Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.

Identifying the Optimal Refactoring Dependencies Using Heuristic Search Algorithms to Maximize Maintainability

Bad smells represent imperfection in the design of the software system and trigger the urge to refactor the source code. The quality of object-oriented software has always been a major concern for the developer team and refactoring techniques help them to focus on this aspect by transforming the code in a way such that the behavior of the software can be preserved. Rigorous research has been done in this field to improve the quality of the software using various techniques. But, one of the issues still remains unsettled, i.e. the overhead effort to refactor the code in order to yield the maximum maintainability value. In this paper, a quantitative evaluation method has been proposed to improve the maintainability value by identifying the most optimum refactoring dependencies in advance with the help of various meta-heuristic algorithms, including A*, AO*, Hill-Climbing and Greedy approaches. A comparison has been done between the maintainability values of the software used, before and after applying the proposed methodology. The results of this study show that the Greedy algorithm is the most promising algorithm amongst all the algorithms in determining the most optimum refactoring sequence resulting in 18.56% and 9.90% improvements in the maintainability values of jTDS and ArtOfIllusion projects, respectively. Further, this study would be beneficial for the software maintenance team as refactoring sequences will be available beforehand, thereby helping the team in maintaining the software with much ease to enhance the maintainability of the software. The proposed methodology will help the maintenance team to focus on a limited portion of the software due to prioritization of the classes, in turn helping them in completing their work within the budget and time constraints.

Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests

We study correlations between the structure and properties of a free association network of the English language, and solutions of psycholinguistic Remote Association Tests (RATs). We show that average hardness of individual RATs is largely determined by relative positions of test words (stimuli and response) on the free association network. We argue that the solution of RATs can be interpreted as a first passage search problem on a network whose vertices are words and links are associations between words. We propose different heuristic search algorithms and demonstrate that in “easily-solving” RATs (those that are solved in 15 seconds by more than 64% subjects) the solution is governed by “strong” network links (i.e. strong associations) directly connecting stimuli and response, and thus the efficient strategy consist in activating such strong links. In turn, the most efficient mechanism of solving medium and hard RATs consists of preferentially following sequence of “moderately weak” associations.

Exploiting Obstacle Geometry to Reduce Search Time in Grid-Based Pathfinding

Pathfinding is the problem of finding the shortest path between a pair of nodes in a graph. In the context of uniform-cost undirected grid maps, heuristic search algorithms, such as A ★ and weighted A ★ ( W A ★ ), have been dominantly used for pathfinding. However, the lack of knowledge about obstacle shapes in a gird map often leads heuristic search algorithms to unnecessarily explore areas where a viable path is not available. We refer to such areas in a grid map as blocked areas (BAs). This paper introduces a preprocessing algorithm that analyzes the geometry of obstacles in a grid map and stores knowledge about blocked areas in a memory-efficient balanced binary search tree data structure. During actual pathfinding, a search algorithm accesses the binary search tree to identify blocked areas in a grid map and therefore avoid exploring them. As a result, the search time is significantly reduced. The scope of the paper covers maps in which obstacles are represented as horizontal and vertical line-segments. The impact of using the blocked area knowledge during pathfinding in A ★ and W A ★ is evaluated using publicly available benchmark set, consisting of sixty grid maps of mazes and rooms. In mazes, the search time for both A ★ and W A ★ is reduced by 28 % , on average. In rooms, the search time for both A ★ and W A ★ is reduced by 30 % , on average. This is achieved while preserving the search optimality of A ★ and the search sub-optimality of W A ★ .

A Unifying View on Individual Bounds and Heuristic Inaccuracies in Bidirectional Search

In the past few years, new very successful bidirectional heuristic search algorithms have been proposed. Their key novelty is a lower bound on the cost of a solution that includes information from the g values in both directions. Kaindl and Kainz (1997) proposed measuring how inaccurate a heuristic is while expanding nodes in the opposite direction, and using this information to raise the f value of the evaluated nodes. However, this comes with a set of disadvantages and remains yet to be exploited to its full potential. Additionally, Sadhukhan (2013) presented BAE∗, a bidirectional best-first search algorithm based on the accumulated heuristic inaccuracy along a path. However, no complete comparison in regards to other bidirectional algorithms has yet been done, neither theoretical nor empirical. In this paper we define individual bounds within the lower-bound framework and show how both Kaindl and Kainz's and Sadhukhan's methods can be generalized thus creating new bounds. This overcomes previous shortcomings and allows newer algorithms to benefit from these techniques as well. Experimental results show a substantial improvement, up to an order of magnitude in the number of necessarily-expanded nodes compared to state-of-the-art near-optimal algorithms in common benchmarks.

Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search

Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.

A new surrogate model–based method for individualized spot welding sequence optimization with respect to geometrical quality

In an individualized shee metal assembly line, form and dimensional variation of the in-going parts and different disturbances from the assembly process result in the final geometrical deviations. Securing the final geometrical requirements in the sheet metal assemblies is of importance for achieving aesthetic and functional quality. Spot welding sequence is one of the influential contributors to the final geometrical deviation. Evaluating spot welding sequences to retrieve lower geometrical deviations is computationally expensive. In a geometry assurance digital twin, where assembly parameters are set to reach an optimal geometrical outcome, a limited time is available for performing this computation. Building a surrogate model based on the physical experiment data for each assembly is time-consuming. Performing heuristic search algorithms, together with the FEM simulation, requires extensive evaluations times. In this paper, a neural network approach is introduced for building surrogate models of the individual assemblies. The surrogate model builds the relationship between the spot welding sequence and geometrical deviation. The approach results in a drastic reduction in evaluation time, up to 90%, compared to the genetic algorithm, while reaching a geometrical deviation with marginal error from the global optimum after welding in a sequence.

Optimisation of Mix Proportioning of Self-Compacting Concrete using Heuristic Search

The quality of concrete construction mainly depends on the factors like concrete constituents, degree of supervision, method of curing, etc. Hitherto, one of the most influencing factors is the mix design or proportioning that contributes major parts directly to the quality of finished product. Unlike conventional concrete the mix proportion of Self-Compacting Concrete or Self-Consolidating Concrete (SCC) is complex since it incorporates special concrete chemicals such as Viscosity Modifying Agent (VMA), Air Entraining Agent (AEA), and so on, that tends to alter the workability and other properties of concrete and moreover it has no standard procedures and is generally carried out by trial and error method. This research seeks to develop a rational methodology for optimal mix proportion strategy for SCC based on the standard tests on SCC such as V-funnel and J-ring test conducted in the concrete laboratory in controlled environment as per EFNARC 2005 standard guidelines. The test results are incorporated for computer simulation and using a Heuristic Search Algorithms, the optimum mix proportioning of SCC for different admixtures are obtained.

A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network

The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. In this paper, a mixed integer programming model for observation tasks is established, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed. The configurable probability of the observation task is obtained by constructing a structural symmetric recurrent neural network, and finally, the optimal task planning scheme is obtained. The experimental results are compared with several typical heuristic search algorithms, which have certain advantages, and the validity of the paper is verified. Finally, future application prospects of the method are discussed.