In this work, an observer-based sliding mode control strategy is proposed for a discrete-time nonlinear multiagent systems (MASs) with unknown disturbance. Only some agents are capable of acquiring the reference traje...
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ISBN:
(纸本)9798350321050
In this work, an observer-based sliding mode control strategy is proposed for a discrete-time nonlinear multiagent systems (MASs) with unknown disturbance. Only some agents are capable of acquiring the reference trajectory, and the dynamic models of the agents are unknown. Unlike the traditional model-based consensus control protocol, this method is data-driven and solely dependent on the input/output (I/O) data of the agents. The stability of the proposed control strategy is ensured by theoretical analysis and the simulation outcomes ultimately validate the viability of the developed approach.
A new periodic SMC algorithm is constructed for 4-DOF tower crane systems under unknown control direction constraints. It should be pointed out that compared with existing control direction-related control algorithms,...
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ISBN:
(纸本)9798350321050
A new periodic SMC algorithm is constructed for 4-DOF tower crane systems under unknown control direction constraints. It should be pointed out that compared with existing control direction-related control algorithms, the designed control coefficient is allowed to cross 0 continuously. The stability of the controlled system is illustrated utilizing the Lyapunov techniques. Several simulations are evaluated to prove the satisfactory control performance of the proposed periodic SMC method.
In this paper, a new idea of in-distribution stability is analyzed for a class of neural Markovian jump systems with non-differential time-delays and Ito type disturbance. To achieve such a goal, an asynchronous state...
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ISBN:
(纸本)9798350321050
In this paper, a new idea of in-distribution stability is analyzed for a class of neural Markovian jump systems with non-differential time-delays and Ito type disturbance. To achieve such a goal, an asynchronous state-feedback controller is proposed to facilitate the design. Consider the fact that there always exist delays during practical signal transmissions, and therefore a new asynchronous delay-feedback control is reconstructed to render the closed-loop system to satisfy two preconditions. Whereupon, the closed-loop system is proved to be in-distribution stable through three steps. Note that the boundary of the designed controller does not need to be known in advance, which shows superiority over the existing delay-involved controllers.
In this paper, the problem of perfect group consensus tracking is discussed for first-order discrete-time multi-agent systems with linear and nonlinear dynamic under directed communication topology. First of all, for ...
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ISBN:
(纸本)9798350321050
In this paper, the problem of perfect group consensus tracking is discussed for first-order discrete-time multi-agent systems with linear and nonlinear dynamic under directed communication topology. First of all, for first-order discrete-time linear and nonlinear multi-agent systems with two subgroups, distributed control protocols are constructed using iterative learningcontrol method, based on matrix theory and compression mapping principle, sufficient conditions are derived to achieve perfect group consensus tracking under the proposed control protocol. The novel initial state learning laws are proposed, which can make each follower agent with arbitrary initial state and track the corresponding leader as iteration number approaches infinity. Secondly, we extend the corresponding results to the case of multiple subgroups. Then the formation control with multiple subgroups is also considered. Finally, three numerical examples are given to demonstrate the validity of the results.
In this article, a data-driven multiple-input-multiple-output (MIMO) feedforward control approach is synthesized to enhance the tracking performance of precision MIMO motion systems. Specifically, a MIMO feedforward c...
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In this article, a data-driven multiple-input-multiple-output (MIMO) feedforward control approach is synthesized to enhance the tracking performance of precision MIMO motion systems. Specifically, a MIMO feedforward controller parameterized with polynomial basis functions is employed to address the coupling of MIMO systems. A new data-driven feedforward tuning algorithm for the MIMO feedforward controller is then developed based on the measured step response of the process sensitivity function. The proposed approach requires only one tracking experiment in each iteration, resulting in an experimentally efficient feedforward parameter optimization w.r.t. a user-defined and tracking-performance-related criterion through iterative learning from the measured data. Finally, application to an industrial three degrees-of-freedom motion stage illustrates that the proposed approach outperforms a data-driven single-input-single-output (SISO) feedforward control scheme in terms of tracking performance and achieves good performance robustness against the reference variation.
The asynchronous data-drivencontrol problem is concerned for discrete-time switched systems with average dwell time. For a class of unknown switched systems, a data-driven approach is employed to parameterize the con...
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In this paper, a robust data-driven model predictive control (MPC) via on-policy reinforcement learning (RL) is presented for the regulation of constrained robot manipulators subject to both model mismatch and bounded...
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ISBN:
(纸本)9798350363029;9798350363012
In this paper, a robust data-driven model predictive control (MPC) via on-policy reinforcement learning (RL) is presented for the regulation of constrained robot manipulators subject to both model mismatch and bounded additive disturbances. Based on the Euler-Lagrangian model of the robot manipulator, the model mismatch is characterized by the difference between the system matrix describing the Coriolis and centrifugal torques and its initial approximation. The additive disturbances considered in this work affect the torques applied to the joints. To reduce the complexity of the prediction model adopted by MPC and thus the computational load of solving the online control problem in MPC, the inverse dynamics controller is incorporated into the control framework. The rigid tube-based MPC with adaptive design of the terminal weighting matrix and terminal set is presented to ensure robust constraint satisfaction. The use of the inverse dynamics control policy leads to the selection of the on-policy RL algorithm: Sarsa for designing the policy to update key parameters of the MPC optimization problem. The efficacy of the proposed control framework is validated by a case study using a robot manipulator with two revolute joints in simulation.
This paper is concerned with distributed state estimation problem over sensor networks with uncertainty in communication networks. Because of the instability of communication in real systems, it is meaningful to consi...
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ISBN:
(纸本)9798350321050
This paper is concerned with distributed state estimation problem over sensor networks with uncertainty in communication networks. Because of the instability of communication in real systems, it is meaningful to consider packet loss and topology change. Thus, based on Kalman consensus filtering algorithm and data-driven filtering technique, we proposed a modified data-driven Distributed information-weighted Kalman Consensus Filter to estimate the state. Finally, the effectiveness of the designed algorithm is validated by a simulation example.
In this paper, we study the problem of data-driven fault-tolerant formation control for linear multiagent systems with actuator faults in a directed communication switching topology. Initially, constructing a distribu...
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This letter introduces an innovative data-driven integral reinforcement learning (IRL) algorithm for the control of a class of underactuated mechanical systems. We propose a novel value function that allows shaping an...
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This letter introduces an innovative data-driven integral reinforcement learning (IRL) algorithm for the control of a class of underactuated mechanical systems. We propose a novel value function that allows shaping and learning the potential energy of an underactuated system and to drive it to a desired closed-loop potential energy. Consequently, we derive an actor-control policy that ensures asymptotic stability. In addition, we propose to parameterize the value function with a multi-layered perceptron (with 0, 1, and 2 hidden layers), exploring various parameter configurations. Eventually, we assess the performance of the proposed IRL through simulations and experimental results, thus confirming the practical effectiveness of the control design approach.
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