Search

In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method. [ABSTRACT FROM AUTHOR]

In this technical note, a new stochastic control algorithm is presented for nonlinear and non-Gaussian continuous-time systems. Based on the recently developed generalized density evolution equation, which is more tractable than the classical Liouville equation, the relationship is established among the probability density function (PDF) of tracking error, control input and disturbances. An improved performance index is then constructed for this stochastic control strategy, which includes quadratic information potential of the tracking error, mean value of squared tracking error and constraints on the control input energy. By minimizing the performance index, a recursive optimal control algorithm is obtained using the gradient descent method. Moreover, the statistical linearization technique is adopted to formulate the boundedness condition of the closed-loop system. An illustrative example is given to demonstrate the validity and efficiency of the proposed stochastic optimal control methodology. [ABSTRACT FROM PUBLISHER]