Hierarchical Intelligent Control Research Articles

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Vehicles' perception capability of environmental situations has been enhanced in the connected environment. However, the utilization of abundant and diverse traffic information poses a challenge in the formulation of energy-efficient strategies for current connected vehicles. To address this challenge, a hierarchical intelligent energy-saving control strategy for fuel cell hybrid buses based on traffic flow prediction is proposed. Diverging from traditional approaches that rely on surrounding vehicles status information, a more macroscopic perspective is introduced by incorporating traffic flow prediction information into the formulation of energy-saving control strategies for the first time, which enhances the adaptability of strategies. At the upper layer, a multi-objective intelligent eco-driving control strategy is designed, encompassing driving safety, energy consumption costs, traffic efficiency, and ride comfort. At the lower layer, an intelligent energy management strategy is developed to reduce hydrogen consumption and maintain stable battery state of charge. Simultaneously, action variables serve as the information bridge between the upper and lower layer strategies, and comprehensive analyses and validations of strategic performance are conducted from various perspectives. The research findings indicate that the introduction of traffic flow information enhances the cognitive capabilities of the intelligent system towards the traffic environment with little impact on the convergence process. The model excels in energy efficiency, driving smoothness, and passenger comfort compared to the baseline model, while also having the opportunity to surpass the traffic efficiency of the IDM model. The developed energy management strategy demonstrates an energy-saving benefit of 92.07 % of the offline optimal benchmark, and closely approaches the theoretical optimal performance of MPC with a prediction horizon of 5s. Additionally, the proposed strategy demonstrates high computational efficiency and effectively mitigates the degradation of the vehicle lifespan, making it conducive to real-time online applications.

Research on layered control of path tracking for unmanned industrial vehicles based on fully hydraulic steering leakage compensation

Industrial vehicles work in complex terrain, the variable centre of mass position, leakage nonlinearity of full hydraulic steering and other issues lead to poor path tracking stability and accuracy. In this paper, an intelligent hierarchical controller for industrial vehicles’ path tracking is designed with full hydraulic steering leakage compensation, including an upper decision layer and a lower execution layer. The upper decision layer observes the pavement-tyre adhesion coefficients through an extended Kalman filter algorithm, and uses the real-time observations of the adhesion coefficients to establish a variable constraint control for the linear time-varying MPC, and thus performs the path tracking control. The lower actuator layer receives the upper layer’s target steering wheel angle output and performs steering operations. Based on the establishment of a mathematical model of full hydraulic steering considering the leakage characteristics, it analyses the leakage disturbance factors and constructs a fuzzy feed-forward steering leakage compensation controller to compensate for the leakage disturbances in real time for path tracking steering. Simulation and experimental results show that the lateral acceleration, sideslip angle, tyre side slip angle and tracking error of intelligent industrial vehicles under different loads and working conditions are improved by more than 32.4%, 35.8%, 40.2% and 45.8% respectively, and the designed hierarchical controller for the path tracking of intelligent industrial vehicles, which considers the compensation of all-hydraulic steering leakage, can effectively improve the path tracking stability and tracking accuracy of intelligent industrial vehicles under different loads and working conditions.

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved.

A hierarchical intelligent control strategy for greenhouse gas reduction in wastewater treatment process of paper mill

Due to the huge amounts of wastewater discharge and the high pollution loads in papermaking industry, many greenhouse gases (GHG) are emitted in the papermaking wastewater treatment process. The wastewater dissolved oxygen (DO) control has been considered as the most cost-effective control solution for GHG reduction in wastewater treatment plants (WWTP). However, the competition between contaminant removal efficiency and GHG reduction hinders the drastic reduction of GHG emissions from WWTP. In this study, based on the established integrated GHG emission model, explicitly considering the total GHG reduction targets on the premise of effluent compliance, an intelligent control scheme has been developed for an activated sludge process in a paper mill. Regarding DO as the controlled variable, the proposed hierarchical optimal proportional-integral (HOPI) control scheme was established consisting of three layers: 1) Layer 1 for the effluent quality estimation, 2) Layer 2 for the optimal DO set point determined by genetic algorithm with the influent variations to obey discharging norms and reduce GHG emissions, 3) Layer 3 for the DO tracking proportional integral (PI) control with the controller parameters adjusted by the back propagation neural network to track the dynamically optimized DO set points. The simulation results showed that, compared with the open-loop (OL) operation (averaged aeration, 10/h), the proposed HOPI control (averaged aeration, 7.9/h) reduced the GHG emissions by 12.54% under the premise of discharging norms, which was mainly attributed to the reduction of the aeration electricity consumption. In contrast, the PI control (averaged aeration, 12.9/h) increased the GHG emissions by 7.48% compared with the OL operation. Thus, the proposed HOPI control strategy has demonstrated potential for the application of GHG reduction in industrial WWTPs.

Research on Intelligent Vehicle Path Tracking with Subsystems Based on Multimodel Intelligent Hierarchical Control Theory

In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system.

A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

It is difficult to coordinate the various processes in the process industry. We built a multiagent distributed hierarchical intelligent control model for manufacturing systems integrating multiple production units based on multiagent system technology. The model organically combines multiple intelligent agent modules and physical entities to form an intelligent control system with certain functions. The model consists of system management agent, workshop control agent, and equipment agent. For the task assignment problem with this model, we combine reinforcement learning to improve the genetic algorithm for multiagent task scheduling and use the standard task scheduling dataset in OR-Library for simulation experiment analysis. Experimental results show that the algorithm is superior.

COMPLEX-SHAPED PARTS GRINDING TECHNOLOGY INFORMATION ENSURING

A method of computer-aided design and manufacture of complex-shaped parts of machines and implants from difficult-to-machine materials (titanium, cobalt-chromium alloys, zirconium dioxide, etc.) has been developed, based on the principles of building an integrated CAD/CAM/CAE system of computer-aided designing and a hierarchical intelligent numerical control system. It is shown that kinematical mechanisms created over the past several centuries do not allow reproducing with the required accuracy the joints movement of living organisms for their use in biomedical implantation technologies. Therefore, the worn out joints of living organisms are reconstructed by adding complex-shaped parts from these difficult-to-machine materials. Information about the geometric shape of these parts (3D model) at the pre-production stage is obtained using modern methods of computed tomography and magnetic resonance imaging, and at the production stage the actual location of the stock grinding allowance is measured by laser (or tactile) scanning. To reduce the unevenness of the position of the grinding stock allowance, the workpiece of a complex-shaped part before grinding is oriented in the coordinate system of a CNC machine based on the established criterion for minimizing the allowance. An example of such orientation of the gear workpiece is given. This workpiece is measured with a Renishaw tactile probe on the left and right sides of the gear valleys before gear grinding. Both the minimum allowance on the left and right sides of the valleys and the difference between them are determined, and then additionally the gear wheel blank is rotated in the appropriate direction to align these minimum values detected. In turn, the aligned minimum allowances, should be sufficient to compensate for the influence of technological factors from the previous operation and the error in setting the workpiece for this operation. For complex-shaped implants, such an additional orientation is performed, for example, according to algorithms for ensuring the minimax value of the allowance.

Smart Grid Network Resource Scheduling Algorithm Based on Network Calculus

Smart grid technology is further developed on the basis of distributed power grids and micro-grids. The communications network is an indispensable part of the smart grid. How to achieve real-time accurate transmission of various types of smart information is a technical guarantee for smart grids. This paper analyze data of smart grid bases on the demand of net resource, and adopts the idea of priority scheduling to draw on the advantages of integrated services and differentiated services to design a time-sharing polling scheduling strategy. In this strategy, the algorithm classify the network data, Using network calculus theory to analyze the optimal length of the queue satisfying the upper bound of delay, According to the degree of network congestion perception, the credibility of the data is calculated, and non-adaptive flows are quickly identified. The strategy ensure that the data is able to transfer to the user quickly and lively depend on its needs, So ensure the real-time transmission of energy supply & consumption information of active distribution network, realize multi-source collaborative optimization scheduling and distributed hierarchical intelligent control management. The algorithm can effectively ensure the demand of high-priority services, making the overall performance of the smart grid communication system data such as real-time and accuracy optimized.

A Research on Multimedia Traffic Streaming Load Balancing Scheme for IPTV over E2E based Integrated Wireless Access Networks

Multimedia services should be smoothly provided high quality service that satisfies the QoS (Quality of Service), QoE (Quality of Experience) focused on multimedia contents in an HD class and many of selected channels applying Multicast between the backbone and access. But now, there are several problems such as channel delay-time, network bandwidth limit, related traffic distribution control, and so on. So, as providing 2D, 3D multimedia data and using MPEG4 that realizes the optimized effect in a limited Bandwidth to boost safe synchronization and superior efficiency between the present video and network, we suggest an efficient network bandwidth control technique through HIASCR (Hierarchical Intelligent Access Control Router) which is a solution for network bandwidth control technique that may occur when many users access a high-definition service in a HD class at the same time as well as trend of video compression that can decrease cost for high-definition and network efficiency. This technique transmits floating high-definition data to many users depending on number of users and we offer a method to prevent the network load.

Hierarchical Intelligent Alignment Control of Air Spring Vibration Isolation System of Ship Propulsion Plant

Alignment control of propulsion plant air spring vibration isolation system is a complicated Multi objects control problem. In this paper a new hierarchical intelligent control method of alignment control is brought out. Known from current hierarchical intelligent control methods with three layers, this method consists of task layout layer, control decision-making layer, control layout layer and control executive layer. The complicated alignment control task is decomposed into four hierarchical layers to reduce complexity. The control system has good control convergence capability and fast convergence speed. Test results validate the feasibility of this method.

Intelligent hierarchical robot control for sewing fabrics

In this paper an intelligent hierarchical controller for the robotized sewing of two plies of fabrics is presented. The proposed system is based on the concept: fabric properties estimation - tensional force determination - sewing - adaptation. A new methodology for integrating the tensile test of fabrics into the robotic sewing station using the sewing machine is presented. The output of this test is the estimation of the fabrics extensibility, which is fed to the next level of decision making to determine the appropriate fabric tensional force that should be applied during the sewing process. Computational intelligence methods (fuzzy logic and neural networks) have been used throughout the hierarchical structure of the controller. The present research is focused on the concept of using qualitative properties of the fabrics and the processing of qualitative and quantitative knowledge in different levels of the introduced hierarchical system. The proposed system is flexible, adaptable and robust enough to sew a wide range of unknown double ply of fabrics as it is shown by the test results. It has also the capability of on-line and endless training in order to be able to respond, handle and sew new types of fabrics. Seams that are produced by the robot and a human operator for joining two pieces of fabrics are presented and compared.

Hierarchical Simulated Annealing-Reinforcement Learning Energy Management for Smart Grids

For energy management problems in smart grid, a hybrid intelligent hierarchical controller based on simulated annealing (SA) and reinforcement learning (RL) is proposed. The SA is used to adjust the parameters of the controller. The RL algorithm shows the particular superiority, which is independent of the mathematic model and just needs simple fuzzy information obtained through trial-and-error and interaction with the environment. By means of learning procedures, the proposed controller can learn to take the best actions to regulate the energy usage for equipments with the features of high comfortable for energy usage and low electric charge meanwhile. Simulation results show that the proposed load controller can promote the performance energy usage in smart grids.

Hierarchical Intelligent Control System and Its Application to the Sintering Process

This paper presents a hierarchical intelligent control system (HICS) based on soft-sensing of burn-through point(BTP) and vertical sintering speed (VSS) for the sintering process, which has a two-level hierarchical configuration (intelligent control level and basic automation level), and is being used in an iron and steel plant. At the intelligent control level, first, BTP and VSS soft-sensing models are established, respectively. Next, the BTP mechanical control model and fuzzy controller are designed. The basic automation level contains a distributed control system (DCS), which performs process control including stable tracking control of the strand velocity; and a communication interface, which enables the intelligent controller and the DCS to exchange data on process information. The real-world application results show that the system not only sufficiently suppresses the fluctuation, but also effectively improve the utilization factor of the sintering machine and the quality and quantity of the sinters.

Human Simulated Intelligent Control for Permanent Magnet Synchronous Motor

This paper proposes a new speed control scheme for a Permanent Magnet Synchronous Motor(PMSM) by means of multi-mode control method,Human Simulate Intelligent Control. Similar to hierarchical intelligent control system,the proposed controller for PMSM is composed of three sections,Task Adapter Level (TA), Self-Tuning Level (ST) and Master Controller Level (MC).Simulation results clearly show that the controller guarantees the speed tracking errors asymptotically converge to zero despite time varying reference speed and load torque disturbance variation.Numerical simulations reveal the superiority of the proposed controller to traditional PI.

Vehicle Active Chassis Integrated Control Based on Multi-Model Intelligent Hierarchical Control

According to the characteristics of the vehicle active chassis system, multi-model system including Electric Power Steering System (EPS), Anti-lock Braking System (ABS) and Semi-active Suspension system (SAS) is established. Then, using the strategy of intelligent hierarchical control, the coordinated controller of active chassis system is designed. The bottom layer controller includes 3 separate controllers, i.e., suspension, steering and braking system controllers. They are used to carry out different control tasks and achieve performance indexes of subsystems. The upper layer coordinated controller is used to judge the vehicle states. At the same time, combined with the vehicle chassis coordinated control logic and the characteristics of feed-back information coming from the bottom controllers, the upper coordinated controller make whole coordination and control decision-making to vehicle active chassis subsystems. The simulation results show that the intelligent hierarchical control can improve the vehicle operational performances effectively.

Soft Computation Using Artificial Neural Estimation And Linear Matrix Inequality Transmutation For Controlling Singularly-Perturbed Closed Time-Independent Quantum Computation Systems, Part A: Basics And Approach

Abstract This article presents basic background and approach methods that are needed for implementing a new method of intelligent control for time-independent quantum computing systems. This includes basic background on quantum computing, supervised neural networks, linear matrix inequalities, and model order reduction. The presented basics will be utilized in the second part of the paper for applying a new implementation of intelligent hierarchical control method within quantum computing where the obtained results are satisfying for the robust control of time-independent closed quantum computing systems.

Soft Computation Using Artificial Neural Estimation And Linear Matrix Inequality Transmutation For Controlling Singularly-Perturbed Closed Timeindependent Quantum Computation Systems, Part B: Hierarchical Regulation Implementation

Abstract A new method of intelligent control for time-independent closed quantum computation systems is introduced in this second part of the paper. The goal is to apply a new implementation of intelligent hierarchical control method within quantum computing systems where the obtained results are satisfying for the robust control of time-independent quantum computations. The new method utilizes supervised recurrent artificial neural networks (ANN) to estimate parameters of the [ A ]transformed system matrix After system matrix estimation is performed, linear matrix inequality (LMi) is used to detemvne the permutation matrix [P] so that a complete system transmutation {[ B ], [ C ], [ D ]} is accomplished. The transformed system model is then reduced using singular perturbation and state feedback control is implemented for system performance enhancement. In quantum computing and mechanics, a closed system is an isolated system that can’t exchange energy or matter with its surroundings and doesn’t interact ...

A Model of Construction Quality Intelligent Control for Large-Scale Complex Project

This paper analyzes the characteristics of the construction quality control in large-scale complex Projects ( LCP) and points out the deficiencies of the traditional quality control in the general quality control in LCP. To meet the demand of good general quality control in LCP, based on the technique of Hierarchical Intelligent Control(HIC), a Quality Intelligent Control Model(QICM), was founded in the paper, which gives the quantity control index value K and the quantity standard value H in the stability of QICM by using entropy function evaluation methodology and solves the problems existing in LCP that general construction quality could hardly be described in quantity, and consequently offers the new ways of quality control in LCP.

AN INTELLIGENT CONTROL ARCHITECTURE FOR ADAPTIVE SERVICE-BASED SOFTWARE SYSTEMS

Service-oriented architecture (SOA) for distributed computing has become increasingly popular due to the big advantage that distributed applications can be rapidly synthesized with the needed services provided by various service providers through heterogeneous networks. Systems based on SOA are called Service-based Systems (SBS), and a special variety of SBS, namely the Adaptive Service-Based Systems (ASBS), is aimed to be adaptable to constantly changing user requirements, environments and resource constraints. An important and difficult issue is how to design and develop ASBS to satisfy multiple QoS requirements in an open dynamic environment. In this paper, inspired by the underlying principle of hierarchical intelligent control, a three-layer architecture for developing and deploying ASBS is presented to address this issue. Compared with existing architectures for SBS, the advantage of using our architecture is that it provides the flexibility for system designers to adopt different control based approaches to guarantee user requirements and satisfy resource constraints at different levels of the system. Moreover, our architecture enables the system to take hierarchical adaptation actions at runtime to avoid possible violation of user requirement or resource constraint. An example is given to illustrate how to adopt our architecture to guide the design of a simple ASBS, and preliminary experimental data are presented to demonstrate the feasibility of developing ASBS based on our architecture.

Operating-State-Based Intelligent Control of Combustion Process of Coke Oven

This paper describes a hierarchical intelligent integrated control method for controlling the combustion process in a coke oven. A key feature is the determination of the operating state of the oven from an analysis of the characteristics of the process. The system contains three layers: a decision layer, a temperature optimization and control layer, and a process control layer. An information fusion method is used in the decision layer to determine the operating state of the combustion process. The control strategy uses an outer and an inner loop. The outer loop uses fuzzy controllers to adjust the temperature, and to generate proper settings for the gas flow rate and air suction power for the inner loop. The parameters of the outer loop controllers are tuned by a multiple-objective optimization method with an adaptive genetic algorithm. The inner loop controllers keep the temperature of the coke oven in the proper range. A switching control strategy is used to select a suitable controller for the current operating state.