Dynamic Integrated System Optimisation And Parameter Estimation Research Articles

DISOPE-based networked MPC and performance analysis for plant-wide processes

A networked MPC scheme based on dynamic integrated system optimisation and parameter estimation (DISOPE) is proposed for online optimisation and control of plant-wide processes. Under network environment, online optimisation of the whole system is decomposed into that of several small-scale subsystems, and the connectivity of communication network is assumed to be sufficient for each subsystem to exchange information with other subsystems. An iterative algorithm for DISOPE-based networked MPC is developed in spite of the model-reality differences by iterating on modified linear optimal control problems on the basis of estimating parameters at each iteration. It can be proved that the converged solution of the iterative scheme satisfies the optimality conditions of the original non-linear optimisation problem under some assumptions via computational convergence analysis. The simulation results of the fossil fuel power unit (FFPU) are illustrated to verify the effectiveness and practicability of the presented algorithm.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Application of a novel optimal control algorithm to a benchmark fed - batch fermentation process

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE) which has been designed to achieve the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A method based on Broyden's ideas is used for approximating some derivative trajectories required. Ways for handling con straints on both manipulated and state variables are described. Further, a method for coping with batch-to- batch dynamic variations in the process, which are common in practice, is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch processes. The algorithm is success fully applied to a benchmark problem consisting of the input profile optimisation of a fed-batch fermentation process.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

On Hierarchical Structures for Optimal Control of Interconnected Dynamical Systems

Hierarchical structures are developed and discussed for optimal control of interconnected dynamical systems. Four basic hierarchical structures are proposed involving iterative procedures which utilize and incorporate process measurements from interaction inputs in the model based optimal control problem. These structures, which take into account the model-reality differences, are developed by extending the Dynamic Integrated System Optimization and Parameter Estimation (DISOPE) techniques to such systems

A Hierarchical Extension of Dynamic Integrated System Optimisation and Parameter Estimation

A hierarchical algorithm for solving large-scale nonlinear optimal control problems with model-reality differences is developed. The approach used is based on the interaction-prediction approach and on an extension of the centralized Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE) technique. This technique achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure

Nonlinear Predictive Control Using Dynamic Integrated System Optimisation and Parameter Estimation

A nonlinear predictive controller based on state space models of the controlled plant is developed and implemented. Receding horizon long range prediction and dynamic optimization is carried out at every sampling instant by using an algorithm based on Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE), a novel technique for solving optimal control problems subject to model-reality differences. States and parameters are estimated from possibly noisy output measurements by using an Extended Kalman Filter. The technique has been tested on a chemical reactor simulation example and its performance has been evaluated. The controller is flexible and is able to handle input bound and state dependent constraints.

An Algorithm for Optimal Control of Nonlinear Systems with Model-reality Differences

An iterative procedure known as Dynamic Integrated System Optimization and Parameter Estimation (DISOPE) is described for solving nonlinear optimal control problems where the model employed to compute the optimal solution is different from reality. An algorithm, implemented in MATLAB on the basis of a linear model and quadratic performance, but where it is not required that the real problem satisfy these restrictions, is presented which achieves the correct real solution in spite of model-reality differences. Convergence aspects are discussed and a simulation example is described which illustrates the performance of the DISOPE algorithm.