Intelligent PID Research Articles

High-order sliding modes and intelligent PID controllers: First steps toward a practical comparison

AbstractThis communication is devoted to a “practical” comparison between high-order sliding modes and the recently introduced model-free control. The perfect knowledge of the relative degree of the output variable, which is a standard assumption for sliding modes, is assumed here. Our comparisons are based on two concrete case-studies and on numerous computer simulations. The smoothness of the input variables, the robustness with respect to noises and the straightforward extendibility of the model-free controllers to MIMO systems are highlighted.

Event driven intelligent PID controllers with applications to motion control

AbstractA novel type of reduced complexity controller is proposed. It is the combination of model free control and event triggered control. The robustness of model free control, especially for badly known dynamics, is added to the event based scheme. The performances of the proposed method are illustrated in two motion controls, vehicular longitudinal control and quadrotor control. Comparisons with existing control schemes are also proposed.

Revisiting some practical issues in the implementation of model-free control

AbstractThis paper simplifies several aspects of the practical implementation of the newly introduced model-free control and of the corresponding intelligent PID controllers (M. Fliess, C. Join, “Model-free control and intelligent PID controllers: towards a possible trivialization of nonlinear control?,” 15th IFAC Symp. System Identif, Saint-Malo, 2009). Four examples with their computer simulations permit to test our techniques.

Design and Implementation of Intelligent PID Control System of High-efficiency Comber

The home-made combers mainly are mechanical. The comber movement system has some complex factors such as strong coupling, multi-changing and nonlinearity. Thus, only using the traditional methods can’t implement a comprehensive monitoring on the combers, and this has blocked the further development of China's textile industry to a great extent. Based on these unfavorable factors, the authors put forward an intelligent fuzzy control method in this paper, and analyze the technological process and dynamic characteristics of the combers and hence establish the control system model for the transmission part. In this paper, through the characteristics and operation flow of the combers, the research and development of the intelligent PID system are introduced. The development of the intelligent PID system of the high-efficiency comber can improve the automatic control technology and also can acquire the considerable economic benefits. Therefore, it has a significant promotion role in the technical innovation of the traditional textile industry.

Control basado en PID inteligentes: aplicación al control de crucero de un vehículo a bajas velocidades

In spite of its limitations, the most widespread industrial control technique is still today PID control. In this article, a new approach, intelligent PID based control (i-PID), will be presented. It takes advantage of its strength, while it improves one of its main weak points: the loss of performance in presence of non-linear terms or unmodeled dynamics. To show the i-PID behavior it has been implemented on a real application, the robust cruise control of an experimental vehicle in urban environments.

Hybrid GA–BF based intelligent PID controller tuning for AVR system

This paper deals with hybrid system (GA–BF) based on the conventional GA (Genetic Algorithm) and BF (Bacterial Foraging) which is the social foraging behavior of bacteria. A variety of test function is introduced and simulated to illustrate the characteristics and performance by mutation, crossover, variation of step size, variation of chemotactic step, and variation of lifetime of bacteria in the proposed hybrid system GA–BF. The simulated results represent that the proposed method is highly satisfactory. This approach provides us with novel hybrid model based on foraging behavior and also with a possible new connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces.

Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system

This paper explores a comparative performance study of two new classes of particle swarm optimization techniques, one with velocity update relaxation (VURPSO) and the other based on novel position, velocity updating strategy and craziness (CRPSO). Both VURPSO and CRPSO highly enhance searching ability. Genetic algorithm (GA) is considered for the sake of comparison. Finally, it is revealed that while applying in two power systems applications (PID controlled AVR system, PSS controlled AVR system), VURPSO exhibits better transient performance than CRPSO/GA. For on-line, off-nominal conditions, Takagi Sugeno fuzzy logic is applied to obtain on-line responses for both the system models.

Control parameter tuning of magnetic bearing PID based on Matlab-Simulink-SISO and SRO

The improved neuron intelligent PID controller for magnetic bearing was described in this paper which combined Artificial Neural Networks(ANNs) technique with traditional PID controller and adjusted PID parameters automatically to reach adaptive effect according to its improved learning rules.The application of the controller in a real magnetic bearing system confirmed that the controller can overcome its significant drawback such as high nonlinearity and inherent instability.Simulation results by M-file in Matlab show that improved neuron PID controller can achieve better performance and show a number of advantages over traditional PID controller.

Hybrid intelligent PID control design for PEMFC anode system

Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.

3-D Numerical analysis on heating process of loads within vacuum heat treatment furnace

A 3-D numerical heat transfer model integrated with an intelligent PID temperature control subroutine has been developed and employed in the computational simulation of the heating process of typical load within vacuum heat treatment furnace. The transient temperature field within the load was obtained and the relationship among thermal hysteresis time, maximal temperature difference, heating temperature and heating rate was analyzed in detail. The results indicate that the maximal temperature difference within the load, as well as the thermal hysteresis time, decreases with preheating temperature, and increases with heating rate. A set of qualitative relationship guiding the design of thermal schedule for vacuum heat treatment was proposed and thus an optimum heating process was achieved, which could not only reduce the heating time considerably, but also save energy effectively. The simulated heating curves on certain points fitted well with experimental ones, which validated the numerical model used in this study.

TCP의 트래픽 제어를 위한 동적 베이시안 네트워크 기반 지능형 PID 제어기

This paper presents an intelligent PID control for stochastic systems with nonstationary nature. We optimally determine parameters of a PID controller through learning algorithm and propose an online PID control to compensate system errors possibly occurred in realtime implementations. A dynamic Bayesian network (DBN) model for system errors is additionally explored for making decision about whether an online control is carried out or not in practice. We apply our control approach to traffic control of Transmission Control Protocol (TCP) networks and demonstrate its superior performance comparing to a fixed PID from computer simulations.

Development of intelligent system for a thermal analysis instrument

The key techniques for the intelligent analysis instrument developed are proposed. Based on the technique of virtual instrumentation, the intelligent PID control algorithm to control the temperature of thermal analysis instrument is described. The dynamic character and the robust performance of traditional PID controls are improved through the dynamic gain factor, temperature rate change factor, the forecast factor, and the temperature correction factor is introduced. Using the graphic development environment of LabVIEW, the design of system modularization and the graphic display are implemented. By means of multiple mathematical modules, intelligent data processing is realized.

A Design of CMAC Based Intelligent PID Controllers

Owing to the rapid progress of the computer technology in recent years, lots of works about intelligent control schemes have been developed in the field of control engineering. However, PID control algorithms still continue to be widely used for most industrial control systems, particularly in chemical process industries. However, it is difficult to find a suitable set of PID gains, because most processes are represented by nonlinear systems. On the other hand, in recent years, various neural networks (NNs) have been proposed. These technologies of the NNs enable us to deal with the nonlinear systems, and they play an important role in the field of control engineering. Furthermore, a Cerebellar Model Articulation Controller (CMAC) has been proposed as one of artificial neural networks. This paper presents a new design scheme of intelligent PID controllers whose PID gains are generated by using CMACs. Owing to the high-generalizing ability of the CMAC, the proposed scheme does not need large learning time with comparison to the conventional NN. Furthermore, the computational burden can be drastically reduced and the robust tracking can be realized for untrained reference signals. The newly proposed control scheme is numerically evaluated on some simulation examples.

A Design of PID Controllers Fused CMACS with Neural Networks

Several neural-net based PID controllers have been proposed for nonlinear process systems. However, they have been not so widely used in process industries due to the considerably computational cost. This paper presents a new intelligent PID tuning scheme, whose PID tuner is constructed by the fusional structure of a cerebellar model articulation controller and a neural network. This PID tuner gives us the higher learning efficiency which has not been realized by the conventional neural-net based controllers, and it enables us to tune PID gains in an on-line manner. The behaviour of the proposed scheme is examined by a simulation example for a chemical reactor model.

Knowledge-based multivariable PID control

Knowledge-based control has become an important approach towards the realization of intelligent control systems. To date, multivariable control system design is restricted to off-line design aided possibly by a knowledge-based system. A first-generation on-line knowledge-based multivariable PID control system is implemented in this paper. Real-time considerations are taken into account. The integration of the knowledge-based system with conventional process control system software and hardware is achieved through an interfaced approach. Heuristics developed in recent research on intelligent PID control is implemented to attain some of the visionary goals of knowledge-based control. An implementation on a laboratory scale heat exchanger is also given.

Implementation of a knowledge-based PID auto-tuner

This paper presents a particular implementation of a knowledge-based control system. It attempts to show how heuristics developed in recent research on intelligent PID control can be implemented to attain some of the visionary goals of knowledge-based control. The other characteristics of the implementation are the blackboard architecture of the knowledge base, and the clear separation of heuristic logic from the numerical algorithms.

Implementation of a Knowledge-Based Pid Auto-Tuner

This paper presents a particular implementation of a knowledge-based control system. It attempts to show how heuristics developed in recent research on intelligent PID control can be implemented to attain some of the visionary goals of knowledge-based control. The other characteristics of the implementation are the blackboard architecture of the knowledge base, and the clear separation of heuristic logic from the numerical algorithms.

Implementation of a knowledge-based PID auto-tuner

This paper presents a particular implementation of a knowledge-based control system. It attempts to show how heuristics developed in recent research on intelligent PID control can be implemented to attain some of the visionary goals of knowledge-based control. The other characteristics of the implementation are the blackboard architecture of the knowledge base, and the clear separation of heuristic logic from the numerical algorithms.

Intelligent Self-Tuning PID Controller

A new self-tuning PID controller has been developed for distributed process control systems. The self-tuning PID controller is able to identify the process dynamics using a short period of process behaivior such as a setpoint change under the closed loop control condition, and to tune PID parameters based on the identified iodei parameters for both setpoint tracking and disturbance regulation characteristics, which makes it easy to set up the inital PID parameters and adapt the PID values to the process dynamics changes. This paper describes the basic principles of the self-tuning controllers, and explains the functions to be implemented on. Finally, some application results illstrate the effectiveness of the self-tuning controllers.