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In this paper, selection of the state feedback gains by Particle Swarm
Optimization (PSO) technique is presented in opposition to the selection of the feedback
gains reported in literature. The proposed design has been applied to the variable
speed induction motor drive system. The system performance has been simulated
and compared with some previous methods such as Variable Structure Controller
(VSC) method and Genetic Algorithm (GA) approach. Simulation results show
improved dynamic system performance. The results bring out the effectiveness of the
proposed technique.
Optimal control deals with the problem of finding a control law for a given system
such that a certain optimality criterion is achieved. A control problem includes a cost
functional that is dependent on the state and control variables. An optimal control is a set of
differential equations describing the paths of the control variables that minimize the cost
function. Application of the variable structure controllers to different engineering problems,
including power systems (Sivaramakrishnan et
al., 1984; AI-Hamouz and Abdel-Magid, 1993; Bhattacharya et al., 1995; and AI-Hamouz and AI-Duwaish, 2000), aerospace
(X-Y Lu et al., 1997), robotics (Zribi et
al., 1997), and many others, had been
increasing in the last two decades. Very recently, the problem of Variable Structure Controller
(VSC) feedback gains selection was considered by Bhattacharya et al. (1995). Their approach essentially was to try all permissible values of the feedback gains and evaluate
a performance index for each set of feedback gains. The optimal feedback gains
selected are those which minimize the performance index. This approach is numerically
intensive, especially for large numbers of feedback gains.
Particle Swarm Optimization (PSO) is a new evolutionary computation technique
which has been applied recently to some practical problems (Sen et al., 2002). In the present work, a new approach based on PSO is proposed for the selection of the state
feedback gains. This is accomplished by formulating the state feedback gains selection as
an optimization problem and PSO is used in the optimization process. The proposed
method provides an optimal and systematic way of state feedback gains selection. |
