Particle swarm optimization (PSO) is extensively used for real parameter optimization in diverse fields of study. This paper describes an application of PSO to the problem of designing a fractional-order proportional-integral-derivative (PI λ D δ ) controller whose parameters comprise proportionality constant, integral constant, derivative constant, integral order (λ) and derivative order (δ). The presence of five optimizable parameters makes the task of designing a PI λ D δ controller more challenging than conventional PID controller design. Our design method focuses on minimizing the Integral Time Absolute Error (ITAE) criterion. The digital realization of the deigned system utilizes the Tustin operator-based continued fraction expansion scheme. We carry out a simulation that illustrates the effectiveness of the proposed approach especially for realizing fractional-order plants. This paper also attempts to study the behavior of fractional PID controller vis-à-vis that of its integerorder counterpart and demonstrates the superiority of the former to the latter.

System identification is a necessity in control theory. Classical control theory usually considers processes with integer order transfer functions. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme is presented for approximation of such a real world fractional order process by an ideal integral order model. A population of integral order process models is generated and updated by PSO technique, the fitness function being the sum of squared deviations from the set of observations obtained from the actual fractional order process. Results show that the proposed scheme offers a high degree of accuracy.

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In a long-haul optical fiber communication system, fiber attenuation, dispersion and nonlinearity combined with nondeterministic noise from optical amplifiers used for periodic regeneration cause adverse effects on system performance. Several optical and electrical signal processing techniques have been proposed and implemented to extract the transmitted data; some provide better performance than others, but at a cost of higher computational complexity. We present a modified nonlinear decision feedback equalizer designed for use in a legacy optical communication system with periodic dispersion compensation. The effects of noise and nonlinearity on the equalizer coefficients are investigated, and a suboptimal convergence algorithm to reduce such effects is proposed and verified. Our equalizer provides performance comparable to that obtained using digital backpropagation while being computationally simpler, compensating linear and nonlinear physical impairment effects effectively even at high power levels where fiber nonlinearity is significant. Performance prediction of the designed DFE is also discussed, using a numerical method, with and without error propagation.

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