Abstract
Communication and computation resources are normally limited in remote/networked control systems, and thus, saving either of them could substantially contribute to cost reduction and life-span increasing as well as reliability enhancement for such systems. This article investigates the event-triggered control method to save both communication and computation resources for a class of uncertain nonlinear systems in the presence of actuator failures and full-state constraints. By introducing the triggering mechanisms for actuation updating and parameter adaptation, and with the aid of the unified constraining functions, a neuroadaptive and fault-tolerant event-triggered control scheme is developed with several salient features: 1) online computation and communication resources are substantially reduced due to the utilization of unsynchronized (uncorrelated) event-triggering pace for control updating and parameter adaptation; 2) systems with and without constraints can be addressed uniformly without involving feasibility conditions on virtual controllers; and 3) the output tracking error converges to a prescribed precision region in the presence of actuation faults and state constraints. Both theoretical analysis and numerical simulation verify the benefits and efficiency of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 5076-5085 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 34 |
| Issue number | 8 |
| Early online date | 15 Nov 2021 |
| DOIs | |
| Publication status | Published - Aug 2023 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2012 IEEE.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 61860206008, Grant 61773081, Grant 61933012, and Grant 61833013; and in part by the Chongqing Technology Innovation and Application Development Special Project under Grant cstc2019jscx-fxydX0092.
Keywords
- Event-triggered control
- full-state constraints
- neuroadaptive control
- strict-feedback nonlinear system