Hierarchical Intelligent Control System Research Articles

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.

Hierarchical intelligent control of automobile anti-lock braking system

A new method using difference of the peak values of wheel velocity to calculate reference vehicle velocity and ref- erence slip ratio is proposed,which is based on the analyses of the mechanics characteristics between the tire and the road when the vehicle is being braked.To solve complex control problem of automobile anti-lock braking system(ABS)in dif- ferent conditions,a hierarchical intelligent control(HIC)system is investigated.It includes running control level,parameters adjusting level and organization coordination level.According to the instantaneous performance index of reference slip ratio,a human-simulation intelligent controller(HSIC)aiming at con- trolling reference slip ratio is designed in running control level. The characteristic model,control modes set and reasoning rules set are built.In parameter adjusting level,to decrease the deft- ciency of reference slip ratio control,a method is pointed that modifies the calculation values of HSIC pursuant to wheel an- gle deceleration.In coordination level,a fuzzy intelligent con- troller with the inputs of wheel deceleration and reference slip ratio is designed to implement road automatic identification. Then four wheels braking coordination control rules are also given.The simulation on HIC system of ABS is developed with Matlab.An automotive ABS test and control system is con- structed and the road tests under different conditions are com- pleted according to the international standards.Compared to traditional logic gate control,test results show that the HIC is an effective new ABS control method with better braking sta- bility,adaptiviness and control accuracy.

AI approaches to identification and control of total plant production systems

Recent progress in intelligent control techniques has enabled complex systems such as cultivation and fruit-storage processes to be dealt with. This paper presents the application of a hierarchical intelligent control system, which consists of an expert system and an optimizer based on neural networks and genetic algorithms, for optimizing a total plant production process. Environmental factors in the cultivation and storage processes are optimally controlled, based on the physiological status of the plant (or fruit). The expert system determines suitable environmental setpoints throughout growth, and the optimizer determines optimal environmental setpoints during important growth stages and during storage, based on plant responses. In the optimizer, neural networks were used for the identification of plant responses to environmental factors, and genetic algorithms were used to search for the optimal environmental setpoints through the simulation of the identified models. Optimal setpoints of the nutrient concentration in hydroponic tomato cultivation and optimal setpoints of the temperature during tomato storage were determined using this control technique.

A fuzzy logic intelligent control system paradigm for an in-line-of-sight leader-following HMMWV

A hierarchical intelligent control system paradigm for a vision-based autonomous driving scheme is presented for a leader-following HMMWV. This article shows how fuzzy logic is employed to represent knowledge at the organization level, how to resolve conflicting perceived information at the coordination level, and how to carry out the best action for the vehicle at the execution level. After introducing the methodology, simulation and experimental aspects for realizing and testing the scheme are discussed. ©1997 by John Wiley & Sons, Inc.

Intelligent control for a plant production system

For the optimization of long-term plant growth in hydroponics, this paper proposes a hierarchical intelligent control system consisting of an expert system and a hybrid system based on genetic algorithms and neural networks. These two control systems are used appropriately, depending on the plant growth. The plant growth is controlled by the nutrient concentration of the solution. The expert system was used for determining the appropriate setpoints of nutrient concentration through the whole of the growth stages, and the hybrid system for determining the optimal setpoints of nutrient concentration which maximize TLL/SD (TLL: Total leaf length, SD: Stem diameter) only during the initial growth (seedling) stage. In the hybrid system, TLL/SD as affected by nutrient concentration was first identified using neural networks and then the optimal value was determined through simulation of the identified model using genetic algorithms. The setpoints from the expert system were almost similar to those used by a skilled grower. Also, the setpoints from the hybrid system increased the TLL/SD. Thus, this intelligent control technique allowed the optimization of both long-tern and short-term plant growth to be realized. This shows that this control technique is suitable for the optimization of such complex and long-terns processes as the plant-cultivation process.

Intelligent Control for Plant Production System

For optimization of plant growth in hydroponics, a hierarchical intelligent control system consisting of an expert system and a hybrid system based on genetic algorithms and neural networks is proposed. Plant growth is controlled by nutrient concentration of the solution. An expert system is used for normal control through the whole growth stage. On the other hand, a hybrid system is used for optimization control during an important growth stage. For optimization, the ratio of total leaf length to stem diameter (TLL/SD), as affected by nutrient concentration, was first identified using neural networks and then the optimal 4-step setpoints of nutrient concentration which maximize the TLL/SD was determined through simulation of the identified model using genetic algorithms. The results show that this control technique is suitable for optimization of such complex systems as plant growth systems.

Hybrid Control of a Robotis Manipulator by the Neural Network Model. 6th Report. Hierarchical Intelligent Control System.

In this paper, we present a new scheme for intelligent control of robotic manipulators. This scheme is an integrated approach of the Neuromorphic and Symbolic control, including the applied neural network for the servo control and the knowledge-based approximation. The neural network in the servo level is the numerical manipulation, while the knowledge-based part is the symbolic manipulation. In Neuromorphic Control, the neural network compensates the nonlinearity of the system and the uncertainty in environment. The knowledge-base part forms symbolic control strategy for the servo level. This is control system analogous to the human cerebral control structure.