Marine robots must maintain precise control and ensure safety during tasks like ocean monitoring, even when encountering unpredictable disturbances that affect performance. Designing algorithms for uncrewed surface ve...
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This paper studies the problem of learning-based adaptive state feedback control for networks of heterogeneous agents with input and matched uncertainties. We propose a control strategy that guarantees that the closed...
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This paper studies the problem of learning-based adaptive state feedback control for networks of heterogeneous agents with input and matched uncertainties. We propose a control strategy that guarantees that the closed-loop synchronization error of all agents in the network is bounded. adaptivecontrol laws are designed via matching conditions to approximate the dynamics of each agent, which are partially unknown. Uncertainty parameters in the model are suppressed using adaptive optimal modification theory. Moreover, a momentum-based technique is also introduced in our framework for improving the convergence behavior of the adaptivecontrol law parameters computation. Finally, a simulation example for Cooperative adaptive Cruise control (CCAC) is presented to validate the effectiveness of the control scheme. Numerical examples show that momentum-based techniques reach the bounded error set faster than gradient-based techniques.
A unified disturbance rejection problem based on model reference adaptive control (MRAC) technique is addressed. When the full-state vector is not operational in feedback, we use partial-state signal y(t) to design a ...
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A unified disturbance rejection problem based on model reference adaptive control (MRAC) technique is addressed. When the full-state vector is not operational in feedback, we use partial-state signal y(t) to design a disturbance rejection controller. The parametrization and disturbance compensation techniques are necessary for designing adaptivecontrol strategy to deal with systems uncertainties and unknown disturbances. This scheme guarantees the desired system performances, including closed-loop system stability as well as asymptotic output tracking. The proof progress of system stability is elaborated.
With the speed of high-speed maglev increasing, external disturbances will be further aggravated, leading to a deterioration in the stability and dynamic performance of the electromagnetic levitation (EML) system. Imp...
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A novel least-squares model-reference direct adaptivecontrol (LS-MRAC) algorithm for multivariable (MIMO) plants is presented. The controller parameters are directly updated based on the output tracking error. The co...
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This paper proposes a useful approach to Fault Tolerant control (FTC) based on the model reference adaptive control (MRAC) technique with application to a quadrotor helicopter Unmanned Aerial Vehicle (UAV) in hovering...
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ISBN:
(纸本)9780791854792
This paper proposes a useful approach to Fault Tolerant control (FTC) based on the model reference adaptive control (MRAC) technique with application to a quadrotor helicopter Unmanned Aerial Vehicle (UAV) in hovering as well as trajectory tracking flight in order to control and keep the desired height and trajectory of the quadrotor helicopter in both normal conditions and in the presence of faults in one or more actuators. A Linear Quadratic Regulator (LQR) controller is used in cooperation with the MRAC to control the pitch and roll attitude of the helicopter. Three cases of fault are considered: 1) simulated fault in all the four actuators;2) simulated fault in back and right motors;3) a physical damage of 23% of one of the four propellers during autonomous flight. It can be seen from the test results that under the faulty and damage conditions MRAC controller provided a good response of the quadrotor UAV and result in safe landings of the quadrotor.
This paper applies a deep neural network to rotor flux-based field-oriented control of an induction motor drive for electric vehicles. The proposed technique uses a deep neural network in a modelreferenceadaptive sy...
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This paper presents a direct model reference adaptive controller for multi-input multi-output linear systems with known nonminimum-phase zeros. This controller uses a surrogate tracking error, which approximates the t...
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ISBN:
(纸本)9781457710964;9781457710957
This paper presents a direct model reference adaptive controller for multi-input multi-output linear systems with known nonminimum-phase zeros. This controller uses a surrogate tracking error, which approximates the true tracking error. The recursive-least-squares-based adaptive law is developed by minimizing an integral quadratic cost of the surrogate tracking error.
Online learning and model reference adaptive control have many interesting intersections. One area where they differ however is in how the algorithms are analyzed and what objective or metric is used to discriminate &...
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Induction motors' longevity, ease of use, and relative affordability have contributed to their widespread adoption across various industries and application domains for their low cost and high efficiency. model Re...
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