Control of a class of nonlinear distributed parameter systems via direct search on the performance index
Direct search of performance index for optimal control of class of nonlinear distributed parameter systems
Fan, L. T.↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Modified Cascading Generalized Inverse Control Allocation
The current aviation revolution towards electric propulsion aircraft (e.g., electric vertical takeoff-and-landing) brings unique control challenges. These vehicles are typically over-actuated (more effectors than desired control outcomes), may require control strategies for the three phases of flight (hover, transition and cruise), and currently have limited electric power availability. These vehicle challenges bring the need for optimal control allocation to the forefront of research. A leading control allocation algorithm, used in current flight vehicles, is the Cascading Generalized Inverse (CGI). Unfortunately, the Cascading Generalized Inverse algorithm is unable to achieve some desired outcomes, it intermittently provides non-optimal allocations, and it may fail to preserve moment direction near maximal achievable outcomes. In this research, the shortcomings of the Cascading Generalized Inverse algorithm are addressed by augmenting the algorithm with Scalar Difference Quadratic unsaturation identification and location at each iteration. Rigorous theory is shown that the Modified Cascading Generalized Inverse performs better at obtaining optimal allocations for all attainable outcomes. Numerical case studies for over-actuated vehicles demonstrate resolution to the aforementioned deficiencies.
Control Allocation↗
Modified Cascading Generalized Inverse Control Allocation
The current aviation revolution towards electric propulsion aircraft (e.g., electric vertical takeoff-and-landing) brings unique control challenges. These vehicles are typically over-actuated (more effectors than desired control outcomes), may require control strategies for the three phases of flight (hover, transition and cruise), and currently have limited electric power availability. These vehicle challenges bring the need for optimal control allocation to the forefront of research. A leading control allocation algorithm, used in current flight vehicles, is the Cascading Generalized Inverse (CGI). Unfortunately, the Cascading Generalized Inverse algorithm is unable to achieve some desired outcomes, it intermittently provides non-optimal allocations, and it may fail to preserve moment direction near maximal achievable outcomes. In this research, the shortcomings of the Cascading Generalized Inverse algorithm are addressed by augmenting the algorithm with Scalar Difference Quadratic unsaturation identification and location at each iteration. Rigorous theory is shown that the Modified Cascading Generalized Inverse performs better at obtaining optimal allocations for all attainable outcomes. Numerical case studies for over-actuated vehicles demonstrate resolution to the aforementioned deficiencies.
Control Allocation↗
Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection
With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.
33 ADVANCED PROPULSION SYSTEMS↗
Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor
With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .
42 ENGINEERING↗
Abradable Sealing Materials for Emerging IGCC-Based Turbine Systems (Final Report)
Reducing the gap between rotating and stationary parts in gas turbine engines, and mitigating gas leakage via these paths, can significantly increase the performance and attendant efficiency. One approach to reducing such gaps is to deploy abradable coatings on the stationary shroud components as seals, to reduce the blade-tip clearances under operational conditions. Abradable coatings must be able to withstand high temperature oxidation, thermal cycling, and erosion, while providing optimal controlled abrasion and associated shape retention. Existing abradable seals utilized in power generation turbines have generally been optimized for use with natural gas-fired systems; however, preliminary testing of syngas and high-hydrogen-content (HHC) fired turbines has shown that the stability of hot-section materials (and presumably abradable sealing materials) may be substantially altered due to characteristic changes in the combustion by-products (partial pressures of water vapor, etc.) as well as characteristic impurities and particulate matter entrained in the fuel. The primary focus of this research program has been to develop a mechanistic understanding of how the altered combustion environments modify the thermo-mechanical stability and performance of the sealing materials, including the requisite abradable sealing behavior. A key component of the research program carried out has been an exploration of mechanisms underpinning observed degradation processes, and an assessment of connections to the combustion environments and characteristic non-combustible constituents. The ultimate goal of the project has been to advance the goals of the Advanced Turbine Program by developing materials design protocols leading to turbine hot-section components with improved resistance to service lifetime degradation under advanced fuels exposures. This research program has evaluated the performance and degradation of ceramic abradable seals used in the high-temperature turbine sections of gas turbine engines. The focus has been two-fold: (1) to understand performance of such materials in relation to the turbines operating on coal-derived syngas and high hydrogen content (HHC) fuels, ultimately seeking an improved understanding of factors that control performance and durability of abradable seal materials for these combustion environments (and play into materials design protocols); and (2) to evaluate the potential of alternative materials as abradable seals for the higher temperatures expected with IGCC and HHC fueled turbine systems. In particular, we carried out an initial assessment of multi-layered systems of existing abradable seal materials (YSZ) and other ceramics. The research program also investigated several classes of abradable coatings under simulated exposures to syngas-based combustion environments, evaluating the relevant wear behavior, hardness, stability under cyclic oxidation, and general thermo-mechanical behavior. The research also attempted to develop alternatives to carrying out full-scale combustion-based abradability test rig exposures to evaluate materials system performance. The research has been focused on correlating the measured thermo-mechanical behavior, and controlled abrasive wear, with the intrinsic properties of the multilayer coatings and processing-controlled microstructural features that have shown attractive machinability characteristics based on weak interfacial bonding between the constituent phases at elevate temperatures (while maintaining thermochemical stability). This program has resulted in an improved mechanistic understanding of factors governing performance of high temperature abradable seals, and degradation mechanisms unique to IGCC-based power generation turbine systems and the realization of coal-derived syngas and HHC-based combustion environments – ultimately with the goal of developing a knowledge base upon which the design of coatings that retain optimal sealing characteristics and are more resistant to the observed wear/attack mechanisms, important aspects of advancing the goals of the DOE Advanced Turbine Program.
36 MATERIALS SCIENCE↗
Resilient Operation of Networked Community Microgrids with High Solar Penetration
This project, funded by the US Department of Energy’s Solar Energy Technologies Office (SETO), focused on the operation of microgrids as a coordinated network. The primary objective, which was successfully achieved, was to develop both control strategies and hardware solutions to support the resilient and efficient operation of networked microgrids with high solar penetration. The work was structured around the following four main tasks: • Development of distributed and scalable optimization algorithms for AC-coupled networked microgrids. • Design and implementation of a novel DC interconnection hardware to enable precise power exchange between microgrids. • Laboratory operational validation of the developed technologies using 480 V testbeds and commercially available hardware. • Field operational validation of the complete solution in Adjuntas, Puerto Rico, interconnecting two kW-scale, split-phase microgrids of Casa Pueblo’s microgrids. This project addressed multiple technical challenges across the domains of optimization, control, hardware interconnection, and protection. One of its key contributions was delivering tangible, real-world solutions for networking microgrids. In contrast to purely theoretical or simulation-based work, this project included full-scale hardware operational validation both in the lab and in the field. The work conducted as part of this project—in collaboration with the University of Puerto Rico; the University of Tennessee, Knoxville; the University of Central Florida; and Casa Pueblo—has advanced the state of the art in networked microgrids. Key contributions include the development of distributed control strategies, practical solutions for real-world implementation challenges, and the introduction of a novel DC interlink approach for microgrid interconnection. The project featured both laboratory and field validation using commercial off-the-shelf components. The field deployment successfully validated that a group of microgrids can operate in a coordinated manner, enabling precise power flow between systems and mutual support during extreme events. This project resulted in 15 journal publications and 15 conference papers; 5 graduate students and 15 undergraduate students were supported. The codes of distributed optimization and forecasting were made open-source through OSTI.gov for distributed optimization and forecasting. All the publications are available in the ORNL-hosted project landing page. The DC interlink with state-of-charge balancing control was operationally validated in Adjuntas by interconnecting two real-world, 240 V split-phase microgrids. To the best knowledge of the team, this represents the first operational validation of AC microgrids interconnected via DC-interlinks. As a culmination of this project, a follow-on grant was awarded to support the technology transfer of the distributed optimization framework to a commercial microgrid controller, Stellar Edge, developed by the California-based company New Sun Road.
14 SOLAR ENERGY↗
Model Predictive Control of parametric excited pitch-surge modes in wave energy converters
For a heave-pitch-surge three-degrees-of-freedom wave energy converter, the heave mode is usually decoupled from the pitch-surge modes for small motions. The pitch-surge modes are usually coupled and are parametrically excited by the heave mode, depending on the buoy geometry. In this paper, a Model Predictive Control is applied to the parametric excited pitch-surge motion, while the heave motion is optimized independently. The optimality conditions are derived, and a gradient-based numerical optimization algorithm is used to search for the optimal control. Numerical tests are conducted for regular and Bretschneider waves. The results demonstrate that the proposed control can be implemented to harvest more than three times the energy that can be harvested using a heave-only wave energy converter. As a result, the energy harvested using a parametrically excited model is higher than that is harvested when using a linear model.
16 TIDAL AND WAVE POWER↗
Metric Learning to Accelerate Convergence of Operator Splitting Methods
Recent developments in machine learning have led to promising advances in accelerating the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial intelligence and optimal control has led to a variety of proposed strategies for learning to produce fast solutions to optimization problems. For example, recent works have shown that it is possible to accelerate the convergence of optimization algorithms by learning to select their parameters, such as gradient descent stepsizes. This work proposes a new approach, in which the underlying metric spaces of proximal operator splitting algorithms are learned to maximize convergence rate. While prior works in optimization theory have derived optimal metrics in simple cases, no such result exists for many practical problem forms including general Quadratic Programming (QP). This paper shows how differentiable optimization can enable the end-to-end learning of proximal metrics, enhancing the convergence of proximal algorithms for QP problems beyond what is possible based on known theory. Additionally, the results illustrate a strong connection between the learned proximal metrics and active constraints at the optima, leading to an interpretation in which the predicted proximal metrics can be viewed as a form of active set prediction.
King, Ethan [BATTELLE (PACIFIC NW LAB)]↗
Quadratic performance index generation for optimal regular design.
Application of optimal control theory to practical problems has been limited by the difficulty of prescribing a performance index which accurately reflects design requirements. The task of deriving equivalent performance indices is considered in the present paper for a plant that is a completely controllable, scalar linear system with state feedback. A quadratic index is developed which leads to an optimal design performance satisfying some of the classical performance criteria.
Bullock, T. E.↗
Application of two synthesis methods for active flutter suppression on an aeroelastic wind tunnel model
Two flutter suppression control laws have been synthesized, implemented, and demonstrated on an aeroelastic wind-tunnel model of a transport-type wing. One control law was synthesized using an aerodynamic energy method and the other from using results of optimal control theory. At M = 0.95, the model was tested to a dynamic pressure 44 percent above the system-off flutter dynamic pressure. Both synthesis methods yielded control laws effective in suppressing flutter. The experimental results also indicate that wind-tunnel turbulence is an important factor in the experimental demonstration of system performance.
Abel, I.↗
Real time control for NASA robotic gripper
The ability to easily manipulate objects in a zero gravity environment will pay a key role in future space activities. Emphasis will be placed on robotic manipulation. This will serve to increase astronaut safety and utility in addition to several other benefits. The aim is to develop control laws for the zero gravity robotic end effectors. A hybrid force/position controller will be used. Sensory data available to the controller are obtained from an array of strain gauges and a linear potentiometer. Applying well known optimal control theoretical principles, the control which minimizes the transition time between positions is obtained. A robust force control scheme is developed which allows the desired holding force to be achieved smoothly without oscillation. In addition, an algorithm is found to determine contact force and contact location.
Salter, Carole A.↗
Transonic adaptive flutter suppression using approximate unsteady time domain aerodynamics
A digital adaptive controller is applied to the active flutter suppression problem of a wing under time varying flight conditions in subsonic and transonic flow. Linear quadratic controller gain at each time step is obtained using an iterative Riccati solver. The digital adaptive optimal controller is robust with respect to the unknown external loads. Flutter and divergence instabilities are simultaneously suppressed using a trailing-edge control surface and displacement sensing. A new transonic unsteady aerodynamic approximation methodology is developed which enables one to carry out the rapid calculation required for transonic aeroservoelastic applications. This approximation is based on a combination of unsteady subsonic aerodynamics combined with a transonic correction procedure. Aeroservoelastic transient time response is obtained using Roger's approximation, state transition matrices and an iterative time marching algorithm. The aeroservoelastic system in the time domain is modeled using a deterministic ARMA model together with a parameter estimator. Transonic flutter boundaries of a wing structure are computed, in the time domain, using an estimated aeroelastic system matrix and are in good agreement with experimental data for the low transonic Mach number range.
Pak, Chan-Gi↗
Autonomous vehicle motion control, approximate maps, and fuzzy logic
Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.
Ruspini, Enrique H.↗
Drag reduction strategies
previously a description was given of an active control scheme using wall transpiration that leads to a 15% reduction in surface skin friction beneath a turbulent boundary layer, according to direct numerical simulation. In this research brief further details of that scheme and its variants are given together with some suggestions as to how sensor/actuator arrays could be configured to reduce surface drag. The research which is summarized here was performed during the first half of 1994. This research is motivated by the need to understand better how the dynamics of near-wall turbulent flow can be modified so that skin friction is reduced. The reduction of turbulent skin friction is highly desirable in many engineering applications. Experiments and direct numerical simulations have led to an increased understanding of the cycle of turbulence production and transport in the boundary layer and raised awareness of the possibility of disrupting the process with a subsequent reduction in turbulent skin friction. The implementation of active feedback control in a computational setting is a viable approach for the investigation of the modifications to the flow physics that can be achieved. Bewley et al. and Hill describe how ideas from optimal control theory are employed to give 'sub-optimal' drag reduction schemes. The objectives of the work reported here is to investigate in greater detail the assumptions implicit within such schemes and their limitations. It is also our objective to describe how an array of sensors and actuators could be arranged and interconnected to form a 'smart' surface which has low skin friction.
Hill, D. Christopher↗
Global Radius of Curvature Estimation and Control System for Segmented Mirrors
An apparatus controls positions of plural mirror segments in a segmented mirror with an edge sensor system and a controller. Current mirror segment edge sensor measurements and edge sensor reference measurements are compared with calculated edge sensor bias measurements representing a global radius of curvature. Accumulated prior actuator commands output from an edge sensor control unit are combined with an estimator matrix to form the edge sensor bias measurements. An optimal control matrix unit then accumulates the plurality of edge sensor error signals calculated by the summation unit and outputs the corresponding plurality of actuator commands. The plural mirror actuators respond to the actuator commands by moving respective positions of the mixor segments. A predetermined number of boundary conditions, corresponding to a plurality of hexagonal mirror locations, are removed to afford mathematical matrix calculation.
Rakoczy, John M.↗
LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm
This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.
algorithms↗
An approximation theory for nonlinear partial differential equations with applications to identification and control
Approximation results from linear semigroup theory are used to develop a general framework for convergence of approximation schemes in parameter estimation and optimal control problems for nonlinear partial differential equations. These ideas are used to establish theoretical convergence results for parameter identification using modal (eigenfunction) approximation techniques. Results from numerical investigations of these schemes for both hyperbolic and parabolic systems are given.
Banks, H. T.↗