PurposeThe purpose of this paper is to analyze and compare the performance of several different UAV trajectory tracking algorithms in normal and abnormal flight conditions to investigate the fault‐tolerant capabilities of a novel immunity‐based adaptive mechanism.Design/methodology/approachThe evaluation of these algorithms is performed using the West Virginia University (WVU) UAV simulation environment. Three types of fixed‐parameter algorithms are considered as well as their adaptive versions obtained by adding an immunity‐based mechanism. The types of control laws investigated are: position proportional, integral, and derivative control, outer‐loop nonlinear dynamic inversion (NLDI), and extended NLDI. Actuator failures on the three channels and increased turbulence conditions are considered for several different flight paths. Specific and global performance metrics are defined based on trajectory tracking errors and control surface activity.FindingsThe performance of all of the adaptive controllers proves to be better than their fixed parameter counterparts during the presence of a failure in all cases considered.Research limitations/implicationsThe immunity inspired adaptation mechanism has promising potential to enhance the fault‐tolerant capabilities of autonomous flight control algorithms and the extension of its use at all levels within the control laws considered and in conjunction with other control architectures is worth investigating.Practical implicationsThe WVU UAV simulation environment has been proved to be a valuable tool for autonomous flight algorithm development, testing, and evaluation in normal and abnormal flight conditions.Originality/valueA novel adaptation mechanism is investigated for UAV control algorithms with fault‐tolerant capabilities. The issue of fault tolerance of UAV control laws has only been addressed in a limited manner in the literature, although it becomes critical in the context of imminent integration of UAVs within the commercial airspace.

This paper presents a Matlab/Simulink simulation environment developed at West Virginia University to facilitate the design, testing, and analysis of fault-tolerant control laws for autonomous operation of unmanned aerial vehicles. Custom map generation software allows the user to setup the mission scenario by defining threat zones, obstacles, and points of interest. FlightGear provides vehicle and environment visualization. Maximum portability, flexibility, and extension capability are ensured through a modular structure including libraries of aircraft models, path planning algorithms, and trajectory tracking algorithms. Models for environmental upset conditions and aircraft sub-system failure/damage, including GPS, are implemented allowing the investigation of a variety of abnormal flight conditions. Several adaptive trajectory tracking algorithms with significant fault-tolerant potential are available. A set of comprehensive performance metrics based on tracking errors and control activity are computed and provided to the user for control laws performance analysis and comparison. The versatility and utility of the simulation environment is illustrated through example simulation results at normal and abnormal flight conditions.

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Purpose – The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA). Design/methodology/approach – The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures. Findings – The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution. Research limitations/implications – The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality. Practical implications – The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs. Originality/value – The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.