Article
Automation & Control Systems
Keyan Miao, Richard Vinter
Summary: This article discusses an optimal control problem in neo-classical macroeconomics, aiming to maximize expenditure on social programs by balancing investment for growth and consumption. A nonstandard verification technique is introduced and applied to handle singularities caused by fractional singularities, providing a detailed solution and analysis of the problem.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2021)
Article
Mathematics, Applied
Yu Yuan, Zhibin Liang, Xia Han
Summary: This paper studies a robust optimal reinsurance problem for an ambiguity-averse insurer and derives the closed-form expressions of the optimal reinsurance strategy and the associated value function. It also investigates the influence of model ambiguity and provides numerical examples to illustrate the impact of model parameters on the optimal results.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Mathematics, Applied
Karl Kunisch, Donato Vasquez-Varas
Summary: This article analyzes a learning technique for finite horizon optimal control problems and its approximation based on polynomials, and illustrates the practicality and efficiency of the method.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2023)
Article
Operations Research & Management Science
Hidekazu Yoshioka, Motoh Tsujimura
Summary: This paper investigates the countermeasure of sand replenishment against sediment depletion in rivers and addresses the critical issue of cost-efficient sizing of its capacity. A robust ergodic control approach is proposed to tackle the design problem. By utilizing stochastic differential equations and dynamic risk measures, the optimal storage capacity is determined, and the existence and uniqueness of analytical solutions are proven.
Article
Automation & Control Systems
Javier de Frutos, Julia Novo
Summary: This paper provides error bounds for fully discrete approximations of infinite horizon problems using the dynamic programming approach. The paper revises the error bound of the fully discrete method and proves that, under assumptions similar to the time discrete case, the error of the fully discrete case is O(h+k), giving first order accuracy in time and space for the method. This error bound matches numerical experiments in the literature where the behavior predicted by the O(k/h) bound has not been observed.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2023)
Article
Automation & Control Systems
Qing Guo
Summary: An optimal robust controller is proposed in this study to address the issues of hydraulic parametric uncertainty and unknown external loads in the electro-hydraulic system (EHS). The controller constructs a Hamilton-Jacobi-Bellman (HJB) equation based on the tracking error model of EHS to optimize the performance cost function and derive the optimal control variable. A critical neural network is used to estimate the optimal control solution of the HJB equation, ensuring the uniform ultimate boundary of the system state error of EHS. The effectiveness of the proposed controller is verified through comparative simulation and experimental results.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Business, Finance
Yong He, Xia Zhou, Peimin Chen, Xiaoyang Wang
Summary: This paper investigates the investment strategies of different investors in the investment-reinsurance problem and provides an analytical solution method. The method decomposes the nonlinear equation into linear equations using the homotopy analysis method, allowing for analytical solutions. The research results demonstrate that different risk reference investors have different investment-reinsurance strategies.
NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE
(2022)
Article
Automation & Control Systems
Jeongho Kim, Insoon Yang
Summary: Maximum entropy reinforcement learning methods have been successfully applied to a range of challenging sequential decision-making and control tasks. However, there is a need to extend these methods to continuous-time systems. This article studies the theory of maximum entropy optimal control in continuous time and derives a novel class of equations. The results demonstrate the performance of the maximum entropy method in continuous-time optimal control and reinforcement learning problems.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Yumiharu Nakano
Summary: This study tackles the inverse problem of stochastic optimal control for general diffusions, where the performance index includes a quadratic penalty term for the control process. By utilizing a stochastic maximum principle under mild conditions, the inverse problem is shown to be well-posed. The authors propose a numerical method based on root finding and the kernel-based collocation method to solve the inverse problem, even in multi-dimensional cases and without explicit knowledge of the value function. Numerical experiments validate the accuracy of the method in recovering the unknown penalty parameter.
Article
Automation & Control Systems
Behzad Azmi, Dante Kalise, Karl Kunisch
Summary: A sparse regression approach is proposed for computing high-dimensional optimal feedback laws in deterministic nonlinear control, utilizing the link between Hamilton-Jacobi-Bellman PDEs and first-order optimality conditions. It is shown that enriching the dataset with gradient information reduces training sample size and leads to a feedback law of lower complexity.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Chemistry, Analytical
Juan Parras, Patricia A. Apellaniz, Santiago Zazo
Summary: This study utilizes deep learning and optimal control tools to address underwater motion planning problems, taking into account disturbances in the underwater medium, and proposes an effective solution using the Deep Galerkin Method.
Article
Mathematics, Applied
Linlin Tian, Zhaoyang Liu
Summary: This paper investigates the optimal dividend problem for the renewal risk model with phase-type distributed interclaim times and exponentially distributed claim sizes. By proposing an algorithm and analyzing the theoretical properties, the optimal strategy is found, and the optimality of phasewise barrier strategy as well as the convergence of the algorithm is proved.
APPLIED MATHEMATICS AND OPTIMIZATION
(2022)
Article
Automation & Control Systems
Karl Kunisch, Donato Vasquez-Varas, Daniel Walter
Summary: A learning-based method is proposed to obtain feedback laws for nonlinear optimal control problems. The method approximates the infinite dimensional problem using a polynomial ansatz and employs a penalty term combined with the proximal point method to find sparse solutions. The proposed methodology provides a promising approach for mitigating the curse of dimensionality.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Mathematics, Interdisciplinary Applications
Jingzhen Liu, Yike Wang, Ning Zhang
Summary: In this paper, the authors analyze the optimal reinsurance and dividend problem with model uncertainty for an insurer. They propose a goal of finding the optimal strategy that maximizes the expected discounted dividend before ruin in the worst case of all possible scenarios. The authors use a dynamic programming approach to solve the problem, reducing it to a Hamilton-Jacob-Bellman-Isaac (HJBI) equation with singular control. They prove that the value function is the unique solution to this equation and derive closed-form solutions when the function in the objective function is specified.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2023)
Article
Acoustics
Zahra Nikooeinejad, Mohammad Heydari
Summary: The collocation method is a powerful and effective technique for solving nonlinear differential equations. This paper presents a collocation method based on the shifted fractional-order Legendre functions and applies it to solve linear and nonlinear Hamilton-Jacobi-Bellman partial differential equations in stochastic optimal control problems.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Automation & Control Systems
Sayan Basu Roy, Shubhendu Bhasin, Indra Narayan Kar
ASIAN JOURNAL OF CONTROL
(2020)
Article
Engineering, Aerospace
Rajasree Sarkar, Joyjit Mukherjee, Deepak Patil, Indra Narayan Kar
Summary: A Reusable Launch Vehicle (RLV) faces uncertain environment and extreme turbulence during the re-entry phase, requiring an effective control strategy for safe landing. A Time-Delayed Control (TDC) strategy has been proposed to track the space vehicle trajectory in the presence of uncertainties, with theoretical analysis confirming stability and simulation studies validating robust tracking of the optimal trajectory.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Automation & Control Systems
Arunava Banerjee, Joyjit Mukherjee, Mashuq un Nabi, Indra Narayan Kar
Summary: This paper proposes an efficient guidance strategy for a two-dimensional interceptor problem, generating an near optimal trajectory using Differential Evolution and guiding the missile to intercept the target through robust control law.
Article
Energy & Fuels
Ganesh P. Prajapat, N. Senroy, I. N. Kar
Summary: An enhanced control strategy is proposed for the DFIG-based variable speed wind turbine system to maximize energy extraction from variable wind. The strategy modifies the reference torque to improve MPPT, utilizing estimated wind speed and unobservable states. The proposed approach acts during transient time, requires no alterations to existing control systems, and can be implemented in real-life applications without much additional cost.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Automation & Control Systems
Joyjit Mukherjee, Spandan Roy, Indra Narayan Kar, Sudipto Mukherjee
Summary: This article introduces an adaptive-robust maneuvering control framework for a planar snake robot to address parameter uncertainties. The control objective is to maintain consistent motion of the snake robot's body shape while tracking velocity and head angle simultaneously. The proposed dual adaptive-robust time-delayed control (ARTDC) demonstrates improved performance compared to existing methodologies in simulation studies.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Madan Mohan Rayguru, Bhabani Shankar Dey, Indra Narayan Kar
Summary: Contractive nonlinear systems have attracted attention from the control community due to their desirable properties. An extended HGO based output feedback strategy is proposed in this work, which ensures contraction of a class of uncertain singularly perturbed systems. The analysis shows that the smallness requirement of the singular perturbation parameter can be relaxed for certain classes of nonlinear systems, offering additional freedom to tune the closed loop performances.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Madan Mohan Rayguru, Spandan Roy, Indra Narayan Kar
Summary: This article presents a saturated tracking controller based on a different theoretical framework, ensuring bounded tracking performance and quantifying steady-state error bounds in multi-input-multi-output nonlinear systems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Priyam Parikh, Reena Trivedi, Jatin Dave, Keyur Joshi, Dipak Adhyaru
Summary: In this paper, an indigenous 3D printed 6 DoF robotic arm is proposed to support specially-abled people in their independent-feeding process. The objective is to find the combination of optimal positional controllers that can handle the reference input signal and reduce positional error. The technical challenge lies in synchronizing machine vision, robot kinematics and trajectory planning with robot control for multiple intermediate points.
IETE JOURNAL OF RESEARCH
(2023)
Article
Automation & Control Systems
Rajasree Sarkar, Deepak Patil, Indra Narayan Kar
Summary: This study proposes an alternative strategy for solving the time-L-1 optimal control problem by intermittently applying open-loop optimal solution. The strategy retains system states within a small bounded safe region and achieves practical stability with reduced usage of system resources.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Article
Automation & Control Systems
Rajasree Sarkar, Deepak Patil, Ameer K. Mulla, Indra Narayan Kar
Summary: The computation of a decentralized feedback strategy for the consensus tracking problem of multi-agent systems is considered in this study. The agents can communicate over a directed graph and track the reference trajectory generated by the root node in finite time.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Proceedings Paper
Automation & Control Systems
Niladri Sekhar Tripathy, Indra Narayan Kar, Mohammadreza Chamanbaz, Roland Bouffanais
IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
(2020)
Proceedings Paper
Automation & Control Systems
Rajasree Sarkar, Joyjit Mukherjee, Deepak Patil, Indra Narayan Kar
2020 28TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED)
(2020)
Proceedings Paper
Automation & Control Systems
Shyam Krishan Joshi, Shaunak Sen, Indra Narayan Kar
Proceedings Paper
Automation & Control Systems
Sujeet Kumar, I. N. Kar
Article
Computer Science, Information Systems
Niladri Sekhar Tripathy, Indra Narayan Kar, Mohammadreza Chamanbaz, Roland Bouffanais
