Robots have become an integral part of industrial automation. Their ultimate role and contribution in this sector is essentially a function of the associated control strategy to ensure precision, r...

Optimal and Robust Control of Multi DOF Robotic Manipulator: Design and Hardware Realization

Current advancements on autonomous mission planning and management systems: An AUV and UAV perspective

In this paper, twin rotor multi input multi output system (TRMS) is considered as a prototype laboratory set-up of helicopter. The aim of studying the model of TRMS and designing the controller for it is to provide a platform for controlling the flight of helicopter. An optimal state feedback controller based on linear quadratic regulator (LQR) technique has been designed for twin rotor multi input multi output system. TRMS is a nonlinear system with two degrees of freedom and cross couplings. The mathematical modeling of TRMS has been done using MATLAB/SIMULINK. The linearised model of TRMS is obtained from the nonlinear model. The simulation results of optimal controller are compared with the results of conventional PID controller. The appropriateness of proposed controller has been shown both in terms of transient and steady state response.

https://www.springer.com/cda/content/document/cda_downloaddocument/9788132222040-c2.pdf?SGWID=0-0-45-1490587-p177122077

Optimal Control of Twin Rotor MIMO System Using LQR Technique

There is a high demand for developing effective controllers to perform fast and accurate operations for either flexible link manipulators (FLMs) or rigid link manipulators (RLMs). Thus, this paper is beneficial for such vast field, and it is also advantageous and indispensable for researchers who are interested in robotics to have sufficient knowledge about various controllers of FLMs and RLMs as the controllers’ concepts are elaborated in detail. The paper concentrates in critically reviewing classical controllers, intelligent controllers, robust controllers, and hybrid controllers for both FLMs and RLMs. The advantages and disadvantages of the aforementioned control methods are summarized in this paper; it also has a detailed comparison for the controllers in terms of the design difficulty, performance, and the suitability for controlling FLMs or RLMs.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/77234C86571FA246BCFD0B8331BE37DC/S0263574720000223a.pdf/div-class-title-a-critical-review-of-control-techniques-for-flexible-and-rigid-link-manipulators-div.pdf

A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

Optimal route planning with prioritized task scheduling for AUV missions

In this paper we calculate the Shortest p ath between Source and Destination node for Static and Dynamic Routing Networks. First we applied Dijkstra’s Algorithm (DA) and then Genetic Algorithm (GA) to find the Shortest Path. Both the Algorithms provide the same solution. The results affirmed the potent ial of Genetic Algorithm. We will compare the Execution Ti me for both Algorithms and find that DA takes more time in finding the optimum path, as compared to GA. So, GA has the potential to replace DA in finding the shortest path for Network Topologies. Simulation results are performed for both Algorithms on MATLAB and GENETIC ALGORITHM TOOL. Keywords— Dijkstra’s algorithm, Genetic algorithm, Routing I-INTRODUCTION Routing is a process of transferring packets from s urce node to destination node with minimum cost[5] [6]. Cost factors may be the distance of a Router, network th roughput of a link or link availability and reliabi lity expressed as simple unit les numbers. Hence routing algorithm ha s to acquire, organize and distribute information a bout network states. It should generate feasible routes between nodes and send traffic along the selected path and also achieve high performance. Routing process uses a data struc ture alled routing table at each node to store all the nodes which are at one hop distance from neighbor node. It also stores the other nodes along with the numbe r of hops to reach that node, followed by the neigh bor node through which it can be reached. Router decides whi ch neighbor to choose from routing table to reach s pecific destination. A. Static routing Static routing is a data communication concept desc ribing one way of configuring path selection of rou ters in computer networks [5]. The routes added manually to the routing table In these systems, routes through a data network are described by fixed paths. An entire net work can be configured using static routes. When th re is a change in the network or a failure occurs between two stat ically defined nodes, traffic will not be rerouted. B. Dynamic Routing Adaptive routing describes the capability of a syst em, through which routes are characterized by their destination, to alter the path that the route takes through the system in response to a change in conditions [7]. T he adaptation is intended to allow as many routes as possible to rem ain valid. For finding the shortest path, Dijkstra’s algorithm is used. Dijkstra's algorithm, conceived by Dutch computer scientist Edsger Dijkstra in 1956 and published in 1959[5] [7], is a graph search algorithm that solve s the singlesource shortest path problem for a graph with nonne gative edge path costs, producing a shortest path t ree. This algorithm is often used in routing and as a subrout ine in other graph algorithms. II. DIJKSTRA’S ALGORITHM [7] The Dijkstra’s algorithm calculates the shortest pa th between two points on a network using a graph ma de up of nodes and edges. It assigns to every node a cost va lue, set it to zero for source node and infinity fo r all other nodes. The algorithm divides the nodes into two sets: tent ative and permanent. It chooses nodes, makes them t entative, 417 Comparison of Dijkstra’s Shortest Path Algorithm wi th Genetic Algorithm for Static and Dynamic Routing Network ISSN-2277-1956 /V1N2-416-425 examines them, and if they pass the criteria, makes th m permanent. The algorithm can be defined by th e following steps: 1. Start with the source node: the root of the tree . 2. Assign a cost of 0 to this node and make it the first permanent node. 3. Examine each neighbor node of the node that was the last permanent node. 4. Assign a cumulative cost to each node and make i t tentative. 5. Among the list of tentative nodes a. Find the node with the smallest cumulative cost and mark it as permanent. A permanent node will not be checked ever again, its cost recorded now is final. b. If a node can be reached from more than one dire ction, select the direction with the shortest cumul ative cost. 6. Repeat steps 3 to 5 until every node becomes per manent. Figure1.Network Topology If the algorithm is applied to the network in figur e (1) to calculate the shortest path between the so urce node A(1) and the destination node B(7),the shortest path wil l be 1-3-6-7 with cost 23. III. GENETIC ALGORITHMS Genetic Algorithms (GAs) was invented by John Holla nd [4]. Holland proposed GA as a heuristic method b ased on “Survival of the fittest”. GA was discovered as a useful tool for search and optimization problems. A. Search Space [4] Most often one is looking for the best solution in a specific set of solutions. The space of all feasi ble solutions (the set of solutions among which the desired solution r esides) is called search space (also state space). Each and every point in the search space represents one possible s olution. Therefore each possible solution can be “m arked” by its fitness value, depending on the problem definition. With Genetic Algorithm one looks for the best solu ti n among a number of possible solutions represented by one poi nt in the search space i.e.; GAs are used to search the search space for the best solution e.g., minimum. The difficulti es in this ease are the local minima and the starti ng point of the search. A very important point is that genetic algo rithms always consider a population of solutions. K eeping in memory more than single solution attach iteration o ffers a lot of advantages. The algorithm can recomb ine different solutions to get better ones and so, it can use the ben fits of assortment. B. Random Search [4] Random search is an extremely basic method. It only explores the search space by randomly selecting so lutions and evaluates their fitness. The efficiency of GA i s extremely dependant on consistent coding and rele vant reproduction operators. If the reproduction operato rs are just producing new random solutions without any concrete IJECSE,Volume1,Number 2 Yagvalkya Sharma et al. ISSN-2277-1956 /V1N2-416-425 links to the ones selected from the last generation , he genetic algorithm is just doing nothing else that a random search. Random search does have a few interesting q ualities. However good the obtained solution may be , if it’s not optimal one, it can be always improved by continuin g the run of the random search algorithm for long e ough. The evolution from one generation to the next one invol ves mainly three steps: fitness evaluation, selecti on and reproduction. First , the current population is evaluated using the fit ness evolution and then ranked based on their fitness. A new generation is created with the goal f improving the fitness. Simple GA uses three oper ators: reproduction, crossover and mutation. First selecti v reproduction is applied to the current populatio n s that the string makes a number of copies proportional to the ir own fitness. This results in an intermediate pop ulation. Second, GA selects “parents” from the current population wi th a bias that better chromosome are likely to be s el cted. Third , GA reproduces “children” (new strings) from selecte d parents using crossover and/or mutation operators . IV.ROUTING SOLUTION USING GENETIC ALGORITHM [1] [4] Before going to solve the given network by using th e Genetic Algorithm Tool, we have to first understa nd the basic aspects of GA. suppose we have the Static Net work as shown in fig(2). Now GA works as: first, let us consider different paths from the so urce Node to the destination node.i.e, Parent path 1: 1-3-5-7-9-10 Parent path 2: 1-2-4-7-9-8-10 Parent path 3: 1-2-4-6-8-10 Parent path 4: 1-2-3-5-6-8-10 Figure (2) Network Topology For Simulation Secondly, the paths are encoded using binary number s where each node is encoded by 4-bits, as shown in table below, Table (1) Connection between the nodes 419 Comparison of Dijkstra’s Shortest Path Algorithm wi th Genetic Algorithm for Static and Dynamic Routing Network ISSN-2277-1956 /V1N2-416-425 Thirdly, made chromosomes according to the parent p aths, where, each number is represented by its corresponding binary bits, i.e., Chromosome A: 0001 0011 0101 0111 1001 1010 Chromosome B: 0001 0010 0100 0111 1001 100

Comparison of Dijkstra’s Shortest Path Algorithm with Genetic Algorithm for Static and Dynamic Routing Network

this work presents a comparative study of two different control strategies for a flexible single-link manipulator. The dynamic model of the flexible manipulator involves modeling the rotational base and the flexible link as rigid bodies using the Euler Lagrange's method. The resulting system has one Degree-Of-Freedom (one DOF) and it provide freedom to increase the degree as well. Two types of regulators are studied, the State-Regulator using Pole Placement, and the Linear-Quadratic regulator (LQR). The LQR is obtained by resolving the Ricatti equation, in this work, we apply and compare two strategies to control the tip of the flexible link: state-feedback and linear quadratic regulator. These regulators are designed to reduce tip vibrations and increase system stability due to the flexibility of the arm.

Subhash Chandra Saini

Papers

Comparison of Dijkstra’s Shortest Path Algorithm with Genetic Algorithm for Static and Dynamic Routing Network

Comparison of Pole Placement and LQR Applied to Single Link Flexible Manipulator