INTELLLIGENT SYSTEMS ROADMAP
Topic Area: Adaptive and Non-Deterministic Systems
Christine Belcastro, NASA LARC
Nhan Nguyen, NASA ARC
1.
Roles and Capabilities
As demands on aerospace accessibility increase and become more complex, intelligent systems
technologies can play many important roles to improve operational efficiency, mission
performance, and safety for current and future aerospace systems and operations. Future
intelligent systems technologies can provide increased adaptive and autonomous capabilities at
all levels, as illustrated in Figure 1. At the lowest level of autonomy, adaptation through closedloop control and prognostics enables aerospace systems to be more resilient by automatically
adjusting system operations to cope with unanticipated changes in system performance and
operating environment. At mid-level of autonomy, planning and scheduling provide capabilities
to perform automatic task allocations and contingency management to reduce human operator
workloads and improve situational awareness and mission planning. At high levels of autonomy,
automated reasoning and decision support systems provide higher degrees of intelligence to
enable aerospace systems to achieve autonomous operations without direct human supervision in
the loop.
Figure 1. Levels of Autonomy Integration with Human Operators
Adaptive systems are an important enabling feature common to all these levels of autonomy.
Adaptive systems can learn and optimize system behaviors to improve system performance and
safety. Adaptive systems can also enable efficient, intelligent use of resources in aerospace
processes. Furthermore, adaptive systems can predict and estimate aerospace system’s long-term
and short-term behaviors via strategic learning and tactical adaptation. As a result, safety
improvements through resiliency can be achieved by adaptive systems, which can automatically
perform detection and mitigation of uncertain, unanticipated, and hazardous conditions, thereby
enabling real-time safety assurance.
As aerospace systems become increasingly more complex, uncertainty can degrade system
performance and operation. Uncertainty will always exist in all aerospace systems, no matter
how small, due to imperfect knowledge of system behaviors, which are usually modeled
mathematically or empirically. Uncertainty can be managed but cannot be eliminated. Typical
risk management of uncertainty in aerospace systems requires improved system knowledge by
better system modeling which can be very expensive, built-in safety margins and operational
restrictions which can sometimes adversely impact performance if safety margins are
unnecessarily large or operational restrictions are not well established.
Adaptive systems can better manage uncertainty in aerospace systems if they are properly
designed. Uncertainty is managed by adaptation, which adjusts system behaviors to changing
environment through learning and adopting new behaviors to cope with changes. Adaptive
systems provide learning mechanisms to internally adjust system performance and operation to
achieve desired system behaviors while suppressing undesired responses, and to seek optimal
system behaviors over long time horizon.
Adaptive systems achieve adaptation through short-term tactical adaptation and long-term
strategic learning and self-optimization. Tactical adaptation usually involves the need to adjust
system behaviors to cope with rapid changes in operating environments that could cause safety
concerns. Model-reference adaptive control is an example of a tactical adaptation strategy that
has many potential promises in future aerospace systems. Adaptive flight control has many
applications in air and space vehicle systems.
Strategic learning and self-optimization are learning mechanisms of adaptive systems that can
take place over a longer time horizon. This adaptation mechanism usually addresses the need to
adjust system behaviors to optimize system performance in the presence of uncertainty.
Examples of strategic learning are reinforcement learning and extremum seeking selfoptimization in aerospace systems. Air and space vehicles can leverage extremum seeking selfoptimization to adjust their flight trajectories and performance characteristics to achieve energy
savings.
Adaptive systems can provide many useful applications in aerospace systems. Adaptive flight
control for safety resiliency to maintain stability of aircraft with structural and/or actuator
failures has been well studied. Real-time drag optimization of future transport aircraft is an
example of extremum seeking self-optimization that can potentially improve fuel efficiency of
aircraft. Adaptive traction control of surface mobility planetary rovers can be applied to improve
vehicle traction on different types of surface. Adaptive planning and scheduling can play a role
in air traffic management to perform weather routing or traffic congestion planning of aircraft in
the National Air Space.
Machine learning techniques are commonly used in many adaptive systems. These techniques
sometimes employ neural networks to model complex system behaviors. The use of multi-layer
neural networks can result in non-determinism due to random weight initialization. Nondeterministic behaviors of these adaptive systems can cause many issues for safety assurance and
verification and validation. Neural networks are not the only source of non-determinism.
Stochastic processes such as atmospheric turbulence, process noise, and reasoning processes
such as due to diagnostics/prognostics can also be sources of non-determinism.
Adaptive systems offer potential promising technologies for future aerospace systems.
Nonetheless, many technical challenges exist that prevent potential benefits of adaptive systems
from being fully realized. These technical challenges present technology barriers that must be
addressed in order to enable intelligent systems technologies in future aerospace systems.
2.
Technical Challenges and Technology Barriers
Technical challenges and technology barriers must be defined at all levels of autonomy
integration with human operators for ensuring safety, operational efficiency, and improved
performance. Figure 2 illustrates this concept for aircraft systems, with levels of autonomy
integration associated with a potential timeframe for implementation. At the lowest level of
integration to support existing baseline systems and human operators, adaptive and reasoning
systems can improve safety through resilience under uncertain, unexpected, and hazardous
conditions by providing improved situation awareness, guidance, and temporary interventions
under emergency conditions. At a mid-level of integration, semi-autonomous systems can
enable synergistic dynamic teaming between human operators and intelligent systems to improve
safety and operational efficiency. At the highest level of integration, fully autonomous systems
can ensure safety and optimize operational efficiency and performance, while keeping a
(possibly remote) human operator informed of current status and future potential risks.
Key Technology Impediment: Certification of Safety-Assured
Autonomy for Reliable Operation under Uncertainties & Hazards
Ultra-Reliable Fully Autonomous Systems
Pilot-Optional Aircraft
5 – 10 Years
Enable Safety-Assured Operations at All NAS Levels
(Vehicles, Infrastructure, and Operations)
Variable Autonomy Systems
1 – 5 Years
10 – 20 Years
Technical Challenges
Resilient Systems
Enable Synergistic Dynamic Teaming
Between Human and Intelligent
Systems
Provide Safety Augmentation,
Guidance & Emergency
Intervention to Support
Baseline Systems and Human
Operator
Single-Pilot Operations
Remotely Piloted
UAS
Baseline: Technology Used to
Automate Routine Operations
under Nominal Conditions and
Provide Information & Alerts
Current Operations
Figure 2. Illustration of Adaptive Systems Multi-Level Role for Aircraft
Technical challenges and technology impediments are summarized below for improving safety,
operational efficiency, and performance at all integration levels.
2.1. Improved Safety
Key technical challenges and technology impediments for achieving resilience of safetycritical aerospace systems at all integration levels under uncertain, unexpected, and
hazardous conditions are summarized below.
2.1.1. Technical Challenges:


Development and Validation of Resilient Systems Technologies for Multiple
Hazards
 Reliable Contingency Management (Control & Routing) for Unexpected
Events
 Accurate & Fast Situation Assessment, Impacts Prediction, and
Prioritization
 Fast Decision-Making and Ensured Appropriate Response
 Real-Time Sensor & Information Integrity Assurance
Development and Validation of Variable Autonomy Systems that Enable
Effective Teaming between Human Operators and Automation
− Standard, Effective, and Robust Multiple Modality Interface System

− Common Real-Time Situation Understanding between Human and
Automation (Including standard taxonomies and lexicon of terms)
− Real-Time Dynamic Effective Task Allocation and Decision Monitoring
Development and Validation of Ultra-Reliable Safety-Assured Autonomy
Technologies
− Real-Time Safety Assurance
− Universal Metrics and Requirements for Ultra-Reliable Safety-Assured
Autonomy
− Hierarchical Integration and Compositional Analysis between Control and
Planning Functions
2.1.2. Technology Impediments





Certification of Safety-Assured Autonomy Systems for Reliable Operation
under Uncertain, Unexpected, and Hazardous Conditions
Integration into Existing Flight Deck Equipment and Operational System
(e.g., Air Traffic Management (ATM) System)
Public and Policy Perceptions associated with a Lack of Trust in Autonomy
Technologies
Cyber Security (both a Challenge and an Impediment)
Lack of Alignment and Integration between Control, Artificial Intelligence
(AI), and Software Validation and Verification (V&V) Communities
2.2. Improved Operational Efficiency
Key technical challenges and technology impediments for improving operational efficiency
for semi-autonomous and fully autonomous aerospace systems are summarized below.
2.2.1. Technical Challenges


Real-Time Decision Support and Mission Planning for Single Pilot Operations
Pilot Monitoring and Decision-Making for Impaired Pilot (or Human
Operator)
2.2.2. Technology Impediments





Certification of Adaptive Systems and Automatic Takeover of Control
Authority under Pilot Impairment
Integration into Existing Flight Deck Equipment and ATM System
Public and Policy Perceptions associated with a Lack of Trust in Autonomy
Technologies
Cyber Security (both a Challenge and an Impediment)
Lack of Alignment and Integration between Control, Artificial Intelligence
(AI), and Software Validation and Verification (V&V) Communities
2.3. Improved Performance
Key technical challenges and technology impediments for improving performance for semiautonomous and fully autonomous aerospace systems are summarized below.
2.3.1. Technical Challenges


Real-Time Drag, Aerodynamic, Aeroservoelastic, and Aeropropulsive
Optimization
Real-Time Optimization, Convergence, and Computational Intensity
2.3.2. Technology Impediments





Certification of Adaptive Systems for Unstable Aerospace Vehicles
Integration into Existing Flight Deck Equipment and ATM System
Public and Policy Perceptions associated with a Lack of Trust in Autonomy
Technologies
Cyber Security (both a Challenge and an Impediment)
Lack of Alignment and Integration between Control, Artificial Intelligence
(AI), and Software Validation and Verification (V&V) Communities
These technical challenges and technology impediments define research needs that must be
addressed.
3. Research Needs to Accomplish Technical Challenges and
Overcome Technology Barriers
Despite many recent advances, adaptive systems remain at a technology readiness level of 5. The
furthest advancement of this technology has been flight testing on piloted research aircraft and
subscale research aircraft under high-risk conditions, but no production safety-critical aerospace
systems have yet employed adaptive systems. The existing approach to adaptive control
synthesis generally lacks the ability to deal with integrated effects of many different
(multidisciplinary) flight physics. In the presence of vehicle hazards such as damage or failures,
flight vehicles can exhibit numerous coupled effects that impose a considerable degree of
uncertainty on the vehicle performance and safety. To adequately deal with these coupled
effects, an integrated approach in adaptive systems research should be taken that will require
developing new fundamental multidisciplinary methods in adaptive control and modeling. These
multidisciplinary methods in adaptive control research would develop a fundamental
understanding of complex system interactions that manifest themselves in system uncertainty.
With an improved understanding of the system uncertainty, effective adaptive systems could be
developed to improve performance while ensuring robustness in the presence of uncertainty.
Another future research goal is to develop simplified adaptive systems that reduce the
introduction of non-determinism. Despite the potential benefits of neural network applications in
adaptive systems, real-world experiences through flight testing seem to suggest that simplified
adaptive systems without neural networks may perform better in practice than those with neural
networks. Simplified adaptive systems may have other advantages in that they may be easier to
be verified and validated, and there are some existing adaptive control methods that can be
applied to assess stability margins.
Applications of real-time self-optimization systems are still very limited, but the potential
benefits of these systems can be enormous. Aircraft with self-optimization can potentially
achieve significant fuel savings when equipped with suitable control systems that enable selfoptimization. Research in methods of real-time extremum seeking self-optimization is needed to
advance the technology to a level where it can consistently demonstrate reliability and
effectiveness.
Research is needed in the development and validation of resilient control and mission
management systems that enable real-time detection, identification, mitigation, recovery, and
mission planning under multiple hazards. These hazards include vehicle impairment and system
malfunction / failure, external and environmental disturbances, human operator errors, the
sudden and unexpected appearance of fixed and moving obstacles, safety risks imposed by
security threats, and combinations of these hazards. Resilient control and mission management
functions include adverse condition sensing, detection, and impacts assessment, dynamic
envelope estimation and protection, resilient control under off-nominal and hazardous
conditions, upset detection and recovery, automatic obstacle detection and collision avoidance,
and mission re-planning and emergency landing planning. Metrics and realistic current and
future hazards-based scenarios are also needed for resilience testing of these systems, including a
means of generating these hazards with an element of surprise during testing with human
operators.
Research into variable autonomy systems is needed to facilitate dynamic effective teaming
between the automation and human operators. Specific areas of research include real-time
dynamic function allocation and interface systems, resilient guidance and autonomous control
systems for loss of control prevention and recovery, as well as diagnostic and prognostic systems
that enable information fusion, guidance and decision support under complex off-nominal and
hazardous conditions.
Research is also needed for the development and validation of supervisory and management
systems that enable real-time safety assurance of resilient, semi-autonomous, and fully
autonomous systems operating under uncertain, unexpected, and hazardous conditions. These
systems would monitor current vehicle and environmental conditions, all information provided
by and actions taken (or not taken) by human operators and integrated intelligent systems
(including vehicle health management, resilient control and mission planning), and assess the
current and future safety state and associated safety risks in terms of multiple safety factors
(including vehicle health and airworthiness, remaining margin prior to entering a loss of control
condition, and time remaining for recovery). This capability would require deterministic and
stochastic reasoning processes as well as an ability to reliably and temporarily intervene if
necessary over both human and intelligent automation systems, while providing situational
awareness and guidance to both.
Verification and validation (V&V) research is viewed as a key research to enable adaptive
systems to be operational in future flight vehicles. V&V processes are designed to ensure that
adaptive systems function as intended and the consequences of all possible outcomes of the
adaptive control are verified to be acceptable. Currently, software V&V research is being
conducted but is not aimed at adaptive systems or the functional validation of these systems at
the algorithm level. To effectively develop verifiable adaptive systems, the roles of adaptive
systems theory as well as aerospace system modeling should be tightly integrated with V&V
methods. Otherwise, the V&V research could become stove-piped, thereby resulting in
challenges in implementation.
Additional research needs related to all of the above capabilities include the ability to model and
simulate highly complex and multidisciplinary vehicle dynamics effects (e.g., associated with
multiple hazards), sensor and information integrity management to ensure that faulty data is not
being used by human operators or intelligent systems in decision-making and actions taken (or
not taken), as well as improved cost-effective methodologies for evaluating (through analysis,
simulation, and experimental testing) safety-critical integrated autonomous systems operating
under uncertain, unexpected, and hazardous conditions. These capabilities are needed for both
the development and validation of advanced integrated resilient and autonomous systems
technologies at all levels of implementation, as well as in gaining trust in their effective response
under uncertain, unexpected, and hazardous conditions.
Figure 3 provides a detailed assessment of enabling technologies and research needs associated
with improved aircraft safety at all levels of implementation over the near, mid, and far term.
Figure 4 summarizes the research needed to address a key technology impediment for fielding
these systems – certification.
Enabling Technologies /
Research Needs
Key Technology Impediment: Certification of Safety-Assured
Autonomy for Reliable Operation under Uncertainties & Hazards
Ultra-Reliable Fully Autonomous Systems
Safety-Assured Autonomy for
Reliable Operation under
Uncertainties & Hazards
Pilot-Optional Aircraft
5 – 10 Years
Enable Safety-Assured Operations at All NAS Levels
(Vehicles, Infrastructure, and Operations)
Variable Autonomy Systems
1 – 5 Years
10 – 20 Years
Technical Challenges
Resilient Systems
Enable Synergistic Dynamic Teaming
Between Human and Intelligent
Systems
Provide Safety Augmentation,
Guidance & Emergency
Intervention to Support
Baseline Systems and Human
Operator
Single-Pilot Operations
Remotely Piloted
UAS
Baseline: Technology Used to
Automate Routine Operations
under Nominal Conditions and
Provide Information & Alerts
• Real-Time Safety Assurance
• Resilient Control & Mission Management
• Integrated Vehicle Health Management
Current Operations
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Real-Time Dynamic Function Allocation & Interfaces
Resilient Control under LOC Hazards Sequences
LOC Prediction, Prevention & Recovery
Resilient Mission Planning
Diagnostics / Prognostics & Decision Support
Information Fusion & Complex Situation
Assessment / Prediction
Adverse Condition Sensing, Detection & Impacts Assessment
Dynamic Envelope Protection
Resilient Control under Off-Nominal Conditions
Upset Detection & Recovery
Automatic Obstacle Sensing & Collision Avoidance
Emergency Landing Planning
Improved Situation Awareness & Guidance
Sensor & Information Integrity Management
Baseline: Altitude Hold, Autoland, Nominal Envelope
Protection, TCAS, EGPWS, No Significant Warnings
or Guidance under LOC Hazards
Figure 3. Research Needs for Improved Safety via Resilient, Semi-Autonomous and Fully
Autonomous Systems
Enabling Technologies /
Research Needs
1 – 5 Years
5 – 10 Years
10 – 20 Years
Technology Impediments
Certification of Safety-Assured Autonomy Systems for
Reliable Operation under Uncertainties & Hazards
Validation of Safety-Critical Autonomous Systems
Enable the validation of complex integrated safety-assured
autonomous and semi-autonomous systems with
deterministic & non-deterministic components
Validation of Integrated Systems
Enable validation of complex integrated
systems at the functional / algorithm
level (including error propagation and
containment between subsystems)
Single-Pilot Operations
Validation of Resilient
Systems
Develop analytical, simulation,
and experimental test methods
that enable validation of resilient
systems technologies
Remotely Piloted
UAS
(including LOC hazards
coverage and technology level
of effectiveness and limitations)
Baseline: Standard V&V
Techniques to Support Current
Certification Requirements
Current Operations
Validation Technologies for
Resilient & Autonomous Systems
Pilot-Optional
Aircraft
• Integrated Validation Process (Analysis,
Simulation, and Experimental Methods) for
Complex Integrated Deterministic & NonDeterministic Systems
• Level of Confidence Assessment Methods
• Analysis Methods for Non-Deterministic / Reasoning
Systems
• Analysis Methods for Complex Integrated Systems
• Integrated Validation Process for Resilient Systems
• Experimental Test Methods for Integrated
Multidisciplinary Systems under Uncertain /
Hazardous Conditions
•
•
•
•
Nonlinear Analysis Methods & Tools (e.g., Bifurcation)
Robustness Analysis Methods & Tools for Nonlinear Systems
Uncertainty Quantification Methods & Tools
Stability Analysis Methods for Stochastic Filters and Sensor Fusion
Systems
• Multidisciplinary Vehicle Dynamics Simulation Modeling Methods for
Characterizing Hazards Effects
• Hazards Analysis & Test Scenarios for Resilience Testing
• Experimental Test Methods for High-Risk Operational Conditions
Baseline: Linear Analysis Methods, Gain & Phase Margins for SISO
Systems, Monte Carlo Simulations, Structured Singular Value
Robustness Analysis for MIMO Linear Systems
Figure 4. Research Needs for Addressing the Certification of Resilient, Semi-Autonomous and
Fully Autonomous System Technologies