Development Of Intelligent Controller Research Articles

A Fast Detection Method Using Anisotropic Guidance for Infrared Small Target Under Complex Scenes

The development of intelligent control and visual navigation, along with the ongoing increase in imaging distance, has led to realistic demands for weak and small target detection. However, figuring out how to reduce the interference of various complex factors while achieving a high detection rate and low false alarm rate for real-time infrared small target detection is still a significant challenge. We propose a computationally simple single-frame infrared small target fast detection method applicable to complex scenes. First, a novel anisotropy filter bank suitable for small target feature extraction is constructed and modified with a point spread function. This makes it possible to map the salient features and spatial features of the target in the spatial domain using the imaging mechanism. We then combined two sets of feature parameters using the filtered features, including isotropy measure and directional energy factors, to enable the detection of small targets. Experimental findings showed that the algorithm performs well in scenarios involving complicated clouds, urban ground, and vegetation. The software also achieved excellent real-time performance because of its straightforward computational design.

Research on a course control strategy for unmanned surface vessel

With the development of intelligent control and modern communication technology, Unmanned Surface Vessel (USV) will soon become another important unmanned platform after drones and unmanned vehicles. Unmanned vessel are easily disturbed by wind, waves and currents during navigation. Solving the problem of unmanned vessel’s course keeping control during navigation has become one of the key technologies in the research of unmanned vessel. Aiming at the course control problem of the unmanned vessel in the course of navigation, this paper firstly carried out mathematical modeling and data analysis on the dynamic model of the experimental unmanned vessel, carried out related design simulations and experiments through the constructed model, and propose an optimal control strategy based on PID controller. On this basis, the accuracy and effectiveness of the control strategy proposed in this paper are verified through a simulation platform, which can ensure the course stability of the unmanned vessel during the navigation process. Key technologies such as UAV obstacle avoidance and coordination provide a theoretical basis.

Performance Evaluation of Effective Controller for Buck-Boost DC-DC DSNIBBC

This paper deals about the development of intelligent controllers (ICs) for load voltage regulation of a double switch noninverting buck-boost DSNIBBC operated in pseudo continuous coil current (PCCC). The DSNIBBC is a kind of DC-DC converter, which converts the positive DC source voltage into the positive DC load voltage (buck to boost mode and boost to buck mode). Due to the time changing, ON/OFF characteristics along with its transient and dynamic characteristics of DSNIBBC have become nonlinear. Since the classical linear controllers are inappropriate to enhance the transient and dynamic performances of DSNIBBC, ICs are designed to improve the load control of the same DSNIBBC operated in transient and dynamic regions. In this paper, two ICs, namely, fuzzy logic controller (FLC) and neuro controller (NC) incorporated in the load voltage regulation loop are utilized. The performance comparison between the ICs and the classical proportional integral (PI) controller is discussed and validated at various regions through both software and prototype field programmable gate array [FPGA] plants. The responses are depicted to support the designed ICs.

High-Level Motor Planning Assessment During Performance of Complex Action Sequences in Humans and a Humanoid Robot

Examining complex cognitive-motor performance in humanoid robots and humans can inform their interactions in a social context of team dynamics. Namely, the understanding of human cognitive-motor control and learning mechanisms can inform human motor behavior and also the development of intelligent controllers for robots when interacting with people. While prior humans and humanoid robot studies mainly examined motion planning, only a few have investigated high-level motor planning underlying action sequences for complex task execution. This sparse work has largely considered well-constrained problems using fairly simple performance assessment methods without detailed action sequence analyses. Here we qualitatively and quantitatively assess action sequences generated by humans and a humanoid robot during execution of two tasks providing various challenge levels and learning paradigms while offering flexible success criteria. The Levenshtein distance and its operators are adapted to the motor domain to provide a detailed performance assessment of action sequences by comparing them to a reference sequence (perfect sequence having a minimal number of actions). The results reveal that (i) humans produced a large variety of action sequences combining perfect and imperfect sequences while still reaching the task goal, whereas the robot generated perfect/near-perfect successful action sequences; (ii) the Levenshtein distance and the number of insertions provide reliable performance markers capable of differentiating perfect and imperfect sequences; (iii) the deletion operator is the most sensitive marker of action sequence failure. This work complements prior efforts for complex task performance assessment in humans and humanoid robots and has the potential to inform human–machine interactions.

Deep Neural Networks Based Real-time Optimal Control for Lunar Landing

Recent research on deep learning control, a new control algorithm based on machine learning able to learn deep architectures, has shown excellent performance on robots and drones. With the development of intelligent control like deep learning and reinforcement learning, accuracy, real-time, adaptability, robustness and autonomy of control algorithm have been achieved by the intelligent controls. Traditional control methods have difficulties to achieve nice performance in complex situations. Deep learning offers powerful algorithms to real-time search near-optimal controllers of lunar landing spacecraft with nonlinear dynamics. In terms of lunar landing control system, deep architectures offer the possibility to get an approximate solution of co-state equation without time-consuming iterative process. Furthermore, real-time optimal thrust during lunar landing may be derived directly through deep neural networks. As a single infrastructure for machine learning in both production and research, TensorFlow is chosen for training the deep artificial neural networks in this paper. Numerical simulations demonstrate the effectiveness of deep neural networks. The results of deep neural networks based optimal control are contrasted with traditional optimal algorithm, whose main idea is to track the pre-designed optimal trajectory by ground station. This research provides an effective approach to cope with the lunar landing problem.

Study on Clinical Application of Intelligent Control Technology

The main research direction of intelligent control technology is analyzed and discussed in detail, including fuzzy model and basic control algorithm, fuzzy predictive control, stability analysis of fuzzy control system. The status and function of intelligent control are discussed, and the application and development of intelligent control in medicine are summarized and prospected in this paper.

Adaptive neural network fuzzy inference system for HFC processes

The paper presents the design and implementation of a fuzzy inference system (FIS) trained with adaptive neural networks for the generation of specification references in high frequency current (HFC) hardening processes. The specification references are then further used for the control of the process in obtaining the desired outcomes in terms of material hardening and resistance. The FIS is trained using data obtained from experimentation on an industrial HFC device. The trained FIS is then compared to a manually tuned FIS, resulting from expert and operator designs. The results led to the development of intelligent control interfaces in real time through the ANFIS method.

The Application of Intelligent Control in Clinical Medicine

Since the emergence of intelligent control, it has continuously developed and been applied to various fields, which has also greatly promoted the progress of clinical medicine and solved some difficult problems in clinical work. The development of intelligent control pushes the emergence and development of new technologies and new industries. It promotes a new wave of information technology, which continuously penetrates medicine. Intelligent control plays an increasingly important role, and its application in medical field is an inevitable trend. This article mainly gives examples of intelligent control in some clinical departments.

A Development of Intelligent Controller for Phase Control in Main Circuit Breaker

A Development of Intelligent Controller for Phase Control in Main Circuit Breaker

SYSTEM IDENTIFICATION OF CLAMPING FORCE CONTROLLER FOR SECONDARY PULLEY OF ELECTRO MECHANICAL DUAL ACTING PULLEY CONTINOUSLY VARIABLE TRANSMISSION (EMDAP CVT)

This paper investigates the performance of clamping force at the secondary pulley actuator of Electro-mechanical Dual Acting Pulley Continuously Variable Transmission (EMDAP CVT) using an identification technique for development of intelligent control. The implementation details are described and the experimental studies conducted in this research are analyzed. To investigate the dynamic response of the system, step input was applied to the EMDAP CVT and clamping forced was measured. The modeling of the system was developed using the Genetic Algorithm (GA). The validation and verification of the obtained model were evaluated using mean squared error (MSE) and correlation test. The performance of the nonlinear approach was compared and discussed based on MSE value. The predictive ability of the model was further observed with unseen data. The result shows that, Nonlinear ARX (NARX) model converges to an optimum solution faster with increasing of model order and the obtained dynamic model also described the system well.

Researches on the Optimal Design of Intersection Signal Lights Fuzzy Control

Fuzzy control is an important branch of intelligent control, and this paper describes the application of fuzzy pattern study in intelligent control, begins with a brief overview introduction for development of intelligent control, and then describes the main components of the fuzzy model and its characteristics and fuzzy pattern control system. Fuzzy pattern applications in the intersection signal intelligent control showed fuzzy mode control can effectively reduce the single intersection average vehicle delay time, making the intersection more unobstructed to pass.

Research of Intelligent Control of Traffic Signal

At present, in the field of intelligent control of traffic signal, most of scholars at home and abroad use fuzzy control and intelligent algorithm, such as genetic algorithm, ant colony optimization, particle swarm optimization, multi-agent, artificial neural networks, fuzzy method etc. This paper summarizes and analyzes these algorithms, points out the problems and shortcomings in the present research, puts forward the direction and trend in the future research. These works have certain directive significance to the research and development of intelligent control of traffic signal.

Development of Intelligent Control of Optimum Parameters in Deep Drawing of Sheet Metal Using Genetic Algorithm and Finite Element Methods

The main purpose of this work is to develop an intellectualized control technique on the deep drawing of rectangular pan made of AISI 304 DDQ stainless steel using genetic algorithm and finite element methods. These control methods are employed in order to investigate the most significant parameters in sheet metal forming process such as drawing force, with a view of optimizing these parameters. The genetic algorithm is used for the optimization purpose to minimize the force of the deep drawing process and to investigate the roles of other parameters. Experimental results show that these combinations of control system can cover a wide range of both materials and influential forming parameters automatically. The results further confirm that the developed system is effective and valid alternative for quick responsible control system with high flexibility.

Adaptive PID Temperature Controller Based on Fuzzy Logic

This paper develops a fuzzy logic controller (FLC) for the temperature. Temperature is a very popular parameter in industry, which leads an important role in industry process control. There is nonlinearity, time lag, inertia, uncertainty and so on .The advantage of this method is that the proposed FLC has the characteristics of robustness. In order to control systems that contain nonlinearities or uncertainties, control strategies must deal with the effects of these. With the development of intelligent control, Fuzzy-PID control based on Fuzzy control develops more and more popular and makes a great process in temperature control. The whole resistance furnace temperature control system design is based on PLC which Rockwell laboratory supplies and related software and hardware.

Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity.

QoS Parameters of Grid Services and Service Matchmaking

The standard sample space and the nonstandard sample space which is separately composed by standard sample and the nonstandard sample are called general sample space. QoS parameters of grid services and service matchmaking are discussed. On the basis of the ability of service providers, by Algorithm Prim, this method succeeds in calculating a set of balanced expectation values of QoS. its application on the development of intelligent controller was discussed.

Research on Indeterminacy Causal Inductive Automatic Reasoning Mechanism

The generalized inductive logic causal model that can synthetically process fuzzy indeterminacy and random indeterminacy was proposed in this paper. On this basis, the new logic indeterminacy causal inductive automatic reasoning mechanism based on fuzzy state description was put forward. At the end of this paper its application on the development of intelligent controller was discussed.

Development of Intelligent Controller with Virtual Sensing

In many industrial plants, some key variables cannot always be measured on-line and for the purpose of control, an alternative of sensing system is required. This paper is concerned with a development of an alternative intelligent control strategy, which is an integration between the neuro-fuzzy based controller and virtual sensing system. This allows an immeasurable variable to be inferred and used for control. The virtual sensor is composed of the Diagonal Recurrent Neural Network (DRNN) for plant modeling and the Extended Kalman Filter (EKF) as the estimator with inputs from DRNN. The integration between virtual sensor and the controller enables a development of an on-line control scheme involving the immeasurable variable. The real -time implementation demonstrates the applicability and the performance of the proposed intelligent control scheme, especially in dealing with nonlinear processes.

Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute

This is an outline of research in neural networks for feedback control done since the mid 1990s at the Automation and Robotics Research Institute (ARRI) of The University of Texas at Arlington (UTA). It shows how the developments of Intelligent Control Systems based on neural networks have followed three main generations. This statement provides a short, broad-brush perspective on the development of intelligent neural feedback controllers.

Robust stability analysis of switched Hopfield neural networks with time-varying delay under uncertainty

With the development of intelligent control, switched systems have been widely studied. Here we try to introduce some ideas of the switched systems into the field of neural networks. In this Letter, a class of switched Hopfield neural networks with time-varying delay is investigated. The parametric uncertainty is considered and assumed to be norm bounded. Firstly, the mathematical model of the switched Hopfield neural networks is established in which a set of Hopfield neural networks are used as the individual subsystems and an arbitrary switching rule is assumed; Secondly, robust stability analysis for such switched Hopfield neural networks is addressed based on the Lyapunov–Krasovskii approach. Some criteria are given to guarantee the switched Hopfield neural networks to be globally exponentially stable for all admissible parametric uncertainties. These conditions are expressed in terms of some strict linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate our results.