Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role....
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ISBN:
(纸本)9798350328066
Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.
In order to control vibration-prone structures, the vibrations mitigation effect of well-known passive controlsystems such as Tuned Mass Dampers (TMDs) can be enhanced by using a mass amplification device called iner...
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In order to control vibration-prone structures, the vibrations mitigation effect of well-known passive controlsystems such as Tuned Mass Dampers (TMDs) can be enhanced by using a mass amplification device called inerter. In this study, the behavior of this device and its influence on the response of vibration-prone systems are investigated by analyzing different configurations in a civil engineering context, finding optimized parameters, and assessing the performance of various systems with the inerter in different positions. Indeed, the position of the device can strongly influence the benefits obtained from the structural control design. Therefore, a more critical analysis is proposed with respect to previous studies on the use of inerter-based devices for vibration control, with the aim of consistently investigating the exploitation of these innovative systems in practical applications.
The problem of resilient robust model predictive control (RMPC) with hard constraints based on an adaptive event-triggered mechanism for a class of polytopic uncertain systems is investigated in this article. To reduc...
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Model predictive control is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be problematic on embedded devices. Especially, w...
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Model predictive control is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be problematic on embedded devices. Especially, when we need to guarantee a fixed control frequency. Thus, previous work proposed to reduce the computational burden using imitation learning approximating the MPC policy by a neural network. In this work, we instead learn the whole planned trajectory of the MPC. We introduce a combination of a novel neural network architecture PlanNetX and a simple loss function based on the state trajectory that leverages the parameterized optimal control structure of the MPC. We validate our approach in the context of autonomous driving by learning a longitudinal planner and benchmarking it extensively in the CommonRoad simulator using synthetic scenarios and scenarios derived from real data. Our experimental results show that we can learn the open-loop MPC trajectory with high accuracy while improving the closed-loop performance of the learned control policy over other baselines like behavior cloning.
This work addresses the discrete-time simultaneous scheduling and open-loop control (SSOC) of network batch processes with variable processing times through a tailored Generalized Benders Decomposition (GBD) framework...
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ISBN:
(纸本)9798350382662;9798350382655
This work addresses the discrete-time simultaneous scheduling and open-loop control (SSOC) of network batch processes with variable processing times through a tailored Generalized Benders Decomposition (GBD) framework. This SSOC problem is a challenging mixed-integer nonlinear programming (MINLP) problem because variable processing times introduce more binary variables to a discrete-time scheduling formulation and may generate new infeasibilities if those variables are poorly selected. Variable processing times are key in SSOC since they affect both the flexibility of the schedule, and the dynamic performance of batch systems. The key novelty of the proposed GBD approach is the addition of initial and auxiliary feasibility cuts to facilitate the handling of infeasibilities generated by variable processing times. The performance of the proposed GBD framework is tested using a case study adapted from the literature. A GBD methodology that implements traditional feasibility cuts is used as a benchmark. While the conventional GBD method was unable to converge to a feasible solution, the proposed GBD framework found a feasible solution within the first two interactions and then converged by closing the absolute MINLP gap. Therefore, the proposed GBD framework is a promising strategy to solve SSOC problems involving batch processes often found in the pharmaceutical, energy, and food industries.
The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying...
ISBN:
(纸本)9798350301243
The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.
With the vigorous growth of the new generation of information technology, a new round of industrial revolution is sweeping across the world. Governments of various countries are actively introducing policies to promot...
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Distributed sensor networks often include a multitude of sensors, each measuring parts of a process state space or observing the operations of a system. Communication of measurements between the sensor nodes and estim...
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ISBN:
(纸本)9783907144084
Distributed sensor networks often include a multitude of sensors, each measuring parts of a process state space or observing the operations of a system. Communication of measurements between the sensor nodes and estimator(s) cannot realistically be considered delay-free due to communication errors and transmission latency in the channels. We propose a novel stability-based method that mitigates the influence of sensor network delays in distributed state estimation for linear time-varying systems. Our proposed algorithm efficiently selects a subset of sensors from the entire sensor nodes in the network based on the desired stability margins of the distributed Kalman filter estimates, after which, the state estimates are computed only using the measurements of the selected sensors. We provide comparisons between the estimation performance of our proposed algorithm and a greedy algorithm that exhaustively selects an optimal subset of nodes. We then apply our method to a simulative scenario for estimating the states of a linear time-varying system using a sensor network including 2000 sensor nodes. Simulation results demonstrate the performance efficiency of our algorithm and show that it closely follows the achieved performance by the optimal greedy search algorithm.
We propose a method for providing communication network infrastructure in autonomous multi-agent teams. In particular, we consider a set of communication agents that are placed alongside regular agents from the system...
ISBN:
(纸本)9798350301243
We propose a method for providing communication network infrastructure in autonomous multi-agent teams. In particular, we consider a set of communication agents that are placed alongside regular agents from the system in order to improve the rate of information transfer between the latter. In order to find the optimal positions to place such agents, we define a flexible performance function that adapts to network requirements for different systems. We provide an algorithm based on shadow prices of a related convex optimization problem in order to drive the configuration of the complete system towards a local maximum. We apply our method to three different performance functions associated with three practical scenarios in which we show both the performance of the algorithm and the flexibility it allows for optimizing different network requirements.
We formulate intrusion tolerance for a system with service replicas as a two-level game: a local game models intrusion recovery and a global game models replication control. For both games, we prove the existence of e...
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ISBN:
(纸本)9783031748349;9783031748356
We formulate intrusion tolerance for a system with service replicas as a two-level game: a local game models intrusion recovery and a global game models replication control. For both games, we prove the existence of equilibria and show that the best responses have a threshold structure, which enables efficient computation of strategies. State-of-the-art intrusion-tolerant systems can be understood as instantiations of our game with heuristic control strategies. Our analysis shows the conditions under which such heuristics can be significantly improved through game-theoretic reasoning. This reasoning allows us to derive the optimal control strategies and evaluate them against 10 types of network intrusions on a testbed. The testbed results demonstrate that our game-theoretic strategies can significantly improve service availability and reduce the operational cost of state-of-the-art intrusion-tolerant systems. In addition, our game strategies can ensure any chosen level of service availability and time-to-recovery, bridging the gap between theoretical and operational performance.
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