Cyber-Physical Production Systems (CPPSs) are complex manufacturing systems which aim to integrate and synchronize machine world and manufacturing facility to the cyber computational space. However, having intensive interconnectivity and a computational platform is crucial for real-world implementation of CPPSs. In this paper, the potential impacts of blockchain technology in development and realization of real-world CPPSs are discussed. A unified three-level blockchain architecture is proposed as a guideline for researchers and industries to clearly identify the potentials of blockchain and adapt, develop, and incorporate this technology with their manufacturing developments towards Industry 4.0.
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 study of the propagation of multiple cracks is essential to modeling and predicting structural integrity. The interaction between two cracks depends on a number of factors such as the domain geometry, the relative crack sizes and the separation between the two crack tips. In this paper, we study the interaction between two dynamically propagating cracks. We use the phase field method to track the crack paths, since this method can handle complex crack behavior such as crack branching, without any ad hoc criteria for crack evolution. The results from our dynamic simulations indicate that, unlike crack inter- action under quasi-static or fatigue loading, the presence of another crack does not accelerate crack propagation when dynamic loads are applied. However, some similarities in the crack topologies are observed for both quasi-static and dynamic loading.
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.