In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider t...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the proposed method handles all uncertainties stochastically by employing an online adaptive sampling-based approximation of chance constraints. The approach requires initial data in the form of a short input-output trajectory and distributional knowledge of the disturbances. This prior knowledge is used to construct an initial set of dataconsistent system parameters and a distribution that allows for sample generation. As new data stream in online, the set of consistent system parameters is adapted by exploiting set membership identification. Consequently, chance constraints are deterministically approximated using a probabilistic scaling approach by sampling from the set of system parameters. In combination with a robust constraint on the first predicted step, recursive feasibility of the proposed predictive controller and closed-loop constraint satisfaction are guaranteed. A numerical example demonstrates the efficacy of the proposed method.
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distributi...
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This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy mo...
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High performance collaborative tracking problem, requiring a group of independent subsystems to generate a global output that can precisely track the desired reference in a repetitive manner, has found lots of applica...
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ISBN:
(数字)9798350374261
ISBN:
(纸本)9798350374278
High performance collaborative tracking problem, requiring a group of independent subsystems to generate a global output that can precisely track the desired reference in a repetitive manner, has found lots of applications in practice. However, for such an important control task, existing iterative learning control (ILC) methods have not considered the constraint on each subsystem's output, which leads to potential risk within the control process. This paper proposes a novel optimisation based ILC method to address the high performance collaborative tracking problem with output constraints. The proposed ILC framework can guarantee not only each subsystem's output constraint is always satisfied during the control process, but also the monotonic convergence of a well-defined performance index to a possibly minimum value. To avoid huge computational complexity for large scale systems, we further apply the idea of the alternative direction method of multipliers (ADMM) to implement the proposed ILC frame-work in a decentralised manner, which allows the resulting decentralised methods to be applied to large scale and changing systems. Moreover, the decentralised ILC method proposed in this paper is suitable for non-minimum phase, heterogeneous and/or homogeneous systems, which is appealing in practice. Convergence properties of the proposed ILC algorithms are analysed rigorously, and numerical examples are given to demonstrate the algorithms' effectiveness.
Onboard perception systems found on modern vehicles generate data that are incredibly rich in contextual information, and thanks to the increasing number of vehicles equipped with communication capabilities, the valua...
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This review studies recent developments towards the physical design and control of self-assembling multi-robot systems. A wide range of novel robotic systems have been developed lately, for potential applications in t...
This review studies recent developments towards the physical design and control of self-assembling multi-robot systems. A wide range of novel robotic systems have been developed lately, for potential applications in terrestrial, aquatic, and aerospace environments. They increasingly make use of connectors which enable modules to join with each other at arbitrary points instead of discrete locations. Although the majority of contemporary algorithms are shape-driven, an increased focus on task-driven algorithms is observed. Self-assembling multi-robot systems allow the same set of robots to adopt specific morphologies for different tasks. The requirements for robots to be able to connect to each other, locomote, and communicate have led to a wide range of physical designs realising different trade-offs. While algorithms are validated extensively in simulation, only a small portion are yet tested on real robotic platforms. Future research should investigate the real-world application of these systems, possibly aided by the introduction of standardised and open hardware.
This paper aims at developing new techniques that study vital functions of the heart by analysing biological signals. ECG prediction is important for many current medical applications. Currently, there are many machin...
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The analog electronic computers are a type of circuitry used to calculate specific problems using the physical relationships between the voltages and currents following classical laws of physics. One specific class of...
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This work proposes a novel solution to the problem of covering a bounded grid world using a swarm of robotic agents. The controller requires no run-time memory and only few, discrete sensory inputs. Two variants of th...
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Simultaneous localisation and mapping (SLAM) relies on low-cost on-board sensors such as cameras and inertial measurement units. It is crucial that the surroundings are visible to the cameras to maximise the accuracy ...
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