I.G. Sarma

Two robust homing missile guidance laws based on sliding mode control theory

Two new guidance laws for short range homing missiles are developed by invoking the sliding mode control (SMC) theory. Guidance law 1 as structured around the basic proportional navigation (PN), with an additive switching term, which is a function of the line of sight (LOS) rate alone. An adaptive procedure is suggested to select the gain of the switching term, in order to reduce chattering. This guidance law is nearly as simple to implement as the PN itself and does not require any explicit target maneuver estimation. Guidance law 2, based on a first order sliding surface, is designed such that it results in a continuous acceleration law, thereby reducing the chattering problem. While explicitly taking into account the effect of aerodynamic drag, it requires the second derivatives of LOS angle and range, which are not directly measured. An estimation scheme, again based on sliding mode theory, is presented to estimate these quantities. Simulation results clearly demonstrate the superior performance of these schemes.<<ETX>>

Self-Tuning Control of Nonlinear Systems Characterised by Hammerstein Models

Abstract The concept of self-tuning is applied to the control of Hammerstein models. It is also shown that these models are not amenable to self-tuning if the performance index includes control weighting.

State space formulation of ammonia reactor dynamics

The specific objective of this paper is to develop a state space model of a tubular ammonia reactor which is the heart of an ammonia plant in a fertiliser complex. A ninth order model with three control inputs and two disturbance inputs is generated from the nonlinear distributed model using linearization and lumping approximations. The lumped model is chosen such that the steady state temperature at the exit of the catalyst bed computed from the simplified state space model is close enough to the one computed from the nonlinear steady state model. The model developed in this paper is very useful for the design of continuous/discrete versions of single variable/multivariable control algorithms.

Identification and optimization of ammonia reactors through hybrid simulation

A hybrid simulation technique for identification and steady state optimization of a tubular reactor used in ammonia synthesis is presented. The parameter identification program finds the catalyst activity factor and certain heat transfer coefficients that minimize the sum of squares of deviation from simulated and actual temperature measurements obtained from an operating plant. The optimization program finds the values of three flows to the reactor to maximize the ammonia yield using the estimated parameter values. Powells direct method of optimization is used in both cases. The results obtained here are compared with the plant data.

Optimization of an ammonia reactor using regression analysis

This paper presents an optimization algorithm for an ammonia reactor based on a regression model relating the yield to several parameters, control inputs and disturbances. This model is derived from the data generated by hybrid simulation of the steady-state equations describing the reactor behaviour. The simplicity of the optimization program along with its ability to take into account constraints on flow variables make it best suited in supervisory control applications.

Discrete control algorithms for a tubular ammonia reactor

The specific objective of this paper is to develop direct digital control strategies for an ammonia reactor using quadratic regulator theory and compare the performance of the resultant control system with that under conventional PID regulators. The controller design studies are based on a ninth order state-space model obtained from the exact nonlinear distributed model using linearization and lumping approximations. The evaluation of these controllers with reference to their disturbance rejection capabilities and transient response characteristics, is carried out using hybrid computer simulation.

Robust Multivariable Controllers for a Tubular Ammonia Reactor

The specific objective of this paper is to develop multivariable controllers that would achieve asymptotic regulation in the presence of parameter variations and disturbance inputs for a tubular reactor used in ammonia synthesis. A ninth order state space model with three control inputs and two disturbance inputs is generated from the nonlinear distributed model using linearization and lumping approximations. Using this model, an approach for control system design is developed keeping in view the imperfections of the model and the measurability of the state variables. Specifically, the design of feedforward and robust integral controllers using state and output feedback is considered. Also, the design of robust multiloop proportional integral controllers is presented. Finally the performance of these controllers is evaluated through simulation.

Constrained linear minimax filter for systems with large plant uncertainties

This paper presents a method of designing a minimax filter in the presence of large plant uncertainties and constraints on the mean squared values of the estimates. The minimax filtering problem is reformulated in the framework of a deterministic optimal control problem and the method of solution employed, invokes the matrix Minimum Principle. The constrained linear filter and its relation to singular control problems has been illustrated. For the class of problems considered here it is shown that the filter can he constrained separately after carrying out the mini maximization. Numorieal examples are presented to illustrate the results.

Sliding Mode Estimation Scheme For Missile Homing Guidance

A new approach to maneuvering target tracking using the Sliding Mode Observer theory, in the specific context of homing missile guidance, is developed. This scheme requires a priori information about the target acceleration bounds alone, and there is no need for a dynamic model to represent the target maneuver dynamics. The proposed estimator is as simple to implement as the conventional Extended Kalman Filter (EKF) itself. The simulation results, in the presence of realistic noise sources, demonstrate the superiority of the proposed estimation scheme over the EKF.

SELFTUNING CASCADE CONTROL OF LINEAR AND NONLINEAR SYSTEMS

Abstract Selftuning is applied to the control of systems whose parameters are unknown. Both linear and nonlinear systems of the Hammerstein model are considered.