Development of a Controlled Dynamics Simulator for Reusable Launcher Descent and Precise Landing

This paper introduces a Reusable Launch Vehicle (RLV) descent dynamics simulator coupled with closed-loop guidance and control (G&C) integration. The studied vehicle's first-stage booster, evolving in the terrestrial atmosphere, is steered by a Thrust Vector Control (TVC) system and planar fins through gain-scheduled Proportional-Integral-Derivative controllers, correcting the trajectory deviations until precise landing from the reference profile computed in real time by a successive convex optimisation algorithm. Environmental and aerodynamic models that reproduce realistic atmospheric conditions are integrated into the simulator for enhanced assessment. Comparative performance results were achieved in terms of control configuration (TVC-only, fins-only, and both) for nominal conditions as well as with external disturbances such as wind gusts or multiple uncertainties through a Monte Carlo analysis to assess the G&C system. These studies demonstrated that the configuration combining TVC and steerable planar fins has sufficient control authority to provide stable flight and adequate uncertainties and disturbance rejection. The developed simulator provides a preliminary assessment of G&C techniques for the RLV descent and landing phase, along with examining the interactions that occur. In particular, it paves the way towards the development and assessment of more advanced and robust algorithms

Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network

Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu} controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.Comment: 6 pages, 12 figure

Adaptive Backstepping Controller for Uncertain Systems With Unknown Input Time-Delay. Application to SI Engines

International audienceIn this paper, we study the equilibrium regulation of potentially unstable linear systems with an unknown input time-delay and unknown parameters in the plant. We extend recent results from the literature where such systems are treated using a backstepping approach applied to a distributed parameters system representation of the delay. We develop a local result, robust to delay errors and apply it for the control of the Air-Fuel Ratio in Spark Ignition engines. A proof of convergence is established for this particular example. Experimental results stress the relevance of the proposed control algorithm

Adaptive Control System for Autonomous Helicopter Slung Load Operations

This paper presents design and verification of an estimation and control system for a helicopter slung load system. The estimator provides position and velocity estimates of the slung load and is designed to augment existing navigation in autonomous helicopters. Sensor input is provided by a vision system on the helicopter that measures the position of the slung load. The controller is a combined feedforward and feedback scheme for simultaneous avoidance of swing excitation and active swing damping. Simulations and laboratory flight tests show the effectiveness of the combined control system, yielding significant load swing reduction compared to the baseline controller

Observer-Based State Feedback for Enhanced Insulin Control of Type ‘I’ Diabetic Patients

During the past few decades, biomedical modeling techniques have been applied to improve performance of a wide variety of medical systems that require monitoring and control. Diabetes is one of the most important medical problems. This paper focuses on designing a state feedback controller with observer to improve the performance of the insulin control for type ‘I’ diabetic patients. The dynamic model of glucose levels in diabetic patients is a nonlinear model. The system is a typical fourth-order single-input-single-output state space model. Using a linear time invariant controller based on an operating condition is a common method to simplify control design. On the other hand, adaptive control can potentially improve system performance. But it increases control complexity and may create further stability issues. This paper investigates patient models and presents a simplified control scheme using observer-based feedback controllers. By comparing different control schemes, it shows that a properly designed state feedback controller with observer can eliminate the adaptation strategy that the Proportional-Integral-Derivative (PID) controllers need to improve the control performance. Control strategies are simulated and their performance is evaluated in MATLAB and Simulink

A toolbox for robust PID controller tuning using convex optimization

A robust PID controller design toolbox for Matlab is presented in this paper. The design is based on linearizing or convexifying the conventional non-convex constraints on the classical robustness margins or H∞ constraints. Then the existing optimization solvers can be used to compute the controller parameters. The software can be used in a wide range of controller design problems, including multi-model systems and gain-scheduled controllers. The models can be parametric or non-parametric while the software is compatible with the output data of the identification toolbox of Matlab. Three illustrative examples exhibit convenience of working with the developed commands