Suresh K. Kannan

Adaptive trajectory control for autonomous helicopters

For autonomous helicopter flight, it is common to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work, dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation process from actuator limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus, minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation requirements, allowing the outer-loop bandwidth to be closer to that of the inner loop, thus, increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient for accurate position tracking. The theory and implementation of such an approach, with a summary of flight-test results, are described.

Adaptive Flight Control for an Autonomous Unmanned Helicopter

For autonomous helicopter flight, it is common to separate the flight control problem into an innerloop that controls attitude and an outerloop that controls the trajectory of the helicopter. The outerloop generates attitude commands that orient the main rotor forces appropriately to generate required translational accelerations. Recent work in Neural Network based adaptive flight control may be applied to control a helicopter where the reference commands include position, velocity, attitude and angular rate. The outerloop is used to correct the commanded attitude in order to follow position and velocity commands. This however generally requires a model of the translational dynamics which has some model error. This paper introduces adaptation in the outerloop using Pseudo Control Hedging in a way that prevents adaptation to the innerloop dynamics. Additionally, hedging is used in the innerloop to avoid incorrect adaptation while at control limits. Such an approach along with correct placement of the combined poles of the linearized system mitigates inner/outer loop interaction problems and allows one to increase bandwidth in the outerloop, thus, improving tracking performance further.

A Compact Guidance, Navigation, and Control System for Unmanned Aerial Vehicles

The Flight Control System 20 (FCS20) is a compact, self-contained Guidance, Navigation, and Control system that has recently been developed to enable advanced autonomous behavior in a wide range of Unmanned Aerial Vehicles (UAVs). The FCS20 uses a floating point Digital Signal Processor (DSP) for high level serial processing, a Field Programmable Gate Array (FPGA) for low level parallel processing, and GPS and Micro Electro Mechanical Systems (MEMS) sensors. In addition to guidance, navigation, and control functions, the FCS20 is capable of supporting advanced algorithms such as automated reasoning, artificial vision, and multi-vehicle interaction. The unique contribution of this paper is that it gives a complete overview of the FCS20 GN&C system, including computing, communications, and information aspects. Computing aspects of the FCS20 include details about the design process, hardware components, and board configurations, and specifications. Communications aspects of the FCS20 include descriptions of internal and external dataflow.The information section describes the FCS20 Operating System (OS), the Support Vehicle Interface Library (SVIL) software, the navigation Extended Kalman Filter, and the neural network based adaptive controller. Finally, simulation-based results as well as actual flight test results that demonstrate the operation of the guidance, navigation, and control algorithms on a real Unmanned Aerial Vehicle (UAV) are presented.

Nested saturation with guaranteed real poles

The global stabilization of asymptotically null-controllable linear systems with bounded controls has been studied extensively. An early contribution was by Teel who proposed a set of nested saturators to globally asymptotically stabilize the special case of n-integrators with one input. Using this law however, the closed loop system pole locations depend on the choice of coordinate transformation used to arrive at the control law. In this paper we suggest an approach that allows the designer to pick transformations that facilitate the placement of the closed loop poles on the negative real axis.

An open control platform for reconfigurable, distributed, hierarchical control systems

Complex control systems for autonomous vehicles require integrating new control algorithms with a variety of different component technologies and resources. These components are often supported on different types of hardware platforms and operating systems and often must interact in a distributed environment (e.g., in communication with a groundstation, mothership, or other UAVs in a swarm). At the same time, the configuration and integration of components must be flexible enough to allow rapid online reconfiguration and adaptation to react to environmental changes and respond to unpredictable events during flight, such as avoiding a moving obstacle or recovering from vehicle equipment failures. This paper describes an open software architecture, called the open control platform, for integrating control technologies and resources. The specific driving application is supporting autonomous control of VTOL uninhabited autonomous vehicles.

Adaptive control of systems in cascade with saturation

Some systems may be approximated as block cascades structures where each block of the cascade can be approximately feedback linearized. In such systems, the states of the lower subsystem blocks affect the dynamics of the upper subsystems. In generating an inverse for a given block, a subset of its lower subsystem states can be treated as virtual actuators in addition to actual direct actuation that may be available. The desired virtual actuator signal arising from the upper subsystems inverse now appears as a command to the lower subsystem. This paper introduces an adaptive element that is capable of canceling modeling errors arising due to feedback linearization. It also introduces reference models that include a pseudocontrol hedging signal which protects the adaptive element from lower subsystems dynamics.

Flight Results of Autonomous Fixed-Wing UAV Transitions to and from Stationary Hover

Fixed-wing unmanned aerial vehicles (UAVs) with the ability to hover have significant potential for applications in urban or other constrained environments where the combination of fast speed, endurance, and stable hovering flight can provide strategic advantages. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller capable of transitioning a fixed-wing UAV to and from hovering flight in a nearly stationary position. This approach allows utilization of the entire low speed flight envelope even beyond stall conditions. The method is applied to the GTEdge, an 8.75 foot wing span fixed-wing aerobatic UAV which has been fully instrumented for autonomous flight. Results from actual flight test experiments of the system where the airplane transitions from high speed steady flight into a stationary hover and then back are presented.

Flight-Test Results of Autonomous Airplane Transitions Between Steady-Level and Hovering Flight

Linear systems can be used to adequately model and control an aircraft in either ideal steady-level flight or in ideal hovering flight However, constructing a single unified system capable of adequately modeling or controlling an airplane in steady-level flight and in hovering flight, as well as during the highly nonlinear transitions between the two, requires the use of more complex systems, such as scheduled-linear, nonlinear, or stable adaptive systems. This paper discusses the use of dynamic inversion with real-time neural network adaptation as a means to provide a single adaptive controller capable of controlling a fixed-wing unmanned aircraft system in all three flight phases: steady-level flight, hovering flight, and the transitions between them. Having a single controller that can achieve and transition between steady-level and hovering flight allows utilization of the entire low-speed flight envelope, even beyond stall conditions. This method is applied to the GTEdge, an eight-foot wingspan, fixed-wing unmanned aircraft system that has been fully instrumented for autonomous flight. This paper presents data from actual flight-test experiments in which the airplane transitions from high-speed, steady-level flight into a hovering condition and then back again.

Small Adaptive Flight Control Systems for UAVs using FPGA/DSP Technology

Future small UAVs will require enhanced capabilities like seeing and avoiding obstacles, tolerating unpredicted flight conditions, interfacing with payload sensors, tracking moving targets, and cooperating with other manned and unmanned systems. Cross-platform commonality to simplify system integration and training of personnel is also desired. A small guidance, navigation, and control system has been developed and tested. It employs Field Programmable Gate Array (FPGA) and Digital Signal Processor (DSP) technology to satisfy the requirements for more advanced vehicle behavior in a small package. Having these two processors in the system enables custom vehicle interfacing and fast sequential processing of high-level control algorithms. This paper focuses first on the design aspects of the hardware and the low-level software. Discussion of flight test experience with the system controlling both an unmanned helicopter and an 11-inch ducted fan follow.

An open software infrastructure for reconfigurable control systems

Recent advances in software technology have the potential to revolutionize control system design. This paper describes a new software infrastructure for complex control systems, which exploits new and emerging software technologies. It presents an open control platform (OCP) for complex systems, including those that must be reconfigured or customized in real-time for extreme-performance applications. An application of the OCP to the control system design of an autonomous aerial vehicle is described.