This paper describes an optimization problem to minimize the cost of power consumption for the electrochemical process of zinc (EPZ) depending on varying prices of electrical power. A series of conditional experiments was conducted to obtain enough data, which reflect the complex relationships among the factors influencing power consumption. Two backpropagation neural networks are used to build a process model that describes these relationships. An equivalent Hopfield neural network is constructed to solve this nonlinear optimization problem with technological constraints, a penalty function is introduced into the network energy function to meet the equality constraints, and inequality constraints are removed by altering the sigmoid function. An optimal power-dispatching control system (OPDCS) has been developed to provide an optimal power-dispatching scheme and keep the EPZ running economically. Since the OPDCS was put into service in a smeltery, the cost of power consumption has decreased significantly, and it also contributes to balancing the power grid load.

For the large-scale interconnected systems with value bounded uncertainties existing in the state, control input and interconnected matrices, decentralized robust H∞ state feedback controller design problem was investigated. Based on the bounded real lemma the sufficient conditions for its existence were presented, three linear matrix inequality (LMI) methods i.e. direct LMI method, iterative LMI method and homotopy iterative LMI method were proposed to design decentralized robust H∞ state feedback controller. The controllers obtained have the block diagonal structure, enable the closed-loop large-scale interconnected systems robust asymptotically stable and satisfy the given H∞ performance. An example was given to validate the methods offered.

In many industrial processes, especially chemistry and metallurgy industry, the plant is slow for feedback and data test because of complexand varying factors. Considering the multi-objective feature and the complexproblem of production stability in optimal control, this paper proposed an optimal control strategy based on genetic programming (GP), used as a multi-step state transferring procedure. The fitness function is computed by multi-step comprehensive evaluation algorithm, which provides a synthetic evaluation of multi-objective in process state based on single objective models. The punishment to process state variance is also introduced for the balance between optimal performance and stability of production. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution in GP with compact individuals. The optimal solution gained by evolution is a multi-step command program of process control, which not only ensures the optimization tendency but also avoids violent process variation by adjusting control parameters step by step. An optimal control system for operation direction is developed based on this strategy for imperial smelting process in Shaoguan. The simulation and application results showed its effectiveness for production objects optimization in complex process control.

In this paper, based on decentralized control, passivity of interconnected systems with time-delays is studied. Both existence results and expressions of dffentralized and passive controller are presented. The conditions are represented by linear matrix inequality (LMI). The results can be implemented easily in practice.

针对奇异大系统，研究其分散鲁棒H∞，广义输出反馈控制问题。基于有界实引理将存在分散鲁棒H∞广义输出反馈控制器的条件归结为一组矩阵不等式，采用同伦迭代算法求解分散鲁棒H∞广义输出反馈控制器，使闭环大系统鲁棒稳定，并满足给定的H∞性能指标。最后用数值例子说明了该设计方法的有效性。