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Industrial robots are widely used in the painting industry, such as automobile manufacturing and solid wood furniture industry. An important problem is how to improve the efficiency of robot programming, especially in the current furniture industry with multiple products, small batches and increasingly high demand for customization. In this work, we propose an outer loop adaptive control scheme, which allow users to realize the practical application of the zero-moment lead-through teaching method based on dynamic model without opening the inner torque control interface of robots. In order to accurately estimate the influence of joint friction, a friction model is established based on static, Coulomb and viscous friction characteristics, and the Sigmoid function is used to represent the transition between motion states. An identification method is used to quickly identify the dynamic parameters of the robot. The joint position/speed command of the robot’s inner joint servo loop is dynamically generated based on the user-designed adaptive control law. In addition, the zero-moment lead-through teaching scheme based on the dynamic model is applied to a spray-painting robot with closed control system. In order to verify our method, CMA GR630ST is used to conduct experiments. We identified the parameters of the dynamic model and carried out the zero-moment lead-through teaching experiment to track the target trajectory. The results show that the proposed method can realize the application of modern control methods in industrial robot with closed control systems, and achieve a preliminary exploration to improve the application scenarios of spray-painting robots.
In this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a Delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a Delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a Delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a Delta parallel robot.
Software engineering is as much about teamwork as it is about technology. This introductory textbook covers both. For courses featuring a team project, it offers tips and templates for aligning classroom concepts with the needs of the students' projects. Students will learn how software is developed in industry by adopting agile methods, discovering requirements, designing modular systems, selecting effective tests, and using metrics to track progress. The book also covers the 'why' behind the 'how-to', to prepare students for advances in industry practices. The chapters explore ways of eliciting what users really want, how clean architecture divides and conquers the inherent complexity of software systems, how test coverage is essential for detecting the inevitable defects in code, and much more. Ravi Sethi provides real-life case studies and examples to demonstrate practical applications of the concepts. Online resources include sample project materials for students, and lecture slides for instructors.
An old conjecture of Erdős and McKay states that if all homogeneous sets in an $n$-vertex graph are of order $O(\!\log n)$ then the graph contains induced subgraphs of each size from $\{0,1,\ldots, \Omega \big(n^2\big)\}$. We prove a bipartite analogue of the conjecture: if all balanced homogeneous sets in an $n \times n$ bipartite graph are of order $O(\!\log n)$, then the graph contains induced subgraphs of each size from $\{0,1,\ldots, \Omega \big(n^2\big)\}$.
Gambling marketing is frequently visible in the United Kingdom, especially around the national sport, soccer. Previous research has documented the frequency with which gambling marketing logos can be seen in domestic club soccer, and also the frequency of television advertising around international tournaments. The present research investigates the frequency and content of television advertising during the men’s 2020 Euro soccer tournament, a high-profile tournament shown since the industry’s voluntary “whistle-to-whistle ban” on gambling advertising came into effect. Overall, 113 gambling adverts were recorded (4.5 adverts per relevant match). Financial inducements were the most frequently shown category (56.6%), followed by adverts raising awareness of a given operator’s brand (19.5%), adverts featuring the odds on specific complex bets (18.6%), and adverts promoting safer gambling (5.3%). Adverts featured a range of safer gambling messages, with the “when the fun stops, stop” message featuring in 56.6% of adverts. This research indicates that gambling advertising remains a frequent part of the experience of watching live televised soccer in the UK, and shows how the content of this advertising was comparable to what has been seen in the previous literature.
Given a graphon $W$ and a finite simple graph $H$, with vertex set $V(H)$, denote by $X_n(H, W)$ the number of copies of $H$ in a $W$-random graph on $n$ vertices. The asymptotic distribution of $X_n(H, W)$ was recently obtained by Hladký, Pelekis, and Šileikis [17] in the case where $H$ is a clique. In this paper, we extend this result to any fixed graph $H$. Towards this we introduce a notion of $H$-regularity of graphons and show that if the graphon $W$ is not $H$-regular, then $X_n(H, W)$ has Gaussian fluctuations with scaling $n^{|V(H)|-\frac{1}{2}}$. On the other hand, if $W$ is $H$-regular, then the fluctuations are of order $n^{|V(H)|-1}$ and the limiting distribution of $X_n(H, W)$ can have both Gaussian and non-Gaussian components, where the non-Gaussian component is a (possibly) infinite weighted sum of centred chi-squared random variables with the weights determined by the spectral properties of a graphon derived from $W$. Our proofs use the asymptotic theory of generalised $U$-statistics developed by Janson and Nowicki [22]. We also investigate the structure of $H$-regular graphons for which either the Gaussian or the non-Gaussian component of the limiting distribution (but not both) is degenerate. Interestingly, there are also $H$-regular graphons $W$ for which both the Gaussian or the non-Gaussian components are degenerate, that is, $X_n(H, W)$ has a degenerate limit even under the scaling $n^{|V(H)|-1}$. We give an example of this degeneracy with $H=K_{1, 3}$ (the 3-star) and also establish non-degeneracy in a few examples. This naturally leads to interesting open questions on higher order degeneracies.
For any software project, large or small, architecture is key to managing the intrinsic complexity of software. The design of a system includes its architecture, so, more broadly, design is key to managing software complexity. Informally, architecture partitions a system into parts that are easier to work with than the system as a whole. Let us refer to the parts as architectural elements, or simply elements. With a clean architecture, we can reason about the system in terms of the properties of its elements, without worrying about how the elements are implemented.
Software engineering is the application of engineering methods to software development and evolution. Its principles and practices address three fundamental goals: discover user requirements, manage software complexity, and build quality products and services. This chapter introduces the goals, their associated challenges, and how to deal with the challenges.
The selection of a development process is one of the earliest decisions in the life of a software project. A process orchestrates the workings of a team: it guides what each role does and when in order to define and build a product. This chapter introduces the two main groupings of processes: agile and plan-driven. Agile processes are designed to accommodate requirements changes. Plan-driven processes emphasize careful upfront design. Each process class within these groupings addresses some, but not all, of the challenges faced by projects. Teams therefore customize their processes by borrowing best practices as needed. For example, all teams benefit from essentially the same testing practices.
The combination of reviews, static analysis, and testing is highly effective at detecting defects during software development. By themselves, the individual techniques are much less effective; see . Testing is covered in ; the other techniques are covered in this chapter.
A use case describes how a user interacts with a system to accomplish something of value to the user. Written in plain English, use cases are intended to be a single description that is suitable for all stakeholders.