Performance improvement of PID-controller for integrating systems with time delay


Integrating systems with time delay are found in modeling of liquid level systems, liquid storage tanks, boilers, batch chemical reactors and bottom level control of a distillation column. There also exist Processes such as aerospace control system, vertical take-off air place (Filatov et al, 1996), DC motors
and high speed disk drives (Liu et al., 2004). Oxygen control in fed batch fragmental fungal fermentation reactors (Bodizs et a1.2007) whose dynamics show the characteristics of double integrating types.
There are number of methods available in the literature for designing PID controllers for pure integrating with time delay. They are: methods based on stability criteria (Ziegler Nichols, 1942; Tyreus and Luyben, 1992; Chidambaram, 1994; Poulin and Pomerleau, 1999; Luyben, 1996; Wang and Cluette, 1997; Kookos et al., 1999; Wang and Cai, 2002 ;), optimization method (Visioli, 2001; Zhang et al., 1999), IMC method (Rivera et al., 1986; Chien and Fruehauf, 1990; Skogestad, 2003), two degree Freedom Controller (Sung and Lee, 1996), modified smith predictor method for large time delay (Matausek and Micic, 1999; Majhi and Atherton, 1999) and direct synthesis method (Seshagiri Rao and  Chidambaram, 2008).
            Generally when a PID Controller is designed for a process, it is designed to give best performance for the regulatory problem. The same PID is used for a servo problem which leads to a large overshoot for integrating and unstable systems. The overshoot is reduced by set point weighted PID controller. Many of the commercial PID controllers have the set point weighted PID action. However, the method for the Selection of set point weighting parameter is not given for integrating and unstable systems.
Therefore, the present work is intended to calculate set point weighting parameter for integrating Systems, double integrating systems and unstable/stable FOPTD(First   Order Plus Time Delay) systems with an integrator using numerical optimization of ISE (Integral Square Error), IAE (Integral Absolute Error), ITAE (Integral Time weighted Absolute Error) using MATLAB and simulink
It is also proposed to extend the equating coefficient method (Padma Sree and Chidambaram, 2004) for the calculation of set point weighting parameter for unstable systems to the above mentioned systems and Compare the servo performance with numerical optimization methods.
Simulations on various transfer function models of integrating system with time delay, double integrating system with time delay and stable/unstable FOPTD system with an integrator show that both the methods give improved performance  with less ISE, IAE and ITAE values when compared with the conventional PID controller and the conventional PID controller with a set point filter (Lee et al., 1999).


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Architecture for the Autonomous Generation of Preference-Based Trajectories


Abstract: Numerous techniques exist to optimize aircraft and spacecraft trajectories over cost functions that include terms such as fuel, time, and separation from obstacles. Relative weighting factors can dramatically alter solution characteristics, and engineers often must manually adjust either cost weights or the trajectory itself to obtain desirable solutions. Further, when humans and robots work together, or when humans task robots, they may express their performance expectations in a "fuzzy" natural language fashion, or else as an uncertain range of more or less acceptable values. This work describes a software architecture which accepts both fuzzy linguistic and hard numeric constraints on trajectory performance and, using a trajectory generator provided by the user, automatically constructs trajectories to meet these specifications as closely as possible. The system respects hard constraints imposed by system dynamics or by the user, and will not let the user's preferences interfere with the system and user needs. The architecture's evaluation agent translates these requirements into cost functional weights expected to produce the desired motion characteristics. The quality of the resulting full-state trajectory is then evaluated based on a set of computed trajectory features compared to the specified constraints. If constraints are not met, the cost functional weights are adjusted according to precomputed heuristic equations. Heuristics are not generated in an ad hoc fashion, but are instead the result of a systematic testing of the simulated system under a range of simple conditions. The system is tested in a 2DOF linear and a 6DOF nonlinear domain with a variety of constraints and in the presence of obstacles. Results show that the system consistently meets all hard numeric constraints placed on the trajectory. Desired characteristics are often attainable or else, in those cases where they are discounted in favor of the hard constraints, failed by small margins. Results are discussed as a function of obstacles and of constraints.

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Aerospace Project


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AirCraft Hydraulic System


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