A dynamic input-output model of the California educational system
Dynamic programming input-output model of California educational system - correlation of educational variables to economic planning
DYNAMIC PROGRAMMING↗
SEARCH · Engineering Papers
Results for “Dynamic Programming”
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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Mathematical programming and the control of Markov chains
Linear and dynamic programming and Markov chain optimal control
Kleinman, A. J.↗
Optimal control of nonlinear discrete time systems by mathematical programming
Nonlinear discrete time sampled systems optimal control by dynamic programming, with application to stochastic systems
Tabak, D.↗
A Government/Industry Summary of the Design Analysis Methods for Vibrations (DAMVIBS) Program
The NASA Langley Research Center in 1984 initiated a rotorcraft structural dynamics program, designated DAMVIBS (Design Analysis Methods for VIBrationS), with the objective of establishing the technology base needed by the rotorcraft industry for developing an advanced finite-element-based dynamics design analysis capability for vibrations. An assessment of the program showed that the DAMVIBS Program has resulted in notable technical achievements and major changes in industrial design practice, all of which have significantly advanced the industry's capability to use and rely on finite-element-based dynamics analyses during the design process.
Kvaternik, Raymond G.↗
Simulation and analysis of optimal self-paced second order control systems
Local variation of dynamic programming algorithm and computer simulation of optimal second-order self-pace control systems
CONTROL SYSTEM↗
Connected Vehicle-Based Traffic Signal Coordination
This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle’s trajectories. Due to the complexity of the model, we decompose the problem into two levels: an intersection level to optimize phase durations using dynamic programming (DP), and a corridor level to optimize the offsets of all intersections. In order to solve the two-level model, a prediction-based solution technique is developed. The proposed models are tested using traffic simulation under various scenarios. Compared with the traditional actuated signal timing and coordination plan, the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance. When considering varies vehicle types under high demand levels, the proposed two-level model reduced the total system cost by 3.8% comparing to baseline actuated plan. MINLP reduced the system cost by 5.9%. It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels. For intersections with major and minor street, coordination conducted for major street had little impacts on the vehicles at the minor street.
42 ENGINEERING↗
Flexible Aircraft Takeoff and Landing Analysis
Flexible Aircraft Takeoff and Landing Analysis Program, FATOLA, simulates aircraft takeoff and landing dynamics. Program includes maneuver logic and autopilots for glide slope, flare, landing, and takeoff and is modular so performance of aircraft in flight and during landing and ground maneuvers can be studied separately or in combination.
Carden, H. D.↗
Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects
Interaction with tumbling objects will become more common as human activities in space expand. Attempting to interact with a large complex object translating and rotating in space, a human operator using only his visual and mental capacities may not be able to estimate the object motion, plan actions or control those actions. A robot system (RAMBO) equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a tumbling object, is being developed. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations rearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enhancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using dynamic interpolations between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.
Davis, Larry S.↗
A self-learning rule base for command following in dynamical systems
In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.
Tsai, Wei K.↗
Overview of the solar dynamic ground test demonstration program
The Solar Dynamic (SD) Ground Test Demonstration (GTD) program demonstrates the availability of SD technologies in a simulated space environment at the NASA Lewis Research Center (LeRC) vacuum facility. An aerospace industry/ government team is working together to design, fabricate, build, and test a complete SD system. This paper reviews the goals and status of the SD GTD program. A description of the SD system includes key design features of the system, subsystems, and components as reported at the Critical Design Review (CDR).
Shaltens, Richard K.↗
Update of the 2 Kw Solar Dynamic Ground Test Demonstration Program
The Solar Dynamic (SD) Ground Test Demonstration (GTD) program demonstrates the operation of a complete 2 kW, SD system in a simulated space environment at a NASA Lewis Research Center (LeRC) thermal-vacuum facility. This paper reviews the goals and status of the SD GTD program. A brief description of the SD system identifying key design features of the system, subsystems, and components is included. An aerospace industry/government team is working together to design, fabricate, assemble, and test a complete SD system.
Shaltens, Richard K.↗
COBRA-DDP: Trajectory Generation and Collision Avoidance Augmentations for eVTOL Vehicles
This paper presents a receding horizon model predictive control variation of the combined Bernstein polynomial optimal reciprocal collision avoidance (ORCA) differential dynamic programming (COBRA-DDP) algorithm for AAM vehicles. Collision avoidance in combination with effective trajectory replanning are expected to be core components of AAM vehicles operating within a crowded airspace. This environment necessitates the use of real-time trajectory planning algorithms that are capable of planning around large amounts of stationary and moving obstacles. Previous work on COBRA-DDP demonstrated the capability of the algorithm to produce dynamically feasible trajectories for AAM vehicles and general collision avoidance. This paper improves upon the previous work by increasing the number of stationary and moving obstacles, implementing a variation of COBRA-DDP that lends itself to real-time application. These advancements are demonstrated on a vertical takeoff and landing (VTOL) vehicle simulation with highly nonlinear vehicle dynamics.
COBRA-DDP↗
COBRA-DDP: Trajectory Generation and Collision Avoidance Augmentations for eVTOL Vehicles
This paper presents a receding horizon model predictive control variation of the combined Bernstein polynomial optimal reciprocal collision avoidance (ORCA) differential dynamic programming (COBRA-DDP) algorithm for AAM vehicles. Collision avoidance in combination with effective trajectory replanning are expected to be core components of AAM vehicles operating within a crowded airspace. This environment necessitates the use of real-time trajectory planning algorithms that are capable of planning around large amounts of stationary and moving obstacles. Previous work on COBRA-DDP demonstrated the capability of the algorithm to produce dynamically feasible trajectories for AAM vehicles and general collision avoidance. This paper improves upon the previous work by increasing the number of stationary and moving obstacles, implementing a variation of COBRA-DDP that lends itself to real-time application. These advancements are demonstrated on a vertical takeoff and landing (VTOL) vehicle simulation with highly nonlinear vehicle dynamics.
COBRA-DDP↗
Simulation Based Training Improves Airway Management for Helicopter EMS Teams
The use of paralytic medications in the performance of RSI intubation is a high risk intervention used by many HEMS crews. There is no margin for error in RSI intubation as the results can be fatal. Operating room access for airway management training has become more difficult, and is not representative of the environment in which HEMS crews typically function. LifeEvac of Virginia designed and implemented an SST airway management program to provide a realistic, consistent training platform. The dynamic program incorporates standardized scenarios, and real life challenging cases that this and other programs have encountered. SST is done in a variety of settings including the helicopter, back of ambulances, staged car crashes and simulation centers. The result has been the indoctrination of a well defined, consistent approach to every airway management intervention. The SST program facillitates enhancement of technical skills. as well as team dynamics and communication.
Dhindsa, Harinder S.↗
DP-Based Optimization of BESS to Substitute RICE Reserves for Improved Economic Benefits
In regions with a significant proportion of renewable energy sources, reciprocating internal combustion engines (RICE or IC) are frequently employed as grid reserve solutions. This study suggests a scheduling technique for battery energy storage systems (BESS) that utilizes dynamic programming (DP) to replace IC units, adding versatility to the total reserve capacity and resulting in higher BESS earnings compared to traditional scheduling approaches. The capacity sweeping approach to optimize BESS’s revenue sometimes leads to operation which is counter intuitive to conventional battery sizing approaches.
25 ENERGY STORAGE↗
Incorporating Valve Switching Losses Into a Static Optimal Control Algorithm for the Hybrid Hydraulic-Electric Architecture (HHEA)
The Hybrid Hydraulic-Electric Architecture (HHEA) has, in recent years, been proposed as an energy efficient alternative to conventional load-sensing architectures in mobile machines such as excavators and wheel-loaders. HHEA leverages the advantages of hydraulic power and electric power to eliminate throttling valves while also improving the energy and control performance of the system. The architecture utilizes a set of common pressure rails to provide a majority of power and and a small electric motor driven pump to modulate this power to meet the exact demand. Previous work has developed a computationally efficient Lagrange Multiplier approach for determining the optimal pressure rail selections that minimizes the energy losses in the system. The static model used considers only the energy use for each pressure rail selection but not the losses associated with the valves during the transition. This paper presents an approach to include the switching losses in the model and in the optimization procedure. To capture the switching losses, switching events between different rails and at various input and output flow rates were simulated with consideration of valve spool dynamics. A parameterized model that summarizes the losses is then obtained, allowing switching losses to be added to the previous energy analysis. The performance of the switching loss model was compared with reference data obtained from a high-fidelity simulation model. To incorporate the switching losses into optimal control algorithm, an efficient dynamic programming approach that prevents frequent switching is adopted in place of the Lagrange multiplier approach. The overall effect of switching losses on the energy consumption and optimal control decisions is presented. In general, switching losses contribute to about 9–10% of input energy.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Data-based and secure switched cyber–physical systems
In this work, we develop a completely model-free moving target defense framework for the detection and mitigation of sensor and/or actuator attacks in cyber–physical systems with dynamics that evolve in discrete-time. We incorporate an intrusion detection mechanism based on an approximate dynamic programming technique that learns the policies for optimal regulation and optimal tracking while simultaneously defending against actuator and sensor attacks in a model-free fashion. Switching rules are leveraged to force proactive and reactive defense mechanisms as well as, guarantee the stability of the equilibrium point. Finally, as a case study, we apply the proposed moving target defense framework to a DC–DC converter that is used in electric vehicles.
42 ENGINEERING↗
Optimal economic dispatch policy for prosumer with energy storage considering self-consumption demand
This paper analyzed the effects of self-consumption demand on the joint economic dispatch of prosumers (energy consumers who are also producers), particularly for prosumers with both energy storage and distributed energy sources (DERs). Studies in the existing literature on the economic dispatch scheduling policy of energy storage, mostly from the perspective of electricity merchants, do not address the impacts of self-consumption demand. However, due to the intermittent and high levels of uncertainty regarding DERs generation and the dynamic demand of the prosumer, production and consumption are not always simultaneous; there are two possible scenarios in each period depending on whether DERs generation can meet prosumers' self-consumption or not. Incorporating the self-consumption demand will pose modeling challenges since these two scenarios cannot occur simultaneously in each period, and different scenarios require different decisions for prosumers. Further, this paper analyzed the two scenarios separately to find the optimal storage scheduling strategy, and the results were combined to get the optimal global solution. We focused on prosumers' economic decision-making while considering self-consumption demand and the physical constraints of a battery based on dynamic programming. Our study showed that the feasible state of charge (SOC) range of storage can be segmented into several sub-ranges by SOC reference points under the above two scenarios. As a result, a prosumer's optimal scheduling can be uniquely and conveniently selected based on the sub-ranges within which the current SOC falls. The results, therefore, provided multistage decision-making guidance for prosumers with energy storage.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗