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Current research in the field of advanced adaptive structures for space applications is reviewed. A classification of adaptive structures is proposed whereby such structures are subdivided into adaptive, sensory, controlled, active, and intelligent structures. The definition and properties of each type of adaptive structures are presented, and methods of structure control are discussed.
Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.
Control-theoretic pilot models can provide important new insights regarding the stability and performance characteristics of the pilot-aircraft system. Optimal-control pilot models can be formed for a wide range of flight conditions, suggesting that the human pilot can maintain stability if he adapts his control strategy to the aircraft's changing dynamics. Of particular concern is the effect of sub-optimal pilot adaptation as an aircraft transitions from low to high angle-of-attack during rapid maneuvering, as the changes in aircraft stability and control response can be extreme. This paper examines the effects of optimal and sub-optimal effort during a typical 'high-g' maneuver, and it introduces the concept of minimum-control effort (MCE) adaptation. Limited experimental results tend to support the MCE adaptation concept.
Rapid load transition is an essential requirement for integrated energy systems to maintain grid resilience as more renewable resources are added to the grid. Integrated solid oxide fuel cell - gas turbine (SOFC-GT) systems can provide high efficiency and low carbon emissions over a broad range of turndown. These hybrids also have the potential to enable rapid grid response. The challenge has been to demonstrate effective control strategies to manage load transitions. In the present study, a load transition of ~50% was achieved in 10 s using a novel but simple strategy. Power demand on the SOFC and the GT were ramped down concurrently. During this transition, the SOFC anode fuel was manipulated to maintain SOFC fuel utilization while the cathode inlet air flow and temperature were also manipulated to thermally protect the SOFC. This study was conducted using the Hybrid Performance (Hyper) facility at the National Energy Technology Laboratory (NETL) in a co-simulation environment with the Idaho National Laboratory (INL)'s grid-simulation. The load ramping strategy was tested using a hardware-based cyber-physical simulation methodology. The results demonstrate a high-fidelity representation of SOFC-GT hybrid dynamics and validation of the control strategy. Thermal and electrochemical transients indicated that the SOFC was well protected during rapid load turndown without violating operability constraints. This demonstration revealed the non-linear nature of tightly coupled SOFC-GT system components, especially the non-linear response of SOFC cathode air flow and inlet temperature controls. These results highlight the needs and challenges in developing adaptive automatic controls for autonomous rapid load transitions. This work demonstrates that SOFC-GT hybrids are a viable option to provide the fast-ramping characteristics essential to accommodate high levels of variable renewable power while maintaining grid resilience, reliability, and environmental performance. The results also demonstrate the utility of co-simulation in advancing the tightly-coupled integrated energy systems needed to meet goals for zero-carbon power generation.
Long duration spaceflight is known to cause a variety of biomedical stressors to the astronaut. One of the more functionally destabilizing effects of spaceflight involves microgravity-induced changes in vestibular or balance control. Balance control requires the integration of the vestibular, visual, and proprioceptive systems. In the microgravity environment, the normal gravity vector present on Earth no longer serves as a reference for the balance control system. Therefore, adaptive changes occur to the vestibular system to affect control of body orientation with altered, or non-present, gravity and/or proprioceptive inputs. Upon return to a gravity environment, the vestibular system must re-incorporate the gravity vector and gravity-induced proprioceptive inputs into the balance control regime. The result is often a period of postural instability, which may also be associated with space motion sickness (oscillopsia, nausea, and vertigo). Previous studies by the JSC Neuroscience group have found that returning astronauts often employ alterations in gait mechanics to maintain postural control during gait. It is believed that these gait alterations are meant to decrease the transfer of heel strike shock energy to the head, thus limiting the contradictory head and eye movements that lead to gait instability and motion sickness symptoms. We analyzed pre- and post-spaceflight tri-axial accelerometer data from the NASA/MIR long duration spaceflight missions to assess the heel to head transfer of heel strike shock energy during locomotion. Up to seven gait sessions (three preflight, four postflight) of head and shank (lower leg) accelerometer data was previously collected from six astronauts who engaged in space flights of four to six months duration. In our analysis, the heel to head transmission of shock energy was compared using peak vertical acceleration (a), peak jerk (j) ratio, and relative kinetic energy (a). A host of generalized movement variables was produced in an effort to isolate those that best highlighted vestibular adaptation due to spaceflight. Data suggest that astronauts used either head or body centered control to reduce the effects of heel strike shock on head position during normal walking at self-selected speeds. Moreover, the form of that control appears to fall under one of two categories: homeostatic or adaptive. Homeostatic control refers to tight constraint (small error) over the value of a given variable before and after spaceflight with little or no adaptive changes. Adaptive control refers to lesser constraint over a given movement variable with clear adaptation to earth gravity upon return from spaceflight. Heel strike shock absorption (ratio of heel to head peak acceleration) best-discriminated head and body centered control strategies. Further, peak jerk data was useful for illustrating pre- and postflight differences in segmental (shank versus head) movement energy. Results from kinetic energy analysis show high consistency between subjects and across test dates. Whether this result highlights a control strategy or is an artifact of approximating body segments using anthropometric tables is, at this point, unclear.
The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.
Continuous parameter tracking system for measuring human performance in compensatory control system
On-line system identification of linear discrete systems for implementation in a digital adaptive flight controller is considered by the conventional extended Kalman filter and a decoupling process in which the linear state estimation problem and the linear parameter identification problem are each treated separately and alternately. Input requirements for parameter identifiability are established using the standard conditions of observability for a time variant system. Experimental results for simulated linearized lateral aircraft motion are included along with the effect of different initialization and updating procedures for the priming trajectory used by the filter.
A new form of high-performance, disturbance-adaptive pointing controller for the Hubble Space Telescope (HST) is proposed. This new controller is all linear (constant gains) and can maintain accurate 'pointing' of the HST in the face of persistent randomly triggered uncertain, unmeasurable 'flapping' motions of the large attached solar array panels. Similar disturbances associated with antennas and other flexible appendages can also be accommodated. The effectiveness and practicality of the proposed new controller is demonstrated by a detailed design and simulation testing of one such controller for a planar-motion, fully nonlinear model of HST. The simulation results show a high degree of disturbance isolation and pointing stability.
A FY 2001 Center Director's Discretionary Fund task to develop a test platform for the development, implementation. and evaluation of adaptive and other advanced control techniques for brushless DC (BLDC) motor-driven mechanisms is described. Important applications for BLDC motor-driven mechanisms are the translation of specimens in microgravity experiments and electromechanical actuation of nozzle and fuel valves in propulsion systems. Motor-driven aerocontrol surfaces are also being utilized in developmental X vehicles. The experimental test platform employs a linear translation stage that is mounted vertically and driven by a BLDC motor. Control approaches are implemented on a digital signal processor-based controller for real-time, closed-loop control of the stage carriage position. The goal of the effort is to explore the application of advanced control approaches that can enhance the performance of a motor-driven actuator over the performance obtained using linear control approaches with fixed gains. Adaptive controllers utilizing an exact model knowledge controller and a self-tuning controller are implemented and the control system performance is illustrated through the presentation of experimental results.
Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.
Two control schemes designed to alleviate gust-induced vibration are analytically investigated for a helicopter with four articulated blades. One is an individual blade pitch control scheme. The other is an adaptive blade pitch control algorithm based on linear optimal control theory. In both controllers, control inputs to alleviate gust response are superimposed on the conventional control inputs required to maintain the trim condition. A sinusoidal vertical gust model and a step gust model are used. The individual blade pitch control, in this research, is composed of sensors and a pitch control actuator for each blade. Each sensor can detect flapwise (or lead-lag or torsionwise) deflection of the respective blade. The acturator controls the blade pitch angle for gust alleviation. Theoretical calculations to predict the performance of this feedback system have been conducted by means of the harmonic method. The adaptive blade pitch control system is composed of a set of measurements (oscillatory hub forces and moments), an identification system using a Kalman filter, and a control system based on the minimization of the quadratic performance function.
This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-to-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.
An approach to the calculation of the dynamic characteristics of the combined man manipulator system in manual aircraft control was derived from a model of the neuromuscular system. This model combines the neuromuscular properties of man with the physical properties of the manipulator system which is introduced as pilot manipulator model into the manual aircraft control. The assumption of man as a quasilinear and time invariant control operator adapted to operating states, depending on the flight phases, of the control system gives rise to interesting solutions of the frequency domain transfer functions of both the man manipulator system and the closed loop pilot aircraft control system. It is shown that it is necessary to introduce the complete precision pilot manipulator model into the closed loop pilot aircraft transfer function in order to understand the well known handling quality criteria, and to derive these criteria directly from human operator properties.
This paper reviews recently developed techniques of adaptive nonlinear control using neural networks, and demonstrates their application to two important practical problems in orbital operations. An adaptive neurocontroller is first developed for spacecraft attitude control applications, and then the same design, slightly modified, is shown to be effective in the control of free-floating orbital manipulators. The algorithms discussed have guaranteed stability and convergence properties, and thus constitute viable alternatives to existing control methodologies. Simulation results are presented demonstrating the performance of each algorithm with representative dynamic models.
Description of a general approach for the design of an adaptive digital control system for liquid bipropellant rocket engines. The technique employs linearized transfer functions derived from perturbations of an engine simulation. The linear models serve as a basis on which to develop candidate closed-loop control laws quickly and economically.
Although all tissues in the body can adapt to varying physiological/pathological conditions, muscle is the most adaptable. To understand the significance of cellular events and their role in controlling metabolic adaptations in complex physiological systems, it is necessary to link cellular and system levels by means of mechanistic computational models. The main objective of this work is to improve understanding of the regulation of energy metabolism during skeletal/cardiac muscle ischemia by combining in vivo experiments and quantitative models of metabolism. Our main focus is to investigate factors affecting lactate metabolism (e.g., NADH/NAD) and the inter-regulation between carbohydrate and fatty acid metabolism during a reduction in regional blood flow. A mechanistic mathematical model of energy metabolism has been developed to link cellular metabolic processes and their control mechanisms to tissue (skeletal muscle) and organ (heart) physiological responses. We applied this model to simulate the relationship between tissue oxygenation, redox state, and lactate metabolism in skeletal muscle. The model was validated using human data from published occlusion studies. Currently, we are investigating the difference in the responses to sudden vs. gradual onset ischemia in swine by combining in vivo experimental studies with computational models of myocardial energy metabolism during normal and ischemic conditions.