This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.
- Adaptive control systems,
- Backstepping,
- Cost benefit analysis,
- Cost estimating,
- Cost functions,
- Costs,
- Dynamic programming,
- Feedback control,
- Nonlinear control systems,
- Nonlinear feedback,
- Nonlinear systems,
- Adaptive back-stepping,
- Adaptive Control,
- Neural network (nn),
- Optimal controls,
- Strict feedback systems,
- Continuous time systems,
- Adaptive backstepping,
- Neural network (NN)-based dynamic programming,
- Nonlinear strict-feedback systems
Available at: http://works.bepress.com/jagannathan-sarangapani/224/