Previous Work with Autonomous Aircraft


Hypersonics

Current hypersonic control and modeling research efforts are being conducted between the Air Force Research lab (AFRL), the University of Michigan and the Massachusetts Institute of Technology (MIT) under the Michigan/AFRL Collaborative Center in Control Science (MACCCS, or MAX) program. MIT will focus on exploring technologies for flight control of highly uncertain air breathing hypersonic vehicles performing complex maneuvers using a 6-DOF simulation.

MAX at MIT

ghv Control of Hypersonic vehicles is an extremely challenging task due to the largely varying operating conditions taking place during flight envelope. Due to the significant changes that occur in the aerodynamics, propulsion, and environmental conditions, and due to the fact that both physics-based models and data-driven models are either inaccurate or too complex, any feedback controller that is introduced in the vehicle has to be suitably advanced. The sophistication in this controller has to be two-fold: first, it has to accommodate the large changes that occur in the vehicle dynamics by incorporating self-adaptive components that have the ability to make on-line changes based on the newly available system measurements. Second, it should possess the ability to ascertain the dynamics that exists at a given operating point and provide a robust compensatory action that ensures safe realization of desired Figures of Merit.

Current hypersonic control and modeling research efforts are being conducted between the Air Force Research lab (AFRL), the University of Michigan and the Massachusetts Institute of Technology (MIT) under the Michigan/AFRL Collaborative Center in Control Science (MACCCS, or MAX) program. MIT will focus on exploring technologies for flight control of highly uncertain air breathing hypersonic vehicles performing complex maneuvers using a 6-DOF simulation.

MIMO Output Feedback Adaptive Control for Hypersonic Vehicles

A state feedback LQR baseline controller with integral action and augmented with an adaptive component has proven to be an effective choice for accommodating the parametric uncertainties present in flight control applications, and ensuring satisfactory reference tracking. However, such a controller requires that the state is measurable, which is not always possible. Also, inaccuracies in the system output measurements may render state feedback controllers sensitive and thus not applicable. For these reasons there has been an increasing drive to develop an adaptive output feedback extension of the robust integral-augmented LQR baseline plus adaptive controller.

Existing classical methods of multi-input multi-output adaptive control are applicable for plants that are square. An m x m transfer matrix is used to represent the dynamic behavior of the plant, and the existence of a stable adaptive solution depends on the available prior information about this plant transfer matrix. The solution relies on non-minimal controller representations to dynamically decouple the plant, and the controller structure consists of a feedforward gain and two filters in the feedback path, the order of which depends on m and an upper bound on the observability index of the plant, v. The resulting classical MIMO adaptive solution will introduce 2mv controller states and 2m^2v adjustable parameters.

More recent methods of MIMO output feedback adaptive control have adopted a Luenberger observer-based approach in which a minimal observer is used to generate a state estimate to use for feedback control. This observer also serves as the reference model which is used by the adaptive controller, and the presence of the observer feedback gain L provides the structure known as the closed-loop reference model, or CRM. These CRM based approaches have relied on the so-called squaring-up procedure to add fictitious inputs to a tall system, making it square and ensuring any transmission zeros are stable. These fictitious inputs are used only to synthesize a postcompensator S1 and the CRM gain L which ultimately render a set of underlying error dynamics strictly positive real (SPR). These SPR error dynamics allowed stable update laws to be chosen to guarantee system stability. We note that systems with transmission zeros cannot be squared up using the existing method, which has led to a recent modification to overcome this limitation and allow the design of output feedback controllers for systems with stable transmission zeros.

Our recent work on output feedback adaptive control has taken alternative approach to synthesizing S1 and L which does not require the system first be squared-up. Instead, the postcompensator S1 is determined as a generalized inverse of the system matrices, and a state feedback approach is used to stabilize a related lower order plant subsystem resulting in a feasible LMI which is solved to yield L. We consider in this work the case of tall systems, but the case of wide systems holds by duality. Furthermore, because L is a component of both the baseline and adaptive controllers, it is crucial that it be selected to provide good frequency domain properties for the baseline control system, as well as desirable adaptive control performance. This procedure is able to exploit the structure of the given system to obtain a large amount of freedom in the selection of L in order to achieve a robust baseline control design and the desired adaptive performance.


Very Flexible Aircraft

Vulture

vulture High-altitude long-endurance flight (HALE) challenge is first proposed in 1950s for the purpose of military surveil- lance. Special aircraft such as SR-71 in 1970s and Global Hawk in more recent date were developed to meet the chal- lenge1. Those aircraft run on conventional fossil oil and cannot achieve real long-endurance flight. In the last decade, HALE challenge evolves into a scientific research program, and platforms with renewable power source, such as solar energy, was investigated. Possible platform features a huge wingspan to host a large amount of solar panels. Daedalus and Helios are two successful attempts in this category and both were able to climb to a record-breaking altitude with stretched operation hours2,3. For this kind of aircraft, power limited energy source only allows small propellers as thrust, which in turn results into low cruise speed (less than 50mph). As a result, payload is limited. Very light material, such as epoxy and carbon fiber, has to be used on wings. Long slender wing with low-yield material results into a flexible structure. In fact, the flexible effect on the wing is so significant that the wing is visibly deforming when load varies. Figure 1a shows that the wing of Helios deforms into a “V” shape when the aircraft is climbing.

Helios is an example of aerial platforms denoted as Very Flexible Aircraft (VFA) which corresponds to those whose trim critically depends on flexible modes4,5. One of the challenges that VFA introduces is a significant change in the rigid-body dynamics as the wing morphs. For example, the pitch (short period) mode of VFA can become unstable when wing dihedral is trimmed at a high value4. As a consequence, control designs based on rigid-body dynamics only may face unexpected adversities. An example of this adversity occurred in 2003 during the second test flight of Helios when a sequence of events took place: the wind turbulence drove the wing to a high dihedral position; flexible effects of wing fought against control and sustains the high dihedral for a long period of time; unstable pitch mode eventually grew unbounded, which causes airspeed exceeding design limit and structure disintegration. The lesson that was learned from the mishap is that the model for control design has to include body flexible effects4.

Recently, a VFA platform, denoted as Vulture, has been under development to meet the goals of HALE maneu- vers6. Vulture is an experimental aircraft with a huge wingspan of 400ft. The wing body consists of four slender wing segments and four booms. A high-fidelity 707 state model has been derived at a single trim point, with a large amount of body flexible modes6. The goal of this paper is to develop an adaptive controller using this model, accommodate uncertainties that typically occur in a VFA, and carry out desired maneuvers despite the large flexible modes present.

Helios

Analysis on the VFA model show that there are two dominant types of uncertainties that can affect the flight dynamics significantly. The first type of uncertainty is the additive control effectiveness change caused by morphing wing (trim drift). The second type of uncertainty is the multiplicative control effectiveness change caused by actuator anomalies. Both effects can be modeled as parametric uncertainties in the underlying plant model.

Adaptive control outline

Figure: The challenge of VFA control design, a state-inaccessible plant with a large amount of uncertainties (as represented by Λ and Ψ*)

Presence of large parametric plant uncertainties motivates an adaptive control solution.

Adaptive control outline

Figure: The classical LQG Controller is not able to accomodate unknown uncertainties (Λ and Ψ*) that are commonly seen in a VFA model

The theory developed in my recent research provides a decent adaptive control solution to a general multi-input-multi-output system, and therefore can be applied on VFA.



Theoretical Development of Adaptive Output-Feedback Control

Adaptive controller was renowned to handle parametric plant uncertainties. The idea of adaptive control is to compare system's real performance with an ideal reference performance and adjust the control action until the two coincide. It detects any performance-compromising faults right from the beginning of the action and reacts in a way such that the effect of faults are guaranteed to be compensated. However, current adaptive controller requires all plant states (x) are measurable, which is not the case for many plant models, especially VFA (only y available). These wing modes cannot be ignored either because of their significant effects on flight dynamics (as in Helios). Current adaptive control theory needs to be extended to be applicable on a state-inaccessible plant model. To this end, we propose a new model-reference-adaptive-control solution designated as, adaptive output-feedback control.

Adaptive control state-feedback

Figure: A new adaptive output-feedback controller is developed by introducing an additional parameter adaptation module on top of a LQG controller; the adaptive design can provably accomodate model uncertainties

The goal is to develop an adaptive controller for a general MIMO plant model. Innovatively, we proposed an observer-based method that generates state estimate (as a surrogate of states) for parameter adaptation, and achieves esitmate convergence and parameter adaptation simultaneously. In this way, we are able to overcome the inaccessibility of state measurements from the plant.

Unlike previous attempts to the problem, we provided a systematic way to design the controller in a step-by-step fashion, and also gave a rigorous proof for its stability and tracking performance. The overall method is simple, straightforward, instructive and self-contained.

To implement our method, one can keep the LQG controller structure and just add the adaptive component on top of it to perform adaptation. The major change is that the feedback gain L of the observer has to be redesigned as Lρ to satisfy an underlying strict-positive-real condition, for which we provided a closed-form analytical solution. By our design, the Lρ also has the robust property that a classical L has in a LQG controller (known as loop-transfer-recovery). As a result, our controller integrats two excellent controllers together and combine their benefits.

The resulting adaptive controller has two modes: When the adaptive module is off, the controller is a conventional linear controller (LQG-like with LTR properties); when the adatpive module is on, the controller becomes adaptive and robust to model uncertainties. The controller can switch between these two modes in real time seamlessly.


Flight Control Applications

Climb Control of 3-Wing VFA

2015 AIAA JGCD pre-print

The most important feature of a VFA is that its wing structure is so flexible that it cannot be considered as a rigid body. The most simplified illustration of the flexible wing is that three rigid wing sections with elastic pivot connections adjoining them. This wing model introduce an additional degree of freedom: the outer wings can rotate with respect to the center wing about longitudinal axis. The angle of such rotation is denoted as dihedral angle, and its dynamics is denoted as dihedral dynamics. The longitudinal and vertical acceleration dynamics of the 3-wing VFA is coupled with the dihedral dynamics.

3-W_VFA_Model

Figure: 3-Wing VFA model represents the essense of VFA dynamics, and can be viewed as building blocks of large VFA

The model represents a typical MIMO plant with limited measurements and actuation. It captures the essence of VFA dynamics and can be viewed as building blocks of large VFA model. The goal is to make the VFA to climb and dive (BTT), i.e. following a vertical acceleration command, while keeping the VFA oriented. The simulation results validate the performance of the adaptive system when the vehicle was subject to a large magnitude of actuator anomaly and plant uncertainties (6 deg dihderal change and 90% actuator effectivenss reduction).

Both baseline linear controllers suffer and lose stability in the middle of the simulation. Only adaptive controller is able to ensure stability and achieve command tracking.


Bank-to-Turn Control of Vulture VFA

2015 AIAA JGCD pre-print

This section describe the application of our adaptive output-feedback theory on a high-fidelity VFA model. Recently, a VFA platform, denoted as Vulture, has been under development to meet the goals of HALE maneuvers.

Vulture_Model

Figure: Vulture VFA model can be considered as hundreds of 3-wing VFA adjoining together

The VFA model represents a typical MIMO plant with limited measurements and actuation, and can be viewed as hundreds of 3-wing VFA adjoined together. The goal is to make the VFA bank to turn (BTT), i.e. tracking a roll angle command, while keeping the VFA oriented. The details of problem statement can be found here. The simulation results validate the performance of the adaptive system when the vehicle was subject to a large magnitude of actuator anomaly and plant uncertainty (10ft dihderal change and 50% actuator magnitude surge).

The figure below presents a segement of the simulation to illustrate the mechanism of the adaptive controller. It clearly shows that the adaptive controller not only stabilize the system but also achieve ideal performance, while LQR controller suffers in performance and LQG controller suffers in stability. The success of adaptive controller roots from its underlying principle: compare system's real performance with ideal reference performance from the very beginning and adjust control action accordingly until the two become same.

Adaptive control outline

Figure: The underline principle of the adaptive controller: compare actual performance with an ideal reference performance and actively tune the control gain until the two become identical

Unmanned Air Vehicles

Adaptive control of Unmanned Aerial Systems

Control of UAVs in Freeflight

The application of adaptive control to aircraft promises benefits in both safety and robustness. Unmanned Aerial Vehicles (UAVs) represent the next logical step in the evolution of adaptive flight control. UAVs offer several benefits over traditional aircraft, many of which also improve the case for adaptive flight control of these vehicles. Some of these benefits are:

  1. UAVs are able to execute high-performance maneuvers that a typical pilot would not be able to withstand.
  2. Control surfaces do not have to be designed with human operation in mind. Pilots mostly decouple the pitch, roll, and yaw loops through 3 sets of controllers, the elevators, ailerons, and rudder. A computer-controlled pilot is able to use any combination of available control surfaces to generate the desired forces and moments.
  3. UAVs can be made lighter, smaller, and with less exacting specifications than manned aircraft.
  4. UAVs can operate in dangerous or hostile environments without risking the safety of a crew.

Because of points 3 and 4, a UAV is more prone to damages and failures than a traditional aircraft, and as such, it is desirable to utilize a controller that explicitly accounts for uncertainty by design. Furthermore, such a controller must be able to achieve high performance and use all available actuators to generate the desired response. Adaptive control is a natural fit for this problem. There are several areas of ongoing research related to adaptive control of UAVs at the AAC lab. For more details, see:

Single UAV

Quadrotor helicopters have been an increasingly popular research platform in recent years. Their simple design and relatively low cost make them attractive candidates for swarm operations, a field of ongoing research in the UAV community. In designing a controller for these aircraft, there are several important considerations specific to this problem. There are numerous sources of uncertainties in the system–actuator degradation, external disturbances, and potentially uncertain time delays in processing or communication. These problems are only amplified in the case of actuator failures, where the aircraft has lost some of its control effectiveness. Additionally, the dynamics of quadrotors are nonlinear and multivariate. There are several effects to which a potential controller must be robust: the aerodynamics of rotor blade (propeller and blade flapping), inertial anti-torques (asymmetric angular speed of propellers), as well as gyroscopic effects (change in orientation of the quadrotor and the plane of the propeller).

The redundancy in the actuators of a quadrotor makes them robust towards a set of partial failures. Though the performance and maneuverability will most likely be reduced in the case of such a failure, it is desirable for a controller to stabilize the system and allow for reduced mode operations such as a safe return, stable hover, etc. Adaptive control is an attractive candidate for this aircraft because of its ability to generate high performance tracking in the presence of parametric uncertainties.

A composite adaptive controller has also been implemented for these quadrotor UAVs. Composite adaptive control combines aspects of direct and indirect adaptive control, and has been shown to produce smoother transients in many applications. Recent work suggests that composite adaptive control may be an effective tool for dealing with systems with time delay, such as the quadrotor system. The following video shows a performance comparison between nominal, adaptive, and composite adaptive.

Multiple UAV

Several missions such as surveillance, exploration, search-and-track, and lifting of heavy loads are best accomplished by multiple UAVs, leading to a savings in both time and money. Another important advantage to utilizing multiple vehicles is a further reduction in the risk to successful completion of a mission due to the loss of a single vehicle. When a single vehicle malfunctions, neighboring vehicles can adjust their configuration to compensate. This increased robustness can lead to a commensurate decrease in vehicle specifications and cost, further improving the argument for swarm operations.

Our objective is to carry out coordinated control of a fleet of UAVs so as to maintain a desired configuration in the presence of individual vehicle uncertainties, uncertain environmental obstacles, and limited intra-UAV communication. We address this problem using an adaptive approach that suitably utilizes local and global information to carry out trajectory control, path planning, and configuration control. In our preliminary simulation studies of a coordinated control task in the presence of a partial vehicle failure, it was found that including adaptation in the outer-loop decreases the overall configuration error by 45% over a non-adaptive approach. It was observed that including adaptation in both the inner- and outer-loop results in a 55% improvement in configuration error and a 28% reduction in individual vehicle tracking errors.

Tools

The design and testing of advanced, high-performance controllers for safety critical applications necessitates the use of advanced tools such as high-fidelity simulation and hardware-in-the-loop testing. A quadrotor simulation environment has been developed which can be used to quickly evaluate the efficacy of several controller designs. This system consists primarily of a 6 degree of freedom rigid body model of the quadrotor and controllers implemented in MATLAB, as well as a 3D visualization system developed in C++. This combination of simulation and visualization allows for rapid testing and evaluation of control of the quadrotor aircraft. The hardware-in-the-loop essentially replaces the plant model (which is very approximate) in the simulation system.

UAV in Presence of Contact

UAVs have found applications mostly in surveillance where in the task is to follow a collision free trajectory. Our focus here is to study an interesting case where in a UAV is in physical contact with the environment. Such contact based interaction has potential novel applications for UAVs (of type - VTOL, vertical take off and landing), for example UAV can carry out an in-air manipulation (opening a door for example). Another application is to robustify UAVs against accidental collisions, which are often catastrophic. A collision can lead to severe tracking error or may cause serious damage. Our focus here is to understand the rich interaction dynamics of VTOL-UAVs with the environmental objects, and to develop control algorithms for manipulation tasks and for stable flight tasks.

Accidental Collision and Control: ParaFlex, a flexible Quadrotor

We have designed a simple, flexible quadrotor called ParaFlex. The goal here is to let the ParaFlex collide with an obstacle and still be stable where a non-flexible UAV would fail. Initial simulation results, shown in the figure, suggest that the ParaFlex significantly reduces the structural stresses experienced by a non-flexible Quadrotor.

Manipulation using UAVs

In addition to flight task, UAVs have the ability to manipulate objects and perform various tasks. Because the UAV uses a combination of forces to produce flight, the forces can be directed onto another object in order to perform manipulative tasks. The advantages to manipulation using UAVs are:

  1. UAVs can do tasks in areas deemed to be hazardous for humans.
  2. UAVs are able to reach destinations and perform tasks quicker than various land robots.
  3. The payloads of UAVs are high allowing the movement and transportation of heavy objects.

A nominal flight controller that is designed and optimized for flight performance often fails when the UAV comes in contact with an object, often leading to failure or crash. Because the UAV performs both flight task as well as manipulation task, the modeling and control scheme has to be reconsidered from ground up. We propose an array of strategies for manipulation using a Quadrotor UAVs and in particular case, we propose a novel flexible UAV design. While manipulation with a single UAV is adequate in most situations, additional degree of control is desired in some cases. Multiple UAVs can simultaneously manipulate an object leading to greater degree of control, but at the cost of modeling and control complexity.

In order to validate the theory and transit into experiments, we designed a test-bed: Simulation, Test and Validation Environment (STeVE). STeVE is a collection of cots simulation tools, integrated with custom built software and hardware modules. The main advantage being seamless transition from theory, to simulation to experiments -- all in one platform. Hardware-in-the-loop module permits both model identification as well as realistic simulation. The control modules in simulation can easily be cast into a real-time control system for the actual flight experiments, thus reducing development time.

Single UAV Manipulation

Constant Contact Manipulation

Because of its VTOL trait, UAVs can become a practical way of accomplishing tasks in hazardous areas, such as collapsed buildings or places filled with radioactivity, without putting people at risk. UAVs can be thought of as a force producing platform. Forces generated by the actuators can not only induce vehicle motion, but can also be utilized to manipulate objects in space, for example pushing an object, like a door. Manipulation tasks require the UAV to establish a physical contact with the object, possibly through an end-effector that is attached to the UAV.

Our goal is to develop control strategies and UAV modifications so that the UAV maintains contact with the object while applying the necessary force for the manipulation. In the experiments involving manipulation, we use a quadrotor with an arm as an end-effector to perform the manipulation. The end-effector makes contact and docks on the object before the start of the manipulation. For an example, see the video below:

Other end-effectors used in UAV manipulation include cable drives and grippers. The disadvantage of a cable drive is that the cable needs to tied to the object beforehand and hence is less practical. A gripper on the other hand requires a suitably designed fixtures on the object to grip on to. Moreover, it also demands precision maneuver in order to align the gripper with the right spot on the object, which in turn requires precise state estimates. All these tend to reduce the wide applicability of UAV manipulation.

Other experiments involve the issue of friction within the manipulation. If the the objective is to get the end-effector to slide on another object, the friction is felt by the vehicle itself lowering the stability of the contact. Take a look at the example of the Quadrotor writing on the board where friction has to be taken into consideration:

Impact Manipulation

Another type of manipulation involves the UAV to come in contact with the object by collision rather than constant contact. This requires the UAV to build up momentum and strike a specific point on the object. By doing this this, the UAV is creating a stronger force at the point of contact and allows it to manipulate heavier objects.

Multiple UAV Manipulation

The advantage of using multiple UAVs in manipulation is the ability to carry a higher payload and better precision. The dynamics of multiple UAVs in contact with one object follow the same dynamics structure as a single UAV. However, the UAVs require constant communication with each other in order to the produce the desired manipulation. Ideally, multiple UAVs would be controlled by one controller that would allow them to be in sync with one another. Below is a video showing the cooperation of two UAVs in order to move an object in a 1 degree of freedom direction.

Safety in Air Vehicles

The challenge of achieving safe flight comes into sharp focus in the face of adverse conditions that can occur due to faults, damage, or upsets. When these adverse situations occur in aircraft actuators and sensors, the corresponding uncertainties directly affect safe operation of the aircraft. The inherent nonlinearities and multiple time-scales in the actuators further exacerbate the problem. An ongoing project addresses the development of an Adaptive Control Technology for Safe High-Performance Aircraft. This technology will include (i) gain, delay and failure margins for the adaptive control system that quantify the guarantee of safe performance in the presence of actuator anomalies, modeling errors, and delays and (ii) an architecture that accommodates multi-rate dynamics of flight and propulsion as well as magnitude saturation and rate saturation effects.

A Generic Transport Aircraft

A Generic Transport Aircraft

NASA has developed a dynamically scaled model of a transport aircraft, denoted as a Generic Transport Model (GTM). The corresponding high-fidelity simulink model incorporates non-linear aerodynamic models extracted from wind tunnel data, avionics, sensor dynamics, engine dynamics, atmospheric models, sensor noise and bias, and telemetry effects. This aircraft has ten controllable inputs: the elevator in the left outboard, the elevator in the right outboard, the elevator in the right inboard, the elevator in the left inboard, the left aileron, the right aileron, the upperrudder, the lower rudder, and the throttle inputs to the right and left engines. Overall, the open-loop plant has 278 states.

GTM Control Design

An adaptive controller has been developed that accommodates a range of uncertainties for the Generic Transport Model (GTM) including changes in the location of the Center of Gravity (CG), time-delays in the control inputs, symmetric and asymmetric losses in control effectiveness, locked-up surfaces, aerodynamic uncertainties, and engine out conditions. The performance is evaluated using several metrics including loading factor, handling qualities, command tracking, and the ability to realize a reliable flight envelope. The control architecture suitably augments a non-adaptive baseline controller and incorporates a variety of features including gain-scheduling, accommodation of magnitude-saturation, resetting in integral action, rate saturation and control-verification based tuning of adaptation gains.



Flight Test Scenario
  Analog time delay 60 ms
  Digital time delay 105 ms
  Left aileron Locked -10 degu
  Left inboard elevator Locked 0 deg
  Bottom rudder Locked 0 deg
  Top rudder 25% effective
  Left outboard elevator 50% effective
  All right elevators 25% effective
  Center of gravity shift -45% MAC

Internal Algorithm Monitors for Adaptive Systems

While the field of adaptive control has a long history of theoretical analysis and design methodology, the transfer to practice is still a challenge. One of the impediments in this transfer is the lack of systematic methods that allow a safe implementation of adaptive designs. Efforts are ongoing in our laboratory to develop internal monitors of the adaptive systems so as to provide a leading indicator for unexpected behavior. This will allow for the safe testing of adaptive algorithms on real experimental testbeds.

Hypersonics

Adaptive Robust Control for Hypersonic Vehicles

A unique control architecture is being developed in our laboratory that is a unique combination of adaptive deterministic methods and probabilistic robust control methods, thereby attaining satisfactory performance amidst various uncertainties that occur along the flight envelope. The architecture includes:

  1. Adaptation to thrust and aerodynamic uncertainties, actuation uncertainties that may occur due to anomalies in the actuator such as loss of effectiveness, failures, and saturation, or due to control moment/force changes, center-of-gravity movements, and time-delays.
  2. Two classes of integrated adaptive controllers, where the first class of controllers includes a baseline proportional integral filter commonly used in optimal control designs, and a saturation-accommodating adaptive controller that is capable of overcoming problems of non-matching. The second class of controllers accommodates various actuation uncertainties through a novel input-matrix representation and nonlinear damping.
  3. Development, implementation and application of a standalone framework for the robustness analysis of controllers to parametric uncertainties. The optimization-based methodology, which is applicable to nonlinear systems having an arbitrary dependency on the uncertain parameters, enables the assessment and comparison of competing control alternatives regardless of the methods, assumptions, and control structure used to derive them.

Development, implementation and application of a standalone framework to control tuning in order to improve the robustness characteristics of a controller. Strategies were derived that enable bounding the region of the design space where the design requirements are satisfied by all closed-loop systems associated with a prescribed uncertainty set.

Validation and Verification of Adaptive Flight Control Systems

One of the main obstacles to the implementation of adaptive controllers for safety critical applications is the absence of analytically justified Verification and Validation (V&V) techniques for such systems. A project is underway to determine the beginnings of a theoretically motivated V&V technique for adaptive controllers in the context of controlling uncertain flight vehicle dynamics. A set of tools is currently being developed for the following problems:

Guaranteed Delay Margins

A long standing problem in adaptive control is the derivation of robustness properties in the presence of unmodeled dynamics and time-delays, a necessary and highly desirable property for designing adaptive control for systems with trustable autonomy. Our on-going research is focused on a solution to this problem for linear time-invariant plants whose states are accessible for measurement. This is accomplished by using a Lipschitz continuous projection algorithm that allows the utilization of properties of a linear system when the adaptive parameter lies on the projection boundary. This in turn helps remove the restriction on plant initial conditions, as opposed to the currently existing proofs of semi-global stability. Derivation of robustness properties in the presence of time delays, i.e.,the guarantee of a nonzero delay margins, and analytically computable delay margins are some of the main objectives.

Publication: M. Matsutani, A.M. Annaswamy, and E. Lavretsky, “Guaranteed Delay Margins for Adaptive Control of Scalar Plants,” 2012 Conference on Decision and Control (submitted).

Rate Constraints in Adaptive Controllers

Although input rate saturation nonlinearity is considered to be relatively less common than input magnitude saturation, input rate constraints are also observed in many practical systems. Actuator rate saturation in aircraft control is known to lead Pilot Induced Oscillations (PIO), which expose the aircraft to the risk of crash landing. In the event of control surface damage, it is also possible to have additional complexities if rate saturation is dominant.

We have proposed an adaptive control design to remedy the rate saturation nonlinearities imposed on the control input in addition to the uncertainties present in the system. The suggested adaptive controller has two features, the first one of which is anti-windup modification which was originally suggested for accommodating only magnitude saturation. The second is a model mismatch estimation term in the adaptive law. With both of these features, the stability of the resulting closed-loop adaptive system is guaranteed. The results include superior performance and a significant improvement in flight safety under adverse conditions compared to a basic adaptive controller.

Publication: Megumi Matsutani, Anuradha Annaswamy, Luis G. Crespo, ‘Adaptive Control in the Presence of Rate Saturation with Application to a Transport Aircraft Model’, AIAA Guidance, Navigation and Control Conference, Toronto, Canada, 2010

Adaptive Reset Control

Reset control, where a relevant signal in a controller such as an error-integral state is reset to zero under a certain condition, addressed improved feedback performance by providing more flexibility in linear controllers and has achieved renewed attention during recent years.

We have developed an adaptive reset controller which includes a resetting strategy to prevent integrator windup to achieve the improved performance. The underlying architecture of this reset controller includes anti-windup features similar to the earlier works and an additional component of resetting.

Publication: Matsutani, M., Annaswamy, A.M., ‘An adaptive reset control system for flight safety in the presence of actuator anomalies’, In proceedings of American Control Conference, Baltimore, MD, 2010. Megumi Matsutani, Anuradha Annaswamy, Luis G. Crespo, ‘Application of a Novel Adaptive Reset Controller to the GTM’, AIAA Guidance, Navigation and Control Conference, Toronto, Canada, 2010