A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.

Wireless sensor network refers to a group of sensors, linked by a wireless medium to perform distributed sensing task. The primary interest is their capability in monitoring the physical environment through the deployment of numerous tiny, intelligent, wireless networked sensor nodes. Our interest consists of a sensor network, which includes a few specialized nodes called processing elements that can perform some limited computational capabilities. In this paper, we propose a model called SPLAI that allows the network to compute a finite element problem where the processing elements are modeled as the nodes in the linear triangular approximation problem. Our model also considers the case of some failures of the sensors. A simulation model to visualize this network has been developed using C++ on the Windows environment.

No abstract

In the presence of random disturbances, control and optimization problems of the nonlinear discrete-time stochastic dynamic systems are more difficult to solve rather than the linear stochastic optimal control problem. This is due to the nonlinear structure of plant and the partially known state information. In this paper, we discuss the approach of modelreality differences to solve the nonlinear discrete-time stochastic optimal control problem. We modify the Dynamic Integrated System Optimization and Parameter Estimation (DISOPE) algorithm, which developed by Roberts and Becerra, with applying the Kalman filtering for state estimation and choosing the linear quadratic Gaussian as the model-based optimal control problem. The algorithm integrates the problems of system optimization and parameter estimation. The different structures and parameters among the real plant and the model employed are taken into account in the computations. The iterative procedure required for solving the model-based optimal control problem where the value of setpoint are updated. This will gives the optimum of the real plant in spite of model-reality differences when the convergence achieved. For illustration, the solution of a single degree of freedom inverted pendulum with multiplicative white noise is investigated. The computed solution of model used satisfies the necessary optimality conditions. Hence, the efficient of the algorithm is presented.