Enhancing control precision, mitigating external disturbances, and ensuring real-time responsiveness stand as the cornerstone of autonomous vehicle tracking endeavors, each of which intricately interwoven to uphold op...
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Enhancing control precision, mitigating external disturbances, and ensuring real-time responsiveness stand as the cornerstone of autonomous vehicle tracking endeavors, each of which intricately interwoven to uphold operational safety. In pursuit of addressing these issues, this paper presents a triple iterative control method inspired by approximate dynamic programming (ADP) tailored for real-time disturbance avoidance. The control framework orchestrates simultaneous iterations of value function, control policy, and disturbance policy, engineered to optimize tracking control amidst external disturbances cast as a zero-sum differential game, tackled adeptly through deep neural networks. Rigorous mathematical proof underpins its triple iteration, coupled with assurances of residual error convergence, solidifying its safety guarantee ability and algorithmic resilience. To validate its effectiveness, both numerical simulations and experiments on a real micro-vehicle platform were conducted. Results underscore the feasibility of this new method, showcasing its energy-saving capability and a four-times acceleration compared to conventional model predictive control (MPC) approaches when confronted with lateral disturbances. Notably, the single-step calculation time of this method on the Raspberry Pi is only 1.44ms, affirming its practical viability and real-world applicability.
For a longer endurance of vertical and level cruise flight, an electro-gasoline hybrid power system is introduced on a compound-wing unmanned aerial vehicle (UAV). After discharging during vertical flight, the battery...
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For a longer endurance of vertical and level cruise flight, an electro-gasoline hybrid power system is introduced on a compound-wing unmanned aerial vehicle (UAV). After discharging during vertical flight, the battery pack is charged by a piston engine-driven generator, which simultaneously powers the UAV for level cruise flight. A charging model is established based on the configuration of the hybrid power system. Considering fuel consumption and battery attenuation within the typical flight profile of a compound-wing UAV, an optimized charging plan is developed using dynamic programming to determine the trajectory of the generated power sequence. To address deviations between ideal and practical flight conditions in terms of charging performance, a feedforward compensation is introduced to improve optimal tracking control within the dynamic programming framework. Simulations validate the effectiveness of the optimized charging plan, while testbench experiments confirm improvements achieved through compensation enhancement. The results demonstrate practicality with minimal overall cost compared to other conventional control plans.
We use a dynamic programming approach to construct management strategies for a hydropower plant with a dam and a continuously adjustable unit. Along the way, we estimate unknown variables via simple models using histo...
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We use a dynamic programming approach to construct management strategies for a hydropower plant with a dam and a continuously adjustable unit. Along the way, we estimate unknown variables via simple models using historical data and forecasts. Our suggested scheme achieves on average 97.5% of the theoretical maximum (optimal strategy when knowing the future) with small computational complexity. We also apply our scheme to a run-ofriver hydropower plant and compare the strategies and results to the much more involved PDE-based optimal switching method studied in [12];this comparison shows that our simple approach may be preferable when the forecast is good enough.
To improve the tracking performance of autonomous underwater vehicles (AUVs), a sliding optimal tracking control method for linear continuous systems is proposed with adaptive dynamic programming. The AUV vertical dyn...
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To improve the tracking performance of autonomous underwater vehicles (AUVs), a sliding optimal tracking control method for linear continuous systems is proposed with adaptive dynamic programming. The AUV vertical dynamic model is constructed with the kinematic principle first. The tracking model is then transformed into a linear sliding model, and the optimal feedback tracking controller is established. Considering the tracking performance of the AUV and the transient performance of the AUV, an adaptive Riccati equation is linearly parameterized, and an online learning algorithm driven by a parameter estimation error is introduced to study the optimal solution of the algebraic Riccati equation. Finally, the stability of the closed-loop system and the estimation convergence of the sliding model are proved by Lyapunov theory. Simulation results demonstrate that the proposed method effectively achieves sliding optimal tracking performance with good dynamic response, high tracking accuracy, and robustness.
The aim of this work is the consumption-optimized synthesis of an electro-mechanical power-split hybrid drive for a reference vehicle for local rail passenger transport on a given track with a given speed profile. Fir...
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The aim of this work is the consumption-optimized synthesis of an electro-mechanical power-split hybrid drive for a reference vehicle for local rail passenger transport on a given track with a given speed profile. First, a method for the optimization of the operating strategy (energy management) is developed using dynamic programming. This methodology is then applied to several variations of the drivetrain (available operating modes, transmission ratios, drive power of electric machines) to deduce a drivetrain configuration along with an operating strategy for the reference application which exhibits a global minimum regarding the fuel consumption. Results show that the battery-electric and power-split modes are the most efficient operating modes and that the drive power of the electric machines has the most significant impact on fuel consumption. Overall, the findings indicate that a fuel saving of up to 37.5% compared to a conventional diesel-mechanical reference drive operated on the same reference route can be realized with the presented optimization approach. Ziel dieser Arbeit ist die verbrauchsoptimierte Synthese eines elektrisch-mechanisch leistungsverzweigten Hybridantriebs f & uuml;r ein Referenzfahrzeug des Schienenpersonennahverkehrs auf einer gegebenen Strecke mit einem gegebenen Geschwindigkeitsprofil. Zun & auml;chst wird eine Methodik zur Optimierung der Betriebsstrategie (Energiemanagement) des Antriebs mittels dynamischer Programmierung vorgestellt. Diese Methodik wird anschlie ss end auf verschiedene Varianten des Antriebsstrangs, unter Variation verf & uuml;gbarer Betriebsmodi, Getriebe & uuml;bersetzungen sowie der Antriebsleistung der elektrischen Maschinen, angewendet, um eine Antriebsstrangkonfiguration mit einer Betriebsstrategie f & uuml;r die Referenzanwendung abzuleiten, die ein globales Minimum an Kraftstoffverbrauch aufweisen. Die Ergebnisse zeigen, dass der batterieelektrische und der leistungsverzweigte Betrieb die effizientesten Betr
作者：
Fan, Zhong-XinLi, ShihuaSu, JinyaSoutheast Univ
Sch Automat Minist Educ Nanjing 210096 Peoples R China Southeast Univ
Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Southeast Univ
Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Southeast Univ
Inst Intelligent Unmanned Syst Nanjing 210096 Peoples R China
This article aims to derive an adaptive optimal speed regulator for permanent magnet synchronous motors (PMSMs) affected by both disturbances and actuator faults. Load torque is first modeled as a mismatched disturban...
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This article aims to derive an adaptive optimal speed regulator for permanent magnet synchronous motors (PMSMs) affected by both disturbances and actuator faults. Load torque is first modeled as a mismatched disturbance, where its estimation via a disturbance observer is drawn to construct an error system. Then, optimal speed regulation problem for PMSM is equivalently transformed into an optimal control problem for the error system. Given the presence of model variable couplings, an adaptive dynamic programming method is adopted to derive the optimal controller, where a critic neural network (NN) and an actor NN are used to approximate the cost function and the optimal controller, respectively. Notably, this article addresses the simultaneous occurrence of disturbances and actuator faults within the optimal control framework by designing separate treatments. Eventually, a composite controller, fulfilling optimality, robustness and safety constraints, is presented with rigorous proof via Lyapunov method. The proposed method is substantiated through both comparative numerical examples and experimental validation on a PMSM platform.
Grover Search is currently one of the main quantum algorithms leading to hybrid quantum-classical methods that reduce the worst-case time complexity for some combinatorial optimization problems. Specifically, the comb...
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Grover Search is currently one of the main quantum algorithms leading to hybrid quantum-classical methods that reduce the worst-case time complexity for some combinatorial optimization problems. Specifically, the combination of Quantum Minimum Finding (obtained from Grover Search) with dynamic programming has proved particularly efficient in improving the complexity of NP-hard problems currently solved by classical dynamic programming. For these problems, the classical dynamic programming complexity in O-& lowast;(c(n)), where O-& lowast;denotes that polynomial factors are ignored, can be reduced by a hybrid algorithm to O-& lowast;(c(quant)(n)), with c(quant)
This brief introduces a novel adaptive optimal output-feedback controller for permanent magnet synchronous motor (PMSM) systems, eliminating the need for prior knowledge of system dynamics, numerous integral window fu...
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This brief introduces a novel adaptive optimal output-feedback controller for permanent magnet synchronous motor (PMSM) systems, eliminating the need for prior knowledge of system dynamics, numerous integral window functions, or unmeasurable states and load torque. Initially, we design an adaptive optimal output-feedback controller by constructing internal states. Then, a policy iteration algorithm based on adaptive dynamic programming approximates the optimal output-feedback gain using only input and trajectory tracking error information. Notably, this method does not require the minimal polynomial of an exosystem or the solution of regulator equations, facilitating the overall design of the feedforward-feedback controller. The effectiveness of the proposed learning algorithm is validated on a PMSM system.
Efficient computation of expected values is paramount in scenario analysis and decision-making, especially for problems involving large finite state machines with complex state dependencies and transitions. Traditiona...
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It is of great significance for traders to buy and sell volatile assets reasonably in market transactions. Traders will get different returns with different trading strategies. This paper studies the best trading stra...
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It is of great significance for traders to buy and sell volatile assets reasonably in market transactions. Traders will get different returns with different trading strategies. This paper studies the best trading strategies for different traders based on the daily prices of gold and bitcoin spanning a five-year period from September 11, 2016, to September 10, 2021. We propose a novel two-stage ARIMA model to forecast the prices of these assets based on historical data. The model achieves impressive correlation coefficients of 0.998 for gold and 0.999 for bitcoin, with RMSE values of 14.367 and 660.383, respectively. Based on these highly accurate predictions, we develop a trading strategy equation aimed at maximizing total assets, incorporating asset risk as a regularization term. The equation is solved with dynamic programming techniques. Furthermore, we evaluate the model's generalization ability, robustness, yield, and sensitivity to transaction costs, resulting in three representative trading strategies: Insurance-type, Common-type, and Radical-type. Our findings demonstrate that the model exhibits strong resilience to market disturbances, provides returns capable of countering inflation, and aligns well with real-world cost sensitivities. These highlight the practical value of the proposed model in real-world trading scenarios.
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