Self-tuning PID Control Algorithm Research Articles

Fuzzy self-tuning PID control algorithm for belt conveyor driven by multi-motor

Aiming at the problems of low efficiency when long distance and high-inclination belt conveyor driven by multi-motor is in light or idle run as well as electrical power unbalance. Control strategy for belt conveyor driven by four-motor via fuzzy self-tuning PID control algorithm was proposed. The Speed setting algorithm of belt conveyor and fuzzy self-tuning PID algorithm for speed control and power balance control are introduced. The application shows that, according to the coal flow changes of the belt conveyor in real-time, the speed can be automatically adjusted through this algorithm, making the power of the four motors convergence both speed control error and the power control error of which are lower than 2.6%.

Fuzzy Self-tuning PID Control Algorithm for Belt Conveyor Driven by Multi-motor

Fuzzy Self-tuning PID Control Algorithm for Belt Conveyor Driven by Multi-motor

Experimental system for the optimization of the parallel manipulator control

This paper presents the results of an experimental system design for the optimization of parallel manipulator control based on an optimal configuration. The experimental system is an open design for various different configurations and controllers to support parallel manipulator research and applications. Traditional PID control and self-tuning fuzzy PID control algorithm are applied for motion control of parallel manipulator. The quality system standard will be analyzed and compared with simulation results. The system can be a useful tool for research and training.

Fuzzy PID Parameters Self-Tuning in the Application of the Furnace Temperature Control System

According to the set temperature, the parameter self-tuning fuzzy PID control algorithm is used furnace temperature automatic control and fuzzy PID control process to make use of the MATLAB simulation. Simulation results show that compared with conventional PID control, the parameter self-tuning fuzzy PID control algorithm improves the system response, shorten the adjusting time, enhances the disruptive, demonstrate the superiority of the fuzzy PID.

Design of Intelligent Traffic Light Control System Based on Multi CPU

The intelligent traffic light control is the core problem in the intelligent traffic research field, in order to solve this problem, the intelligent traffic light control system is proposed based on multi CPU. In a multi processor system, aiming at the intelligent traffic light, the reasonable control is taken. In the intelligent traffic light control system, the related principles of multi processor system design and shared memory are elaborated in detail. The BP neural network self-tuning PID control algorithm is applied in the traffic lights control process, reasonable control of traffic lights is obtained. The experiment results show that the principle is applied in the intelligent traffic light control system, it can greatly improve the control accuracy, so it can meet the actual demand of intelligent traffic management.

Research and Design of Quantitative Casting Control System for Lead Anode Plate

In this paper, taking the quantitative casting system for lead anode plate as the object of study, develop a control system based on the fieldbus and fuzzy control. Introduce the process and control principle of the lead anode plate casting, and establish the quantitative pouring control system from two aspects as the designs of hardware and software equipment.The system adopts the PROFIBUS-DP fieldbus technology and uses SIMATIC PLC as the master station to structure the casting control system. It describes the realization and simulation of the fuzzy self-tuning PID control algorithm in quantitative casting system.The simulation results show: Fuzzy - PI control scheme has a better control effectiveness than the traditional PID control system,for a smaller control response curve overshoot and error, a shorter response time and restraining the outside interference to the system and the instability of casting flow.

Research on Automatic Digging Control and Algorithm of the Working Device of Skid Steer Loader

Focus on the trajectory planning problem of shovel operations of skid steer loader, a trajectory curve fitting equation is established. By use of the robotics theory and the D-H method, the functional relationship between the kinematics of the working device of skid steer loader and the displacement, position and orientation of boom bucket cylinder is derived, and the driving equation of displacement cylinder is obtained. Based on the requirements of precise trajectory control, an electro-hydraulic proportional position control system for position and orientation control is designed and the state equation of the system is established. By using fuzzy self-tuning PID control algorithm, the outside interference, parameter uncertainty and the error caused by other factors are weakened. The trajectory tracking simulation results demonstrate that: the biggest shovel tracking error is 12.5mm, the designed electro-hydraulic proportional control system can effectively track the trajectory of the working device of skid steer loader in automatic digging process. The rightness of the established model and the effectiveness of the corresponding controller are also proved.

Self-tuning run-time reconfigurable PID controller

Self-tuning run-time reconfigurable PID controller Digital PID control algorithm is one of the most commonly used algorithms in the control systems area. This algorithm is very well known, it is simple, easily implementable in the computer control systems and most of all its operation is very predictable. Thus PID control has got well known impact on the control system behavior. However, in its simple form the controller have no reconfiguration support. In a case of the controlled system substantial changes (or the whole control environment, in the wider aspect, for example if the disturbances characteristics would change) it is not possible to make the PID controller robust enough. In this paper a new structure of digital PID controller is proposed, where the policy-based computing is used to equip the controller with the ability to adjust it's behavior according to the environmental changes. Application to the electro-oil evaporator which is a part of distillation installation is used to show the new controller structure in operation.

Fuzzy self-tuning PID control of the operation temperatures in a two-staged membrane separation process

A two-staged membrane separation process for hydrogen recovery from refinery gases is introduced. The principle of the gas membrane separation process and the influence of the operation temperatures are analyzed. As the conventional PID controller is difficult to make the operation temperatures steady, a fuzzy self-tuning PID control algorithm is proposed. The application shows that the algorithm is effective, the operation temperatures of both stages can be controlled steadily, and the operation flexibility and adaptability of the hydrogen recovery unit are enhanced with safety. This study lays a foundation to optimize the control of the membrane separation process and thus ensure the membrane performance.

Adaptive Self-Tuning PID Control Algorithm for a Yacht

Autopilots design for a yacht is more difficult than for a motor-powered boat because of the special characteristics of the motion of a yacht in response to wind and other sea conditions. Using a Simulink model of a twelve-meter America's Cup yacht, some aspects of designing effective control algorithms are presented. Some aspects of designing an adaptive self-tuning PID controller via LQR approach are reviewed, and simulation results of the performance of the resulting adaptive PID control law are presented.

Self-Tuning Pid Control Using an Adaptive Network–Based Fuzzy Inference System

Abstract This paper presents a self-tuning PID control algorithm using an adaptive network-based fuzzy inference structure (ANFIS). In particular, the design of a self-tuning PID learning-based optimum controller is introduced which can be applied to nonlinear as well as linear systems. A recursive adaptation scheme is employed for on-line implementation of the self-tuning PID controller in which an exponential forgetting factor is used to weigh old data and both an error and control rate cost are used in the backwards pass. Results show that the method is a viable approach for tuning the parameters of a PID controller.

A Design of Self-Tuning PID Controllers Based on a Pole-Assignment Scheme

PID control schemes based on the classical control theory, have been widely used for real control systems up to now, since PID controllers have simple control structures, and are simple to maintain and tune. Furthermore, there are a lot of works with respect to tuning method of PID gains, since control performance depends on these gains. Also several self-tuning and auto-tuning techniques in the PID control systems have been reported for systems with unknown parameters. There, however, is not any result for discrete-time systems with unknown time-delays. On the other hand, the poleassignment control scheme has been reported as a useful self-tuning control technique for unknown time-delay systems. In this paper, a self-tuning PID control algorithm is proposed for unknown time-delay systems, based on the relationship between PID and pole-assignment controlschemes. The proposed scheme is numerically evaluated on an example of an ethylene cracking furnace temperature control.

Design and Implementation of a Self-Tuning PID Controller

PID control schemes based on the classical control theory, have been widely applied to real control systems, and there have been a lot of works on the tuning of PID parameters, as they affect control performance. Several self-tuning and auto-tuning PID techniques have been reported for systems with unknown or slowly time-varing parameters. None, however, is applicable to discrete-time system with unknown dead time. In this paper, a self-tuning PID control algorithm is proposed based on the relationship between the PID control law and the generalized minimum variance control law which is one of self-tuning control strategies. The proposed control scheme is practically evaluated on a temperature control system of a poly butene reactor.

Self-Tuning PID Control of a Polybutene Process

Abstract PID control schemes based on classical control theory, have been widely applied to real control systems, since the control parameters can be considered as PID gains of proportional, integral and differential actions. Furthermore, there has been a lot of research on the tuning of PID gains, as they affect control performance. Several self-tuning and auto-tuning techniques have been reported for PID control systems with unknown parameters. None, however, is applicable to discrete-time system with an unknown dead time. A self-tuning PID control algorithm is proposed based on the relationship between the PID control law and the generalized minimum variance control law, which is a self-tuning control strategy. Furthermore, the proposed control scheme is applied to the temperature control system of a polybutene reactor, and the results are discussed.

ADAPTIVE MULTIVARIABLE PID CONTROLOF A DISTILLATION COLUMN WITH UNKNOWN AND VARYING DEAD TIME

Abstract A new, reliable, and easy-to-use adaptive control strategy has been developed to overcome the long-existing difficulties in adaptive control practice caused by unknown and varying process dead time. A self-tuning PID control algorithm is adopted to control a distillation column possessing second-order-plus-dead-time dynamics. The self-tuning strategy is based on recursive least-squares estimation of process parameters. U-D factorization is applied to stabilize the parameter estimation calculations. A variable forgetting factor is used to alleviate wind-up in the estimator. A simulation study and an experimental evaluation demonstrate the capability of the adaptive algorithm.

A Construction of Self-Tuning PID Control System and Its Application to Temperature Control of a Polystyrene Reactor

Self-tuning control strategies have been considered for systems with unknown parameters. The rapid progress of digital computer enables us to simulate complex and large-scale systems. Many control techniques have been proposed in order to improve the control performance for discrete-time control systems. However, since these control techniques are too complex, it is usually difficult to apply them to real plants and also difficult to understand a physical meaning of control parameters included in these control systems. On the other hand, PID control methods based on the classical control theory have been used widely for real control systems since they have simple control schemes. Therefore, it is necessary to consider control methods with PID control structure in the discrete-time systems. Many literatures with regard to auto-tuning and self-tuning PID control strategies have been reported up to now. But none of them gives us any results for discrete-time systems with time-delay which takes important roles for the stability of the control system and the good control performance.In this paper, we consider a self-tuning PID control algorithm for a system with a time delay element. This control method has four gains, which are proportional, integral, differential gains and a gain for modifying the reference signal. This method enables us to improve a tracking property for a reference signal in the transient state, and also to guarantee the global stability of the control system. Finally, the effectiveness of this control method is shown by applying the method to a temperature control system of a polystrene reactor. In order to show the effectiveness of this method.

Neural Network Self-Tuning Control Of Hot-Spot Temperature In A Fixed-Bed CatalyticReactor

This paper demonstrates the possibility of applying artificial neural networks (ANNs) in the self-tuning PID control of the hot-spot-temperature in a fixed-bed catalytic reactor system. In this reactor system sulfur dioxide is oxidized using vanadium pentoxide catalyst. Unlike the conventional self-tuning PID control algorithm, the ANN applied to the self-tuning PID (NNW-PID) philosophy is an inherent nonlinear estimator and therefore identifies a nonlinear system directly from historical data supplied by the plant. In the majority of control applications, the ANN is employed as a predictor of future outputs within an established predictive control algorithm [1,2]. In this paper we propose a scheme where an ANN is employed on-line with a non predictive PID controller to give adaptive control of the reactor. This is accomplished by applying the values of the ANN weights, that are continually updated by the algorithm, to give the relevant PID controller parameters. Since real plant data is used to train the network, the algorithm can be applied to similar real-world problems.