We present a novel approximation-based event-triggered control of multiinput-multioutput uncertain nonlinear continuous-time systems in affine form. The controller is approximated by use of a linearly parameterized neural network (NN) in the context of event-based sampling. After the NN approximation property has been revisited in the context of event-based sampling, a stabilizing control scheme is introduced first and, subsequently, an optimal regulator is designed with use of NNs. A suite of novel weight update laws for tuning the NN weights at the aperiodic event-trigger or sampling instants is proposed to relax the requirement of knowledge of the complete system dynamics and reduce the computation compared with the traditional NN-based control. For analysis of the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to both derive an event-trigger or sampling condition and show local ultimate boundedness of all signals. Further, to overcome the unnecessary triggering of events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger or sampling errors to zero. Finally, the analytical design is substantiated with numerical results.
- Continuous time systems,
- Dynamic programming,
- Dynamical systems,
- Nonlinear systems,
- Uncertainty analysis,
- Adaptive dynamic programming,
- Event sampled regulation,
- Impulsive dynamical system,
- Linearly parameterized neural networks,
- Multi-input multi-output,
- Neural network control,
- Nonlinear continuous-time systems,
- Uncertain nonlinear systems,
- Adaptive control systems,
- Event sampled control,
- Event sampled regulation,
- Event-driven adaptive dynamic programming
Available at: http://works.bepress.com/jagannathan-sarangapani/160/