Robust output-feedback with active disturbance rejection control (ADRC) proved to be a very effective control paradigm for highly uncertain perturbed systems. The main practical limitation of the ADRC results from an ...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
Robust output-feedback with active disturbance rejection control (ADRC) proved to be a very effective control paradigm for highly uncertain perturbed systems. The main practical limitation of the ADRC results from an application of a conventional high-gain observer which may cause significant amplification of a high-frequency noise corrupting the output measurements. We show that this main limitation can be significantly reduced by augmenting a conventional ADRC system with an auxiliary on-line estimator of the closed-loop tracking error dynamics which contains key residual information, useful for improving a resultant tracking control performance in the augmented ADRC system. It is shown that the tracking control improvement, resulting from an application of the proposed augmentation mechanism, prevents an excessive amplification of feedback noises and keeps a control cost on an acceptable level, especially for systems with higher-order dynamics. The proposed concept has been illustrated by selected results of extensive numerical simulations.
This paper presents a solution to the set-point control problem for nonholonomic mobile robots in the presence of time and control input constraints. We consider the kinematics of a unicycle mobile robot, in which the...
This paper presents a solution to the set-point control problem for nonholonomic mobile robots in the presence of time and control input constraints. We consider the kinematics of a unicycle mobile robot, in which the constraints on the control inputs are longitudinal and angular velocity limitations, while the time constraints impose an upper bound on a settling time for stabilization errors. We show a solution based on the Vector-Field-Orientation (VFO) methodology, which is characterized by non-oscillatory transient states and well-predictable time evolution of these states. Formally derived upper bounds of settling time for configuration errors are verified by results of numerical simulations and experimental results obtained in a fast prototyping system.
In this work, we present the problem of simultaneous input-output feedback linearization and decoupling (non-interacting) for mechanical control systems with outputs. We show that the natural requirement of preserving...
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The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** ...
The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations.
This work studies the issue of remotely controlling a single room’s temperature using a central heating system. The models of the components of the heating system setup are derived by equivalent electric circuits. Th...
This work studies the issue of remotely controlling a single room’s temperature using a central heating system. The models of the components of the heating system setup are derived by equivalent electric circuits. The combined nonlinear description of the process is used to produce a linear approximant. A Proportional, Integral plus Derivative (PID) controller, located at a considerable distance from the process and receiving measurements and transmitting actuation signals via a wireless network, is designed based on the linear approximant and towards room temperature control. A metaheuristic approach, satisfying stability, approximate model matching, asymptotic command following, and disturbance attenuation, is used to calculate the controller parameters. The suggested scheme’s performance is illustrated through simulations.
The use of unmanned aerial vehicles (UAVs) for smart agriculture is becoming increasingly popular. This is evidenced by recent scientific works, as well as the various competitions organised on this topic. Therefore, ...
In this paper, we propose a real-time FPGA implementation of the Semi-Global Matching (SGM) stereo vision algorithm. The designed module supports a 4K/Ultra HD (3840×2160 pixels @ 30 frames per second) video stre...
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This paper deals with the decentralized predefined-time leaderless consensus formation control problem for nonholo-nomic multi -agent systems. First, the predefined-time average consensus problem is investigated. Dist...
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ISBN:
(数字)9798350393965
ISBN:
(纸本)9798350393972
This paper deals with the decentralized predefined-time leaderless consensus formation control problem for nonholo-nomic multi -agent systems. First, the predefined-time average consensus problem is investigated. Distributed observers, which interact with each other via an undirected communication topology, are designed to estimate, in a predefined-time, the average of their initial state using only local information. Then, based on these estimates, a predefined-time Vector-Field-Orientation (VFO) controller is applied, which guarantees that the multi-agent system achieves a desired formation. The VFO controller enables one to predict the paths for each agent and to avoid static obstacles. Some numerical results illustrate the interest of the proposed predefined-time leaderless consensus formation controller.
We propose a novel layer-wise parameterization for convolutional neural networks (CNNs) that includes built-in robustness guarantees by enforcing a prescribed Lipschitz bound. Each layer in our parameterization is des...
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The paper is focused on the selected area of artificial intelligence (AI) applications, and presents only two examples relating to robotics and robotic systems. It is particularly interested in swarm intelligence meth...
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The paper is focused on the selected area of artificial intelligence (AI) applications, and presents only two examples relating to robotics and robotic systems. It is particularly interested in swarm intelligence method applied to path planning problems, and machine and deep learning approaches to anomaly detection. Based on existing methods of AI, we discuss important aspects for the application of AI technologies to view some research problems from robotics perspective.
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