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Information technology and computer technology in every field of society are playing a huge role, in the field of automatic production, computer technology with its strong operation and calculation ability and data processing ability, further promote the centralized control of automatic assembly line. In the future, it is an inevitable trend for computer system to replace human control. The application of computer technology can not only greatly improve the control efficiency, but also save manpower and material resources to a greater extent, and promote the progress of production level as a whole. Computer control technology is a computer-based technology. It is of great significance to analyze the application of computer control technology in automatic production line. Just as technology has changed our lives, it has also changed the way we produce and work. Nowadays, with the development of computer technology, network technology, information technology and other high and new technologies, all fields of the society have undergone earth-shaking changes, including the field of industrial production. Now the industrial production has realized the automatic assembly line production, and in order to better control the automatic assembly line production, in order to ensure its production efficiency, safety and stability, it must be applied to the computer control technology. At the same time, the application of computer control technology also helps to save human and material resources, thus saving the overall production cost and improving the economic efficiency of enterprises.

This paper presents an adaptive control method for a class of uncertain strict-feedback switched nonlinear systems. First, we consider the constraint characteristics in the switched nonlinear systems to ensure that all states in switched systems do not violate the constraint ranges. Second, we design the controller based on the backstepping technique, while integral Barrier Lyapunov functions (iBLFs) are adopted to solve the full state constraint problems in each step in order to realize the direct constraints on state variables. Furthermore, we introduce the Lyapunov stability theory to demonstrate that the adaptive controller achieves the desired control goals. Finally, we perform a numerical simulation, which further verifies the significance and feasibility of the presented control scheme.

With the in-depth development of high-tech industries, especially in the fields of production, manufacturing, aviation, and medical care, most of the work needs to be accomplished with the help of machines. As a high-tech product, a robotic arm plays an irreplaceable role in high-risk and high-precision engineering, such as arc welding, spraying, and assembly. At the same time, with the increasing requirements of scientific and technological production processes, robotic arm tasks are becoming increasingly complicated. Robotic arm trajectory tracking control in industry also has increasingly higher standards. Furthermore, external interference sources invariably affect the robotic arm control system when it is in operation. Therefore, existing manipulator control systems can no longer meet the requirements of industrial production. This paper aims to realize the tracking control of the trajectory of a robotic arm through a neural network algorithm. This research offers an adaptive neural network control method to solve the manipulator trajectory tracking control problem. To increase the control effect and overall performance of the manipulator, a neural network is employed to address the uncertainty in the control system as well as the interference of external elements. Experiments reveal that a neural network-based manipulator trajectory control and tracking system can effectively regulate the manipulator's operation and improve its overall performance.

This review aims to exploit a study on different benchmark test functions used to evaluate the performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH optimization techniques is evaluated with the different sets of mathematical benchmark test functions and various real-world engineering design problems. These benchmark test functions can help to identify the strengths and weaknesses of newly proposed MH optimization techniques. This review paper presents 215 mathematical test functions, including mathematical equations, characteristics, search space and global minima of the objective function and 57 real-world engineering design problems, including mathematical equations, constraints, and boundary conditions of the objective functions carried out from the literature. The MATLAB code references for mathematical benchmark test functions and real-world design problems, including the Congress of Evolutionary Computation (CEC) and Genetic and Evolutionary Computation Conference (GECCO) test suite, are presented in this paper. Also, the winners of CEC are highlighted with their reference papers. This paper also comprehensively reviews the literature related to benchmark test functions and real-world engineering design challenges using a bibliometric approach. This bibliometric analysis aims to analyze the number of publications, prolific authors, academic institutions, and country contributions to assess the field's growth and development. This paper will inspire researchers to innovate effective approaches for handling inequality and equality constraints.

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

This paper introduces a new meta-heuristic technique, named geometric mean optimizer (GMO) that emulates the unique properties of the geometric mean operator in mathematics. This operator can simultaneously evaluate the fitness and diversity of the search agents in the search space. In GMO, the geometric mean of the scaled objective values of a certain agent’s opposites is assigned to that agent as its weight representing its overall eligibility to guide the other agents in the search process when solving an optimization problem. Furthermore, the GMO has no parameter to tune, contributing its results to be highly reliable. The competence of the GMO in solving optimization problems is verified via implementation on 52 standard benchmark test problems including 23 classical test functions, 29 CEC2017 test functions as well as nine constrained engineering problems. The results presented by the GMO are then compared with those offered by several newly proposed and popular meta-heuristic algorithms. The results demonstrate that the GMO significantly outperforms its competitors on a vast range of the problems. Source codes of GMO are publicly available at https://github.com/farshad-rezaei1/GMO .

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

We consider the problems of learning the optimal action-value function and the optimal policy in discounted-reward Markov decision processes (MDPs). We prove new PAC bounds on the sample-complexity of two well-known model-based reinforcement learning (RL) algorithms in the presence of a generative model of the MDP: value iteration and policy iteration. The first result indicates that for an MDP with N state-action pairs and the discount factor γ ∈[0,1) only O ( N log( N / δ )/((1− γ ) 3 ε 2 )) state-transition samples are required to find an ε -optimal estimation of the action-value function with the probability (w.p.) 1− δ . Further, we prove that, for small values of ε , an order of O ( N log( N / δ )/((1− γ ) 3 ε 2 )) samples is required to find an ε -optimal policy w.p. 1− δ . We also prove a matching lower bound of Θ ( N log( N / δ )/((1− γ ) 3 ε 2 )) on the sample complexity of estimating the optimal action-value function with ε accuracy. To the best of our knowledge, this is the first minimax result on the sample complexity of RL: the upper bounds match the lower bound in terms of  N , ε , δ and 1/(1− γ ) up to a constant factor. Also, both our lower bound and upper bound improve on the state-of-the-art in terms of their dependence on 1/(1− γ ).

The development of a distributed trajectory-tracking control strategy that is independent of velocity measurements is critical to achieving finite-time tracking control of autonomous underwater vehicle (AUV) systems. In this study, a group of heterogeneous AUV systems with intermittent communication links is considered and a finite-time trajectory-tracking control strategy is developed. The strategy includes two observers and one controller proposed for each follower-AUV. The first observer, a hybrid finite-time observer, estimates the state of the leader, whereas the second observer, which relies only on the position measurement, is proposed to estimate the states of the follower-AUV itself. In addition, a distributed trajectory-tracking controller is designed using the states estimated by the intermittent communication network even without velocity measurements. A homogeneous technique is utilized to prove that all followers can track the leader in a finite time. Finally, the effectiveness of the developed finite-time tracking control strategy is illustrated by numerical simulations.