Dynamic Optimal Control Strategy Research Articles

Dynamic simulation and optimal control strategy for a parallel hybrid hydraulic excavator

The primary focus of this study is to investigate the control strategies of a hybrid system used in hydraulic excavators. First, the structure and evaluation target of hybrid hydraulic excavators are analyzed. Then the dynamic system model including batteries, motor and engine is built as the simulation environment to obtain control results. A so-called multi-work-point dynamic control strategy, which has both closed-loop speed PI (proportion integral) control and direct torque control, is proposed and studied in the simulation model. Simulation results indicate that the hybrid system with this strategy can meet the power demand and achieve better system stability and higher fuel efficiency.

Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models

A recurrent neural network based non-linear dynamic modelling and optimal control strategy for a batch emulsion copolymerisation reactor is proposed. To avoid the excessive effort and time associated with the development of a detailed mechanistic model, recurrent neural networks are used to build an empirical model to represent the complex polymerisation process. Since a recurrent neural network is trained to minimise its long range prediction errors, it can offer accurate long-range predictions which are required in batch process optimal control where the ultimate interest lies in the final product quality. Based on the developed neural network model, the sequential quadratic programming method was used to calculate the optimal temperature profile leading to a polymer product with a desired number average molecular weight, desired copolymer composition and the highest conversion. Simulation results demonstrate that this optimal control strategy can lead to improved production.

Decentralized optimal control of Markov chains with a common past information set

Decentralized dynamic (closed-loop) optimal control strategies are sought for a class of finite state Markov decision processes, characterized by the sharing of a common past after k steps of delay. The control is considered over a finite time horizon, and it is shown that a nonclassical dynamic programming procedure can be applied, based on the existence of a sufficient statistic of constant dimension. Finally, the infinite horizon case is briefly discussed, in view of an extention of existing results on the minimization of average expected cost for the centralized and decentralized control of Markov chains.

Flow control in integrated local area networks

The modeling and optimal flow control in integrated local area networks is investigated. A generic integrated local area network with a double bus architecture, an adaptive TDMA access protocol, and a heterogenous community of users is considered. The class of control algorithms studied regulates the data flow between a pair of communicating stations. Since the data flow in the network exhibits transient behavior, dynamic optimal flow control strategies on a finite horizon are analyzed. It is shown that an adaptive window flow control mechanism maximizes the average throughput under an average system time delay constraint. The window size dynamically adapts to changes in the integrated traffic load that belongs to the other users accessing the network. Based on this analysis, a flow control mechanism is proposed for implementation that is asymptotically optimum. Simulation results showing the dynamics o of the window process and the effectiveness of the asymptotically optimum control scheme are also presented.