Vehicles today are in a phase between human control and full autonomy. A reliable and stable approach that shares the decisions of both human and autonomous controllers is needed. This paper introduces a shared contro...
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ISBN:
(纸本)9798350321050
Vehicles today are in a phase between human control and full autonomy. A reliable and stable approach that shares the decisions of both human and autonomous controllers is needed. This paper introduces a shared control approach to offer control signal from autonomous controller to complement commands of drivers to reduce driving risks and improve driving performance. Knowing future actions of drivers is essential in final decision making. To predict the behavior of driver, Gaussian process regression is adopted to model the driver with the data gathered in the driving process. Utilizing the drivers model and vehicles model, autonomous signal is provided by Model Predictive controller (MPC) and combined with commands of drivers in certain proportion constituting the final outputs. With the shared control algorithm, both tracking and heading errors are significantly eliminated.
This brief presents a novel reinforcement learning-based robust tracking control method for discrete-time unknown Markov jump systems. First, the optimal tracking and robust controller design problem is formulated as ...
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This brief presents a novel reinforcement learning-based robust tracking control method for discrete-time unknown Markov jump systems. First, the optimal tracking and robust controller design problem is formulated as an optimal output regulation problem. In particular, we reconstruct the stochastic coupled algebraic Riccati equation to decouple the jumping mode and approximate the optimal control policy, where the knowledge of system dynamics should be known as a priori. To solve this problem, by employing the online reinforcement learning approach, the optimal output regulator is learned within a novel data-based parallel learning framework. On this basis, the solutions of the stochastic coupled algebraic Riccati equation and the output regulation equation of Markov jump systems are obtained by using online system data. Moreover, the convergence of the proposed algorithms is analyzed. Finally, a PWM-driven DC-DC boost converter model is provided to show the effectiveness of the proposed method and the main theoretical results.
In this paper, a new model free adaptive control (MFAC) strategy based on partial least squares (PLS) framework is proposed to achieve trajectory tracking for multivariable nonlinear processes. The nonlinear dynamic c...
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ISBN:
(纸本)9798350321050
In this paper, a new model free adaptive control (MFAC) strategy based on partial least squares (PLS) framework is proposed to achieve trajectory tracking for multivariable nonlinear processes. The nonlinear dynamic characteristics of the multivariable systems are addressed by a dynamic linearization method and a linear PLS inner data model is obtained consequently including an unknown pseudo-partial derivative (PPD) parameter. Under the PLS framework, the multivariable system can be decomposed into multiple single-loop systems to facilitate the controller design. The controller design only depends on the measured input and output data. Simulation results demonstrate the effectiveness of the proposed method.
Power electronic converter (PEC)-based resources are growing ubiquitously in power systems and there is a vital necessity for precise dynamic models to comprehend their dynamics to different events and control strateg...
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Power electronic converter (PEC)-based resources are growing ubiquitously in power systems and there is a vital necessity for precise dynamic models to comprehend their dynamics to different events and control strategies. Inaccurate modeling can lead to instability, higher costs, and reliability issues. Anticipating the increase in PECs in the near future, detailed modeling becomes computationally and mathematically complex, requiring extensive computing power and knowledge of vendor-specific PECs. To overcome these challenges, data-driven machine learning/artificial intelligence (ML/AI) approaches are widely used, tracking the dynamic responses of PECs operating in various modes with limited knowledge. These models find applications in protection, stability, fault diagnosis, optimization, control and monitoring, and power quality. While the literature on power systems often emphasizes the advantages of data-driven modeling, an in-depth look at the limitations, challenges, and opportunities related to converter-dominated grids is still lacking. The purpose of this survey is to conduct a comprehensive review of ML/AI methodologies in PECs and investigate their applications in power systems. The article introduces various PEC types, their roles, and modeling approaches. It then provides an in-depth overview of how ML/AI can be applied to PECs in power systems. Finally, the survey highlights gaps in the field's knowledge and suggests potential directions for future research.
This paper proposes a novel transfer learning-based multi-objective predictive control (TLMPC) strategy to reduce the computational burden for the vehicular platoon. A data-driven model is established with subspace id...
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As complementary methods to classical model-based control, data-drivencontrol methods can avoid modeling the dynamics of complex systems but achieve good control performance, hence, data-drivencontrol methods are gr...
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Finite control Set (FCS) Model Predictive control (MPC), as an efficient method used for current tracking of LCL-Coupled three-phase inverters, runs into high computational complexity while finding its optimal version...
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ISBN:
(纸本)9798350321050
Finite control Set (FCS) Model Predictive control (MPC), as an efficient method used for current tracking of LCL-Coupled three-phase inverters, runs into high computational complexity while finding its optimal version with a long predictive interval. For such a difficult problem we take a value function with discounted factors as an indicator to measure the pros and cons of control and propose a novel alternative method based on Q-learning algorithm. In the control scheme, the value function is approximated by reinforcement learning(RL) algorithm and furthermore, the long horizons prediction is transformed into an iterative multi-step matrix calculation. At the same time, the optimal switching position is directly obtained without a modulation link, which greatly reduces the computational complexity. Accordingly, a data-driven Q-learning algorithm is designed with a proof of convergence. Last, the proposed algorithm's performance in the case of complete deviation from the (unknown) system parameters is verified by simulations.
This work proposes an event-triggered model-free adaptive sliding mode control (SMC) strategy for a class of discrete-time nonlinear networked controlsystems. Under the data-driven framework, a nonlinear dynamic line...
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This paper focuses on the adaptive funnel control of a flexible exoskeleton joint based on the singular perturbation method. The singular perturbation is used to find the asymptotic solution of a differential equation...
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ISBN:
(纸本)9798350321050
This paper focuses on the adaptive funnel control of a flexible exoskeleton joint based on the singular perturbation method. The singular perturbation is used to find the asymptotic solution of a differential equation by decomposing the system into two subsystems. For the fast subsystem, a torque-feedback-based subcontroller is proposed to ensure the suppression of flexible vibration. For the remaining slow subsystem, an improved funnel error transformation is introduced and integrated into the controller design to achieve a specified tracking error performance. Fuzzy logic systems are employed to deal with the nonlinear uncertainties, and an adaptive fuzzy funnel controller is constructed by backstepping method. The simulation results verify the feasibility of the proposed control scheme.
In this paper, we study the state consensus problem of multi-agent systems with Markov switching hierarchical network topology and inter-layer communication delay. The statistical property of Markov process and a mode...
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ISBN:
(纸本)9798350321050
In this paper, we study the state consensus problem of multi-agent systems with Markov switching hierarchical network topology and inter-layer communication delay. The statistical property of Markov process and a mode-dependent Lyapunov-Krasovskii functional is used to derive the sufficient conditions of hierarchical consensus in form of linear matrix inequalities. An illustrative example is provided to verify the effectiveness of the proposed approach.
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