Piezoelectric and pyroelectric effects are well known and have been widely used in the field of energy harvesting, actuators or sensors. This paper presents a cantilever structure based on a lead free piezoelectric-py...
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The emission of VOCs (Volatile Organic Compounds, VOCs) during packaging and printing production endangers human health and causes serious pollution to the environment. The regenerative thermal oxidizer is a widely us...
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This study addresses the robust output containment control problem of heterogeneous uncertain multi-agent systems under Markovian switching topologies. A novel distributed output feedback controller is proposed. Based...
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ISBN:
(数字)9798350373691
ISBN:
(纸本)9798350373707
This study addresses the robust output containment control problem of heterogeneous uncertain multi-agent systems under Markovian switching topologies. A novel distributed output feedback controller is proposed. Based on the internal model approach, the main result shows that the output of each follower agent converges to the convex hull formed by the outputs of the leader agents under the proposed distributed controller. An illustrative example is given to validate the effectiveness of the proposed controller.
This paper addresses the challenge of achieving private and resilient average consensus among a group of discrete-time networked agents without compromising accuracy. State-of-the-art solutions to attain privacy and r...
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The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this pape...
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The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due t...
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Adaptive dynamic programming(ADP) is a kind of intelligent control method,and it is a non-model-based method that can directly approximate the optimal control policy via online *** gradient algorithm is usually used t...
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Adaptive dynamic programming(ADP) is a kind of intelligent control method,and it is a non-model-based method that can directly approximate the optimal control policy via online *** gradient algorithm is usually used to update weights of action networks and critic networks,however it is clear that gradient descent-based learning methods are generally very slow due to improper learning steps or may easily converge to local *** this paper,in order to overcome those disadvantages of gradient descent-based learning methods,a novel ADP algorithm based on initial-training-free online extreme learning machine(ITF-OELM),in which the critic network link weights of hidden nodes to output nodes can be obtained by least squares instead of gradient algorithm,is ***,the ADP algorithm based on ITF-OELM is tested on a discrete time torsional pendulum system,and simulation results indicate that this algorithm makes the system converge in a shorter time compared with the ADP based on gradient algorithm.
Model Predictive control (MPC) is used for more and more applications in an industrial context. The applications are characterized by increasing complexity while the available computation time is getting smaller and s...
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Model Predictive control (MPC) is used for more and more applications in an industrial context. The applications are characterized by increasing complexity while the available computation time is getting smaller and smaller. MPC is the most important advanced control technique with even increasing importance. Hence, this topic should be covered in control lectures during the academic studies in order to prepare students for their future work. For the successful implementation of MPC algorithms, knowledge from multiple disciplines is crucial and needs to be taught. Besides teaching knowledge in classical control theory, especially fundamentals in the fields of modeling, simulation and numerical optimization are required for understanding MPC. Programming skills are inevitable to apply the concept in real-world applications. This paper presents a concept for teaching MPC from the theory to the application to real-world systems. Details about the lectures covering the relevant topics are given. In the hands-on exercises, students implement their own linear as well as nonlinear MPC in MATLAB/Simulink. As example application in the exercises, the air path of a turbocharged diesel engine with high pressure exhaust gas recirculation is investigated. At the end of the semester, students can test their developed controllers on a real diesel engine test bench and compete against each other for the best control performance.
Modeling of cross-medium vehicles with complex shapes still requires a thorough *** paper proposes a multi-method combination modeling approach to tackle such a ***-principle model is derived to determinate a model **...
Modeling of cross-medium vehicles with complex shapes still requires a thorough *** paper proposes a multi-method combination modeling approach to tackle such a ***-principle model is derived to determinate a model *** are then set up to estimate parameters related to its rigid-body model and propulsion *** fluid dynamics(CFD) is performed to calculate and identify coefficients related to surrounding *** on the model obtained,we systematically investigate the possible steady motion of the cross-medium vehicle and analyze their related *** are instrumental for designing controllers for the vehicle to perform autonoumous missions.
This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) an...
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ISBN:
(数字)9798350379228
ISBN:
(纸本)9798350390780
This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.
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