Placement is a critical task with high computation complexity in VLSI physical design. Modern analytical placers formulate the placement objective as a nonlinear optimization task, which suffers a long iteration time....
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We study the problem of joint optimization of the linear dimensions of feet and the laws of motion of bipedal walking robots. A robot is modeled as a plane system of nine rigid bodies and its gait is studied on double...
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Humans can effortlessly perform very complex, dexterous manipulation tasks by reacting to sensor observations. In contrast, robots can not perform reactive manipulation and they mostly operate in open-loop while inter...
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ISBN:
(纸本)9798350323658
Humans can effortlessly perform very complex, dexterous manipulation tasks by reacting to sensor observations. In contrast, robots can not perform reactive manipulation and they mostly operate in open-loop while interacting with their environment. Consequently, the current manipulation algorithms either are inefficient in performance or can only work in highly structured environments. In this paper, we present closed-loop control of a complex manipulation task where a robot uses a tool to interact with objects. Manipulation using a tool leads to complex kinematics and contact constraints that need to be satisfied for generating feasible manipulation trajectories. We first present an open-loop controller design using Non-Linear programming (NLP) that satisfies these constraints. In order to design a closed-loop controller, we present a pose estimator of objects and tools using tactile sensors. Using our tactile estimator, we design a closed-loop controller based on Model Predictive Control (MPC). The proposed algorithm is verified using a 6 DoF manipulator on tasks using a variety of objects and tools. We verify that our closed-loop controller can successfully perform tool manipulation under several unexpected contacts.
A main challenge in swarm robotics is the unknown mapping between simple agent-level behavior rules and emergent global behaviors. Currently, there is no known swarm control algorithm that maps global behaviors to loc...
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ISBN:
(纸本)9798350328066
A main challenge in swarm robotics is the unknown mapping between simple agent-level behavior rules and emergent global behaviors. Currently, there is no known swarm control algorithm that maps global behaviors to local control policies. This paper proposes a novel method to circumvent this problem by learning the agent-level controllers of an observed swarm to imitate its emergent behavior. Agent-level controllers are treated as a set of policies that are combined to dictate the agent's change in velocity. The trajectory data of known swarms is used with linear regression and nonlinear optimization methods to learn the relative weight of each policy. To show our approach's ability for imitating swarm behavior, we apply this methodology to both simulated and physical swarms (i.e., a school of fish) exhibiting a multitude of distinct emergent behaviors. We found that our pipeline was effective at imitating the simulated behaviors using both accurate and inaccurate assumptions, being able to closely identify not only the policy gains, but also the agent's radius of communication and their maximum velocity constraint.
The hybrid mode UAV has the characteristics of a fixed-wing UAV for high-speed cruising, but also has the functions of a rotary-wing UAV for vertical take-off and landing, fixed-point hovering and low-speed maneuver, ...
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The hybrid mode UAV has the characteristics of a fixed-wing UAV for high-speed cruising, but also has the functions of a rotary-wing UAV for vertical take-off and landing, fixed-point hovering and low-speed maneuver, which can be an effective solution for the future of the small and medium-sized UAV field. The most prominent feature of the hybrid mode UAV is the existence of a transition flight phase, which can be divided into forward transition and backward transition. In this study, the trajectory optimization problem for transition flight is transformed into a nonlinear programming problem and solved by the direct collocation method. According to the given state and constraints, the optimal transition strategy is obtained by solving the forward and backward transition flight phases respectively while maintaining a constant height, with the minimum energy consumption as the objective.
The existence of strictly positive lower bounds on voltage magnitude is taken for granted in optimal power flow problems. Nevertheless, it is not possible to rely on such bounds for a variety of real-world network opt...
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ISBN:
(纸本)9781665464413
The existence of strictly positive lower bounds on voltage magnitude is taken for granted in optimal power flow problems. Nevertheless, it is not possible to rely on such bounds for a variety of real-world network optimization problems. This paper discusses a few issues related to 0 V assumptions made during the process of deriving optimization formulations in the current-voltage, power-voltage and power-lifted-voltage variable spaces. The differences between the assumptions are illustrated for a 2-bus 2-wire test case, where the feasible sets are visualized. A nonzero relaxation gap is observed for the canonical multiconductor nonlinear power-voltage formulation. A zero gap can be obtained for the branch flow model semi definite relaxation, using newly proposed valid equalities.
Planning and control for uncertain contact systems is challenging as it is not clear how to propagate uncertainty for planning. Contact-rich tasks can be modeled efficiently using complementarity constraints among oth...
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ISBN:
(纸本)9798350323658
Planning and control for uncertain contact systems is challenging as it is not clear how to propagate uncertainty for planning. Contact-rich tasks can be modeled efficiently using complementarity constraints among other techniques. In this paper, we present a stochastic optimization technique with chance constraints for systems with stochastic complementarity constraints. We use a particle filter-based approach to propagate moments for stochastic complementarity system. To circumvent the issues of open-loop chance constrained planning, we propose a contact-aware controller for covariance steering of the complementarity system. Our optimization problem is formulated as Non-Linear programming (NLP) using bilevel optimization. We present an important-particle algorithm for numerical efficiency for the underlying control problem. We verify that our contact-aware closed-loop controller is able to steer the covariance of the states under stochastic contact-rich tasks.
Peer-to-peer (P2P) networks support a wide variety of network services including elastic services such as file-sharing and downloading and inelastic services such as real-time multiparty conferencing. Each peer who ac...
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Peer-to-peer (P2P) networks support a wide variety of network services including elastic services such as file-sharing and downloading and inelastic services such as real-time multiparty conferencing. Each peer who acquires a service will receive a certain level of satisfaction if the service is provided with a certain amount of resource. The utility function is used to describe the satisfaction of a peer when acquiring a service. In this paper we consider optimal resource allocation for elastic and inelastic services and formulate a utility maximization model which is an intractable and difficult non-convex optimization problem. In order to resolve it, we apply the successive approximation method and approximate the non-convex problem to a serial of equivalent convex optimization problems. Then we develop a gradient-based resource allocation scheme to achieve the optimal solutions of the approximations. After a serial of approximations, the proposed scheme can finally converge to an optimal solution of the primal utility maximization model for resource allocation which satisfies the Karush-Kuhn-Tucker conditions.
Model Predictive Control (MPC) has emerged as a promising optimization-based controller in various industrial applications because of its nature of coping with variable bounds and multiple-input-multiple-output (MIMO)...
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Model Predictive Control (MPC) has emerged as a promising optimization-based controller in various industrial applications because of its nature of coping with variable bounds and multiple-input-multiple-output (MIMO) dynamic processes. nonlinear MPC (NMPC) is the nonlinear branch of MPC that makes use of the nonlinear model and constraints to achieve higher accuracy for systems with complicated dynamics. However, the performance of NMPC is influenced by process uncertainty and computational delays. In addition, it faces stability challenges when considering economically oriented objectives. This thesis aims to enhance the performance of NMPC by developing advanced features that improve robustness, stability, and economic efficiency while maintaining reasonable online computation by leveraging both control and optimization theory. First, we consider the well pumping period in hydraulic fracturing and propose a robust control strategy aimed at addressing the constraint violations on operating pressure and terminal requirements resulted from the uncertainty in the rock layer. A comprehensive dynamic model that captures the process is constructed and incorporated into the predictive model of the robust multistage NMPC, which uses a scenario tree to depict the evolution of states with respect to uncertain parameters. The results demonstrate the promising robustness of the controller, as it satisfies all constraints in the face of the rock uncertainty that changes in time. Next, we develop a strategy to alleviate the online computational burden associated with solving Moving Horizon Estimation (MHE) problems, which is essential for NMPC when the process information is incomplete. We propose to solve an extended horizon MHE within a specified number of delayed sampling steps. This approach uses predicted future measurements in background and nonlinear programming (NLP) sensitivity to execute online corrections once the true measurements are available. The proposed algorit
Regularization and interior point approaches offer valuable perspectives to address constrained nonlinear optimization problems in view of control applications. This paper discusses the interactions between these tech...
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ISBN:
(纸本)9781713872344
Regularization and interior point approaches offer valuable perspectives to address constrained nonlinear optimization problems in view of control applications. This paper discusses the interactions between these techniques and proposes an algorithm that synergistically combines them. Building a sequence of closely related subproblems and approximately solving each of them, this approach inherently exploits warm-starting, early termination, and the possibility to adopt subsolvers tailored to specific problem structures. Moreover, by relaxing the equality constraints with a proximal penalty, the regularized subproblems are feasible and satisfy a strong constraint qualification by construction, allowing the safe use of efficient solvers. We show how regularization benefits the underlying linear algebra and a detailed convergence analysis indicates that limit points tend to minimize constraint violation and satisfy suitable optimality conditions. Finally, numerical results indicate that the combined approach compares favorably, in terms of robustness, against both interior point and augmented Lagrangian codes. Copyright (c) 2023 The Authors.
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