The problem of guaranteed cost finite-time control of fractional-order positive switched systems (FOPSS) is considered in this paper. Firstly, a new cost function is defined. Then, by constructing linear copositive Lyapunov functions and using the average dwell time (ADT) approach, a state feedback controller and a static output feedback controller are constructed, respectively, and sufficient conditions are derived to guarantee that the corresponding closed-loop systems are guaranteed cost finite-time stable (GCFTS). Such conditions can be easily solved by linear programming. Finally, two examples are given to illustrate the effectiveness of the proposed method.
This paper considers the guaranteed cost finite-time control for positive switched linear systems with time-varying delays. The definition of guaranteed cost finite-time boundedness is firstly given. Then, by using the mode-dependent average dwell time approach, a static output feedback law and a state feedback control law are constructed, respectively, and sufficient conditions are obtained to guarantee that the closed-loop system is guaranteed cost finite-time boundedness. Such conditions can be easily solved by linear programming. Finally, an example is given to illustrate the effectiveness of the proposed method.
Clean renewable energy is given high priority to generate power in power system dispatching. Unfortunately, the objective to minimize wind power curtailment may results in additional costs, since the inverse peak regulation characteristic of wind power probably result in redundant power qualities curtailed to ensure the balance of the electric supply and demand in real time. In this paper, considering the cost of deep peak regulation of thermal unit and wind power curtailment, unit commitment comprehensive optimal model is proposed to minimize total cost by searching the optimal wind power curtailment. In order to achieve an economic strategy among the wind power curtailment and the deep peak regulation, two indexes including the amount of hourly optimal wind power curtailment and the critical value of comprehensive wind power curtailment are developed. Particle swarm optimization algorithm is employed for solving the proposed model. Afterwards, the relationship between the degree of deep peak regulation and the amount of wind power curtailment is researched, the derived conclusions can provide valuable information for system operators. INDEX TERMS Wind power curtailment, deep peak regulation, unit commitment comprehensive optimal model, particle swarm optimization algorithm, system operators.
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