An adaptive autopilot design for an uninhabited surface vehicle

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council

Sequential Loop Closure Based Adaptive Autopilot Design for a Hypersonic Vehicle

This paper presents a sequential loop closure approach to designing a velocity and altitude tracking au- topilot for a hypersonic vehicle. The control architecture consists of two decoupled control subsystems, one for velocity, the other for altitude. The velocity control subsystem consists of an adaptive augmented baseline controller. The altitude control subsystem consists of an adaptive inner-loop designed to accommodate uncer- tainties in the stability and control derivatives of the aircraft, and track pitch-rate commands. The outer-loop is designed independent of the inner loop, and guarantees stability of the closed-loop system. The outer-loop uses components of a closed-loop reference model, and generates the appropriate pitch-rate commands for the inner loop such that the hypersonic vehicle tracks the desired altitude. A numerical example based on a scram- jet powered, blended wing-body generic hypersonic vehicle model is presented, demonstrating the efficacy of the proposed control design.Approved for Public Release; Distribution Unlimited. Case Number 88ABW-2015-5617

Output consensus of nonlinear multi-agent systems with unknown control directions

In this paper, we consider an output consensus problem for a general class of nonlinear multi-agent systems without a prior knowledge of the agents' control directions. Two distributed Nussbaumtype control laws are proposed to solve the leaderless and leader-following adaptive consensus for heterogeneous multiple agents. Examples and simulations are given to verify their effectivenessComment: 10 pages;2 figure

Design Of An Adaptive Autopilot For An Expendable Launch Vehicle

This study investigates the use of a Model Reference Adaptive Control (MRAC) direct approach to solve the attitude control problem of an Expendable Launch Vehicle (ELV) during its boost phase of flight. The adaptive autopilot design is based on Lyapunov Stability Theory and provides a useful means for controlling the ELV in the presence of environmental and dynamical uncertainties. Several different basis functions are employed to approximate the nonlinear parametric uncertainties in the system dynamics. The control system is designed so that the desire dresponse to a reference model would be tracked by the closed-loop system. The reference model is obtained via the feedback linearization technique applied to the nonlinear ELV dynamics. The adaptive control method is then applied to a representative ELV longitudinal motion, specifically the 6th flight of Atlas-Centaur launch vehicle (AC-6) in 1965. The simulation results presented are compared to that of the actual AC-6 post-flight trajectory reconstruction. Recommendations are made for modification and future applications of the method for several other ELV dynamics issues, such as control saturation, engine inertia, flexible body dynamics, and sloshing of liquid fuels

Predictive Control of a Munition Using Low-Speed Linear Theory

Modified linear theory provides reasonable impact predictions at high speeds. However, for typical small UAS mission speeds, less than 20-m/s impact errors were substantial due to large angles of attack and pitch rates. Low-speed linear theory was developed by including higher-order terms involving w and q that modified linear theory neglects. As a result, the angle of attack, pitch, and yaw predictions are significantly improved, leading to accurate impact predictions even at very low speeds. A predictive control scheme was developed to reduce dispersion using control surfaces near the tail. The predictive controller uses low-speed linear theory to rapidly predict the impact error using the current state and control. Based on the estimated impact error, the control is iteratively found to minimize the predicted-impact error. For an example munition, it was shown that the maximum number of iterations during the control solution only impacted the initial control estimates. Limiting the guidance algorithm to a single iteration had little impact on the final accuracy and permitted a rapid solution. It was shown for the example munition that the predictive guidance significantly reduced the CEP from 14.1 to 2.7 and 2.2 m when the maximum iterations were 1 and 10. Furthermore, for a typical high- explosive 40-mm grenade, the percentage of impacts within a lethal radius was increased from 10 to 78% when the maximum iterations were both 1 and 10. In practical applications, errors in the target location must beincluded when considering the probability of impact within a lethal range of a target

DESIGN CONTROL OF SURFACE MARINE VEHICLE USING DISTURBANCE COMPENSATING MODEL PREDICTIVE CONTROL (DC-MPC)

This research studied ship motion control by considering four degrees of freedom (DoF): yaw, roll, sway, and surge in which comprehensive mathematical modeling forming a nonlinear differential equation. Furthermore, this research also investigated solutions for fundamental yet challenging steering problems of ship maneuvering using advanced control method: Disturbance Compensating Model Predictive Control (DC-MPC) method, which based on Model Predictive Control (MPC). The DC-MPC allows optimizing a compensated control then consider sea waves as the environmental disturbances. Those sea waves influence the control and also becomes one of the constraints for the system. The simulation compared the varying condition of Horizon Prediction (Np) and another method showing that the DC-MPC can manage well the given disturbances while maneuvering in certain Horizon Prediction. The results revealed that the ship is stable and follows the desired trajector