Constrained Control of Weakly Coupled Nonlinear Systems Using Neural Network
In this paper, a new algorithm is proposed for the constrained control of weakly coupled nonlinear systems. The controller design problem is solved by solving Hamilton-Jacobi-Bellman(HJB) equation with modified cost to tackle constraints on the control input and unknown coupling. In the proposed controller design framework, coupling terms have been formulated as model uncertainties. The bounded controller requires the knowledge of the upper bound of the uncertainty. In the proposed algorithm, Neural Network (NN) is used to approximate the solution of HJB equation using least squares method. Necessary theoretical and simulation results are presented to validate proposed algorithm.
KeywordsWeak coupling HJB equation Bounded control Nonlinear system Lyapunov stability
- 3.Gajic, Z., Shen, X.: Parallel Algorithms for Optimal Control of Large Scale Linear Systems. Springer, London (1992)Google Scholar
- 8.Abu-Khalaf, M., Huang, J., Lewis, F.L.: Nonlinear H2/H ∞ constrained feedback control: A practical design approach using neural networks. Springer, Heidelberg (2006)Google Scholar
- 9.Gopal, M.: Modern Control System Theory, 2nd edn. New Age International Publishers, New Delhi (1993)Google Scholar
- 12.Kim, Y.J., Lim, M.T.: Parallel Optimal Control for Weakly Coupled Nonlinear Systems Using Successive Galerkin Approximation. IEEE Transactions on Automatic Control 53(6) (2008)Google Scholar