2022
DOI: 10.1002/rnc.6285
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Distributed optimal event‐triggered cooperative control for nonlinear multi‐missile guidance systems with partially unknown dynamics

Abstract: This article is concerned with the design of a novel distributed optimal event-triggered (ET) cooperative control strategy for nonlinear multi-missile guidance systems under the condition of partially unknown dynamics via adaptive dynamics programming. First, an improved online-identifier is proposed to reconstruct the unknown dynamics based on the data-driven mechanism in which an adaptive compensation term is introduced. The identification residual error is counteracted and the priori identification informat… Show more

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Cited by 11 publications
(7 citation statements)
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“…In the fields of intelligent robots and intelligent transportation, an ETM-based predictive ADP algorithm has been proposed in [92] for path planning for autonomous driving of unmanned ground vehicles at intersections; and an ADP framework has been developed in [93] to achieve ideal control of smart vehicles where a source coding scheme has been applied to vehicle communication. When the aircraft application is a concern, an observer designed to [94] and [95] has been utilized to identify the accurate model of hypersonic vehicles in the re-entry stage, and then the optimal attitude tracking problem of the hypersonic vehicle has been solved under the framework of the ADP algorithm. In addition, a distributed optimal cooperative control strategy has been applied to the simultaneous collision guidance system of multiple missiles in [96], and it was verified that multiple missiles can hit the target simultaneously.…”
Section: The Applications Of Adp Methods In Practical Systemsmentioning
confidence: 99%
“…In the fields of intelligent robots and intelligent transportation, an ETM-based predictive ADP algorithm has been proposed in [92] for path planning for autonomous driving of unmanned ground vehicles at intersections; and an ADP framework has been developed in [93] to achieve ideal control of smart vehicles where a source coding scheme has been applied to vehicle communication. When the aircraft application is a concern, an observer designed to [94] and [95] has been utilized to identify the accurate model of hypersonic vehicles in the re-entry stage, and then the optimal attitude tracking problem of the hypersonic vehicle has been solved under the framework of the ADP algorithm. In addition, a distributed optimal cooperative control strategy has been applied to the simultaneous collision guidance system of multiple missiles in [96], and it was verified that multiple missiles can hit the target simultaneously.…”
Section: The Applications Of Adp Methods In Practical Systemsmentioning
confidence: 99%
“…In this research, we further extend their results as a designable term under the optimal backstepping design structure. By this virtue, all system dynamics can be effectively compensated and adaptive laws can be integrated into the nominal controllers' design to compensate for unknown system dynamics (i.e., (42)) and unknown saturator parameters (i.e., ( 43) and ( 44)).…”
Section: Controller Designmentioning
confidence: 99%
“…Apart from this, existing ADP design methods are also inconvenient in directly handling various model uncertainties and actuator constraints. As the consequence, extra identification systems should be designed to estimate system uncertainties before designing optimal control laws, for example, References 42 and 43. Considering these facts, if we directly use the above‐mentioned philosophy to design the optimal AFM scheme for multiple USVs, the rendered controller will be complicated in both structure and parameter selection rules, which is undesirable for applications.…”
Section: Introductionmentioning
confidence: 99%
“…Although most control algorithms are based on PI, [12][13][14][15] some VI algorithms 16,17 have also been developed. In the control environment, RL algorithms have been applied in the optimal control or tracking control 18 of single-agent systems as well as the optimal coordination control 19,20 of multi-agent systems.…”
Section: Introductionmentioning
confidence: 99%