator and understanding its effect on the power circuit ratings.

Neural networks approach to AIAA aircraft control design challenge

This paper focuses on designing a discrete-time lateral-directional control law for a high-performance aircraft using neural networks. The control law structure is composed of feedback and filter components formulated in the form of a three-layer feedforward neural network whose parameters are adjusted by a gradient descent algorithm to provide stabilization about the aircraft center of mass and asymptotic tracking of pilot command input. The number of parameters was chosen in an ad hoc manner. Only rate gyro and lateral accelerometer outputs are available for feedback, whereas rudder pedal and lateral stick commands are input signals to the filter. Linear simulation results at an operating point within the aircraft's envelope in the presence of atmospheric turbulence and actuator and sensor noises shed light on the ability of neural networks to serve as a practical tool for flight control law designers. UMEROUS classical and modern control law design methodologies have been proposed for designing continuous- or discrete-time longitudinal and lateral-directional control laws for aircraft.1'2 These time- and frequency-domain design techniques usually apply to a linear, time-invariant state-space dynamic model of the aircraft from either a single-input, single-output (SISO) or a multi-input, multi-output (MIMO) point of view. However, the aircraft's equations of motion are highly nonlinear in six degrees of freedom (6-DOF) throughout its operating envelope. Thus, those who design control laws for aircraft face some difficulties in extending these techniques to real-flight vehicles. Traditionally, gain scheduling has been employed to compensate for nonlinearity. In this approach, the resulting nonlinear control law for the full-flight envelope is interpolated between various linear controllers scheduled as a function of speed, altitude, and wing loading. However, the desire to enlarge the flight envelope of modern aircraft and to provide enhanced maneuver capabilities may make gain scheduling infeasible due to rapid changes in dynamics. A more sophisticated flight control system that could work well over a wide range of operating conditions is clearly needed. Adaptive control is intended for plant models that must operate in the presence of uncertainty. It utilizes the available a priori knowledge and adapts to the unknown part of the plant such as timevarying parameters. An adaptive flight control system is capable of adjusting on-line the controller's parameters in response to changing flight characteristics.3 However, instability in the adaptive control system in the presence of unmodeled dynamics, bounded input, and

Design with multiple-delay-model and multiple-design-point approach

In this paper, the multiple-delay-model and multiple-design-point (MDM/MDP) approach is applied to the AIAA 1992 Controls Design Challenge. Design goals of the flight control system are presented with standard quadratic performance indices, and linear control laws of constant feedback gains are obtained numerically with parameter optimization. Robustness against uncertainties is guaranteed by giving appropriate MDMs, and robustness against changing dynamics is obtained by selecting MDPs. Trade-offs between control performance and robustness are conducted with MDMs. Simple Pis introduce reasonable feedback control laws. Realistic control laws are derived from the analytical results. Time responses of the two given flight missions and performances at four evaluation points demonstrate feasibility of the simple constant-gain feedback control.

Controls design challenge - A variable structure approach

The purpose of this paper is to investigate variable structure control as a tool for the design of flight control systems. Variable structure control allows the direct incorporation of model uncertainties and nonlinearities in the design process, yielding a piecewise continuous structure for the compensator that maintains the system dynamics along specified stable trajectories (sliding hypersurfaces). Under mild restrictions, the resulting control law is robust with respect to unknown but bounded parameter variations and other uncertainties. A linearized model of an aircraft is used for the design, and a flight control system is synthesized, which gives desired response to initial conditions and tracks a commanded altitude. The control synthesis and simulation are performed using computer-aided software on a personal computer.

Aircraft model for the AIAA controls design challenge

This paper describes a generic, state-of-the-art, high-performan ce aircraft model, including detailed, full-envelope, nonlinear aerodynamics, and full-envelope thrust and first-order engine response data. Although this model was developed primarily for the AIAA Controls Design Challenge, the availability of such a model provides a common focus for research in aeronautical control theory and methodology. Figures showing vehicle geometry, surfaces, and sign conventions are included. Nomenclature A = axial force, Ib a = speed of sound in air, ft/s an = normal acceleration, g am — jt-body axis accelerometer output, accelerometer at center of gravity, g any ~ y-body axis accelerometer output, accelerometer at center of gravity, g anz = z-body axis accelerometer output, accelerometer at center of gravity, g

Intelligent control law tuning for AIAA controls design challenge

Constrained optimization is used as the basis of the intelligent control law tuning applied to the AIAA Controls Design Challenge. A tuning rule is formulated by translating multiple control system design requirements into a cost function and a set of constraints. During the tuning process, constrained optimization is employed to search for control laws that minimize the cost function subject to the constraints. Simulation results are presented to demonstrate the successful applications of the method.

257 New methods for new challenge in spacecraft control design

257 New methods for new challenge in spacecraft control design

Implementation of a full-envelope controller for a high-performance aircraft

The design, implementation, and evaluation of a full-envelope control system for the AIAA controls design challenge aircraft is presented. The control problem is divided into separate longitudinal and lateral/directional designs. The longitudinal controller is a Mach number and flight path angle command system, and bank angle is commanded through the lateral/directional autopilot. A cross-axis filter minimizes transient errors during coupled maneuvers. Proportional plus integral plus derivative structure is built into a linear quadratic synthesis problem to generate state feedback gains. An implementable output feedback solution is found through a linear transformation of the state feedback gain matrix. The control system is gain scheduled with dynamic pressure through polynomial curve fits of linear point design gains. Required maneuvers are demonstrated within design specifications except in cases where the physical limitations of the aircraft restrict achievable performance.