The present study deals with an AFCA (Adaptive Fuzzy Control Algorithm) for an Euler-Bemoulli approximation of a two-dimensional version of a cantilever beam-like orthogonal tetrahedral space truss. Transient disturbances, modeled as a unit impulse, excite all the modes of the beam. The resulting transverse displacement at the free end of the beam and its corresponding rate are observed by sensors placed there, and active control of the beam is provided by a collocated force actuator.
A design methodology, based on fuzzy logic which assumes no a priori knowledge of plant dynamics, for the closed-loop control algorithm results in relatively quick settling times, low overshoots and dying out of vibration within a few seconds. The control algorithm is enhanced and made much faster by eliminating the need of repeatedly solving the set of differential equations of motion of an emulated dynamic vibration absorber. When the control force is turned off after a mere 15 seconds, almost all the vibrational energy is dissipated as the beam returns to its undisturbed state throughout its length. In addition, the performance of the AFCA is insensitive to varying initial conditions. To examine the robustness of the control system to changes in the temporal dynamics of the cantilever beam, the transient disturbance response to a considerably perturbed plant is simulated. The Young's modulus of the beam was raised as well as lowered by 60%, substantially perturbing the natural frequencies of vibration compared to the nominal plant. The AFCA provided similar settling times and rates of vibrational energy dissipation, satisfying the aim of plant model independence.
UAV’s are being increasingly used today than ever before in both military and civil applications. A certain level of autonomy is imperative to the future of UAV’s. A quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAV’s are VTOL and hovering capabilities as well as a greater maneuverability. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. The objective of this research endeavor is to present the steps of designing, building and simulating an intelligent flight control module for a quadrotor UAV. Validation of the math model developed is discussed using actual flight data. Excellent attitude tracking is demonstrated for near hover flight regimes. System design is comprehensively dealt with. The responses are analyzed and future work involving hardware-in-the-loop simulations is proposed.
A short computational program was undertaken to evaluate the effectiveness of a closed-loop control strategy for the stabilization of an unstable bluff-body flow. In this effort, the non-linear one-dimensional Ginzburg–Landau wake model at 20% above the critical Reynolds number was studied. The numerical model, which is a non-linear partial differential equation with complex coefficients, was solved using the FEMLAB®/MATLAB® software packages and validated by comparison with published literature. At first, a model independent approach was attempted for wake suppression using feedback control. The closed-loop system was controlled using a conventional proportional-integral-derivative (PID) controller as well as a non-linear fuzzy controller. A single sensor is used for feedback, and the actuator is represented by altering the boundary conditions of the cylinder. Simulation results indicate that for a single sensor scheme, the increase in the sophistication of the control results in significantly shorter settling times. However, there is only a marginal improvement concerning the suppression of the wake at higher Reynolds numbers. The feedback control design was then augmented by switching over to a model-dependent controller. Based on computationally generated data obtained from solving the unforced wake, a low-dimensional model of the wake was developed and evaluated. The low-dimensional model of the unforced Ginzburg–Landau equation captures more than 99.8% of the kinetic energy using just two modes. Two sensors, placed in the absolutely unstable region of the wake, are used for real-time estimation of the first two modes. The estimator was developed using the linear stochastic estimation scheme. Finally, the loop is closed using a PID controller that provides the command input to the variable boundary conditions of the model. This method is relatively simple and easy to implement in a real-time scenario. The control approach, applied to the 300 node FEMLAB® model at 20% above the unforced critical Reynolds number stabilizes the entire wake. Compared to the model-independent controllers, the controller based on the low-dimensional model is far more effective in the suppression of the wake at higher Reynolds numbers. Furthermore, while the latter approach employs only the estimated temporal amplitude of the first mode of the imaginary part of the amplitude, all higher modes are stabilized. This suggests that the higher order modes are caused by a secondary instability that is suppressed once the primary instability is controlled.
Closed-loop control strategies were studied experimentally at low Reynolds and incompressible Mach numbers using periodic excitation to vector a turbulent jet. Vectoring was achieved by attaching a short, wide-angle diffuser at the jet exit and introducing periodic excitation from a slot covering one quadrant of the circumference of the round turbulent jet. Closed-loop control methods were applied to transition quickly and smoothly between different jet de ection angles. The frequency response of the zero-mass- ux piezoelectric actuatorwas at to about 0.5 kHz, but the jet responds up to 30–50 Hz only. This is still an order of magnitude faster than conventional thrust vectoring mechanism. System identi cation procedures were applied to approximate the system’s transfer function. A linear controller was designed that enabled fast and smooth transitions between stationary de ection angles and maintained desired jet vectoring angles under varying system conditions. The linear controller was tested over the entire range of available de ection angles, and its performance is evaluated and discussed.
Comparison of approximate approaches to solving the Travelling Salesman Problem and its application to UAV swarming. International Journal of Unmanned Systems Engineering. 3(1): 1-16. The Travelling Salesman Problem (TSP) is a widely researched Non-deterministic Polynomial-time hard optimization problem with a range of important applications in a wide spectrum of disciplines including aerospace engineering. In this paper, a comparison of different approaches to solve the TSP and also its application towards swarming of UAVs is considered. The objective of the TSP is to determine the optimal route associated with the shortest tour connecting all targets just once. Genetic Algorithms (GA) are one of the most widely applied techniques for solving this class of optimization problems. Two other techniques, 2-opt and Particle Swarm Optimization, are used and the results are compared with those obtained using GA. The comparison is made for different numbers of targets, using salient figures of merit such as computational time required and the cost function which is the minimum solution (distance) obtained. Results show that the 2-opt approach with the closest neighbour as initial starting point for the search yields superior performance. In the Multiple Travelling Salesman Problem, we propose a cluster-first approach which allocates each specific UAV to a subset of targets. The 200 targets are divided into four clusters corresponding to the four UAVs and then TSP algorithms like 2-opt and GA are employed to solve each cluster. This approach drastically reduces the computational time and also gives much better results than the conventional technique of directly applying GA over the 200 targets.
The control of exible structures employing the passivity approach has been extended to systems having noncollocated input/output pairs by introducing an observer that incorporates the nominal dynamical model of the plant. The passive observer-based control is applied to the American Control Conference benchmark problem, whereby, the control force emulates a dynamic vibration absorber attached to a virtual wall with passive control elements (spring, mass, and dashpot). The springs and mass elements of the controller are constant, whereas the damping coef cients are selected as time dependent in an attempt to choose continuously the most appropriate amount of damping in compliance with the design goals. A novel approach is introduced, whereby the passive observer-based control law is modi ed by varying the damping coef cient of the virtual dashpot by means of an adaptive fuzzy logic algorithm. This modi ed system exhibits quick settling times and desirable performance characteristics. Results from the statistical robustness analysis for the developed controller are compared to 10 other (linear) solutionsof the benchmarkproblem. The comparisonis based onrobust stability, robust performance (settling time), and control effort. The results obtained by the adaptive fuzzy logic algorithm are superior to those obtained by all other methods, and, consequently, further application of the fuzzy algorithm is advocated.
Uninhabited aerial vehicles provide numerous advantages in fighting wildland fires that include persistent operation and elimination of humans from performing what can be dull, dangerous, and dirty work. Multiple cooperating uninhabited aerial vehicles can potentially bring about a paradigm shift in the way we fight complex wildland fires. This paper investigates algorithmic development for cooperative control of a number of uninhabited aerial vehicles engaged in fighting a wildland fire. The paper considers two tasks to be performed by a group of uninhabited aerial vehicles: 1) Cooperative tracking of a fire front for accurate situational awareness, and 2) cooperative, autonomous fire fighting using fire suppressant fluid. The scenario considered in this paper makes the following assumptions: information regarding the location of the fire and position of all uninhabited aerial vehicles is made available to each uninhabited aerial vehicle; and each uninhabited aerial vehicle is equipped with unlimited fire suppressant fluid which extinguishes fire in a circle of specified area directly beneath it. This paper formulates these two tasks of fire fighting based upon optimization of respective utility functions, develops a decentralized control method for the cooperative uninhabited aerial vehicles, and analyzes the system for its stability and its ability to carry out the tasks. The proposed strategies have been verified with the help of extensive simulations. Although simplifying assumptions have been made, this preliminary study presents a framework for path planning and cooperative control of multiple uninhabited aerial vehicles engaged in gathering data and actively fighting forest fires.
For feedback control using low-dimensional proper orthogonal decomposition (POD) models, the mode amplitudes of the POD mode coefficients need to be estimated based on sensor readings. This paper is aimed at suppressing the von Kairman vortex street in the wake of a circular cylinder using a low-dimensional approach based on POD. We compare sensor placement methods based on the spatial distribution of the POD modes to arbitrary ad hoc methods. Flow field data were obtained from Navier-Stokes simulation as well as particle image velocimetry (PIV) measurements. A low-dimensional POD was applied to the snapshot ensembles from the experiment and simulation. Linear stochastic estimation was used to map the sensor readings of the velocity field on the POD mode coefficients. We studied 53 sensor placement configurations, 32 of which were based on POD eigenfunctions and the others using ad hoc methods. The effectiveness of the sensor configurations was investigated at Re = 100 for the computational fluid dynamic data, and for a Reynolds number range of 82-99 for the water tunnel PIV data. Results show that a five-sensor configuration can keep the root mean square estimation error, for the amplitudes of the first two modes to within 4% for simulation data and within 10% for the PIV data. This level of error is acceptable for a moderately robust controller The POD-based design was found to be simpler. more effective, and robust compared to the ad hoc methods examined.
Fuzzy logic is used in a variety of applications because of its universal approximator attribute and non-linear characteristics. But, it takes a lot of trial and error to come up with a set of membership functions and rule-base that will effectively work for a specific application. This process could be simplified by using a heuristic search algorithm like Genetic Algorithm (GA). In this paper, genetic fuzzy is applied to the task assignment for cooperating Unmanned Aerial Vehicles (UAVs) classified as the polygon visiting multiple traveling salesman problem (PVMTSP). The PVMTSP finds a lot of applications including UAV swarm routing. We propose a method of genetic fuzzy clustering that would be specific to PVMTSP problems and hence more efficient compared to k-means and c-means clustering. We developed two different algorithms using genetic fuzzy. One evaluates the distance covered by each UAV to cluster the search-space and the other uses a cost function that approximates the distance covered thus resulting in a reduced computational time. We compare these two approaches to each other as well as to an already benchmarked fuzzy clustering algorithm which is the current state-of-the-art. We also discuss how well our algorithm scales for increasing number of targets. The results are compared for small and large polygon sizes.