Muwahida Liaquat

Discrete-Time Output Regulation on Sample-Data Systems

—In this paper output regulation problem for sampled-data systems with constant exogenous signals is considered. A discrete output feedback controller is designed under the assumption that the regulator equation in discrete-time has a solution. Discretization of the plant and exosystem is carried out by zero order hold equivalence. The goal is to design the complete controller/observer in discrete domain. As an application magnetic levitation is discussed. The simulation results along with the comparison with the continuous-time controller show the effectiveness of the proposed controller.

A realizable reconstruction filter for sampled data systems

A realizable reconstruction filter is presented. Since the standard reconstruction filters are not realizable, our aim is to produce a reconstruction filter as an impulsive system that contains the dynamics of the reference signal. The proposed design is a memory less system and is able to overcome the intersampling behavior when applied in the closed loop system. As compared to other techniques such as generalized sampled data hold devices being used this design does not require the input and output of the reconstruction filter to be of different dimensions.

Sampled data output regulation of n-link robotic manipulator using a realizable reconstruction filter

This paper proposes a solution to the sampled-data regulation problem for feedback linearizable n-link robotic manipulators. The prime focus is on the development and stability analysis of the proposed control scheme in the presence of model uncertainties and external disturbances. A major constraint is the availability of sampled measurements of output signal. This leads to designing an impulsive observer for feedback linearization. The discrete-time control input is mapped into its continuous-time counterpart using a realizable reconstruction filter (RRF). The underlying control scheme relies on the sampled-data regulator theory based on the discrete-time equivalence of the plant and RRF modeled as impulsive system. This method leads to controller/observer design in discrete time. The working of the entire scheme is dependent on the stability of impulsive observer; hence a Lyapunov-based stability analysis is also included to ensure the stability of a closed-loop system. The working of the proposed scheme along with a comparison with conventional solution is presented, when applied to the control of a 3-degree-of-freedom PUMA 560 robot.

Human detection in sensitive security areas through recognition of omega shapes using MACH filters

Human detection has gained considerable importance in aggravated security scenarios over recent times. An effective security application relies strongly on detailed information regarding the scene under consideration. A larger accumulation of humans than the number of personal authorized to visit a security controlled area must be effectively detected, amicably alarmed and immediately monitored. A framework involving a novel combination of some existing techniques allows an immediate detection of an undesirable crowd in a region under observation. Frame differencing provides a clear visibility of moving objects while highlighting those objects in each frame acquired by a real time camera. Training of a correlation pattern recognition based filter on desired shapes such as elliptical representations of human faces (variants of an Omega Shape) yields correct detections. The inherent ability of correlation pattern recognition filters caters for angular rotations in the target object and renders decision regarding the existence of the number of persons exceeding an allowed figure in the monitored area.

Sampled data output regulation of a linear system based on finite time impulsive observer

This paper deals with the impulsive output feedback regulation of linear time invariant systems. The considerable restriction is the accessibility of only sampled measurements of the continuous-time plant and the reference signal. An impulsive estimation technique is proposed for the finite time state estimation under the given constraints. A continuous-time controller is then used to achieve the regulation such that the estimation and tracking errors converge exponentially. The scheme is applied on a magnetic levitation system, to show the effectiveness of the design.

A New Frequency-Limited Interval Gramians-Based Model Order Reduction Technique

Model order reduction (MOR) is a process of finding a lower approximation of the original system. In many practical applications, only a certain frequency interval is of interest. This motivates limited frequency interval model reduction wherein a reduced model is found whose output fits with that of the original system within the desired frequency interval. A frequency-limited balancing-based MOR technique is proposed, which yields stable models with less approximation error than existing stability-preserving techniques. The superiority of the proposed technique is highlighted with the help of numerical examples.

Robust fault tolerant control of a DC motor in the presence of actuator faults

A robust methodology for the Fault Tolerant Control (FTC) of a permanent magnet DC motor in the presence of different types of actuator faults is developed and tested in this paper. The Fault Detection and Isolation (FDI) is achieved by employing the Observer based residual scheme. In order to make the generated residuals insensitive to disturbances and uncertainties, the method of Eigenstructure Assignment (EA) is also incorporated in the FDI scheme. The Sequential Probability Ratio Test (SPRT) is employed for the efficient statistical testing of the residuals. Subsequently the Multiple Models Switching and Tuning (MMST) technique based on Linear Quadratic Gaussian (LQG) regulator is designed and used as the reconfigurable controller. The robustness and efficiency of the developed FTC system is illustrated through simulations on a permanent magnet DC motor model.

Landing control of unmanned aerial vehicle using continuous model predictive control

In this paper, landing control has been designed using Model Predictive control for autonomous landing of unmanned aerial vehicles (UAVs), along with associated path planning. An improved design and development of controller required for the autonomous landing of fixed wing unmanned aircraft is presented. Model Predictive controller using laguerre function is designed to ensure optimum performance of landing phase of UAV. MPC improves performance by giving superior control over MIMO controller of landing. A quadratic cost function minimization using cyclic co-ordinate search method is utilized in solving constraints imposed on inputs of the system. Numerical simulations are performed to verify feasibility of the proposed landing control law of aircraft under input constraints.

A Passivity-Preserving Frequency-Weighted Model Order Reduction Technique

Frequency-weighted model order reduction techniques aim to yield a reduced order model whose output matches that of the original system in the emphasized frequency region. However, passivity of the original system is only known to be preserved in the single-sided weighted case. A frequency-weighted model order reduction technique is proposed, which guarantees the passive reduced models in the double-sided weighted case. A set of easily computable error bound expressions are also presented.

Robust fault tolerant control of an unmanned aerial vehicle in the presence of actuator faults

This paper is aimed at developing and testing a robust scheme for the Fault Tolerant Control (FTC) of an Unmanned Aerial Vehicle (UAV) in the presence of three different forms of actuator faults. The FTC is composed of Fault Detection and Isolation (FDI) and reconfiguration steps. The FDI is achieved through the use of full-state Observers and it is made robust by incorporating the Eigenstructure Assignment method. The statistical testing of residuals for fault detection is done by the Sequential Probability Ratio Test (SPRT). For reconfiguration, the Linear Quadratic Gaussian (LQG) regulator based Multiple Models Switching and Tuning (MMST) method is employed. At the end, the proposed FTC technique is applied on a linearized lateral directional model of the Aerosonde UAV and simulated in the Simulink/MATLAB environment to justify its robustness and efficiency.