Self-tuning Proportional-integral-derivative Controller Research Articles

Fuzzy Self-Tuning PID Control of the Main Drive System for Four-High Hot Rolling Mill

The main drive system used for four-high mill is usually a double closed-loop DC motor speed regulating system. Generally, the speed controller and current controller are proportional-integral-derivative (PID) controllers. The parameters of these controllers are usually determined by the engineering design method and are constant in the whole process. So the controllers can give good control effect in the rated operating condition. Once disturbance occurs, the control effects are often just passable. In this paper, fuzzy logic is used to adjust the parameters of the speed PID controller to improve the disturbance rejection ability. Simulation results show that the fuzzy tuning PID controller can give better control effect than the regular designed PID control.

Fuzzy Self-Tuning PID Temperature Control Modeling and Simulation System

For the current general heating control system tracks the set temperature hysteresis heating device to automatically adjust the real-time problem of poor design a fuzzy self-tuning proportional-integral-derivative (PID) controller parameters. Scoop out using PID control and fuzzy control algorithm combined method; fuzzy PID control parameters are adjusted. Use Matlab to model in simulink, the aryl the controller simulation analysis. The results show that the fuzzy self-tuning PID controller overshoot σ≈l%, steady-state error Island es=0. This method can improve the performance of the temperature control system..

MPSoCs and Multicore Microcontrollers for Embedded PID Control: A Detailed Study

This paper presents different multiprocessor implementations of the proportional-integral-derivative (PID) controller using two technologies: 1) field programmable gate array (FPGA)-based multiprocessor system-on-chip (MPSoC); and 2) multicore microcontrollers (MCUs). Techniques to implement a parallelized PID controller, a multi-PID controller, and a self-tuning PID controller are proposed. These techniques are verified using hardware (HW) in the loop (HIL) simulations. Then, the paper presents a detailed case study of an embedded real-time (RT) self-tuning PID controller for a 1-degree-of-freedom (1-DOF) aerodynamical system. This includes controller design, parameters tuning, and implementation using a multiprocessor system. Results proved the effectiveness of the proposed techniques to improve performance and functionality. It is shown that customizing HW and software (SW) within MPSoCs provides higher RT performance. Moreover, using multicore MCUs can reduce design time, implementation time, and cost, while keeping adequate performance. Therefore, it is possible to realize and implement complex RT embedded controllers that employ advanced control algorithms in rapid, effective, and cost-efficient fashion.

Design methodology and self-turning velocity control for high-speed slim sensorless brushless direct current motors with self-lubricated bearings

This study presents the motor design methodology and adaptive fuzzy self-tuning proportional–integral–derivative (PID) velocity closed-loop control of slim sensorless brushless direct current (BLDC) motors with self-lubricated bearings that operate at a high speed of 14,000rpm. Moreover, the novel sensorless commutation method includes a mask-based phase shift detector to detect precise optimal commutation points. A prototype of the slim sensorless radial-flux BLDC motor with a self-lubricated bearing for use in the blowers of vacuum cleaners was manufactured and the motor design methodology was verified. The damping interaction caused by the self-lubricating hydrodynamic bearings, shafts, and blades of blowers was also considered, together with a multi-physics analysis. The velocity control experiment used a adaptive fuzzy self-tuning PID control algorithm that has been proven to improve the dynamic performance of the slim sensorless BLDC motors in the blowers of vacuum cleaners. The settling time converged within 1s, that is, the vacuum cleaners rapidly switched to different speeds, and the control approach exhibited robustness and precision in a steady state.

Variable air volume air-conditioning experiment system with advanced controls

A heating, ventilation, and air-conditioning (HVAC) system is a multi-variable strongly coupled large-scale system that is composed of several sub-systems. Considerable research, simulations, and experiments have been conducted on HVAC control. The optimization control of an HVAC system is now the popular issue. The ultimate goal of this paper was to achieve minimum energy consumption and improve system efficiency. Multi-zone variable air volume and variable water volume air-conditioning systems were developed. The dynamic models of HVAC sub-systems were built by the adaptive directional forgetting method. Control strategies such as the gearshift integral proportional-integral-derivative (PID) controller and self-tuning PID controller were studied in the platform to improve the dynamic characteristics of the HVAC system. System performance was improved. The system saved 18.2% of energy with the integration of iterative learning control and sequential quadratic programming based on the steady-state hierarchical optimization control scheme.

Self-tuning PID control of a brushless DC motor by adaptive interaction

In this paper, a self-tuning algorithm for proportional integral derivative (PID) control based on the adaptive interaction (AI) approach theory efficiently used in artificial neural networks (ANNs) is proposed. In this approach, a system is decomposed into interconnected subsystems, and adaptation occurs in the interaction weights among these subsystems. The principle behind the adaptation algorithm is mathematically equivalent to a gradient descent algorithm. The same adaptation as the well-known backpropagation algorithm (BPA) can be achieved without the need of a feedback network, which would propagate the errors, by applying adaptive interaction. Thereby, the ANN controller can be adapted directly without wasting calculation time in order to increase the frequency response of the controller. The velocity control of a brushless DC motor (BLDCM) under slowly and rapidly changing load conditions is simulated to demonstrate the effectiveness of the algorithm. The AI tuning algorithm was used to tune up the PID gains, and the simulation results with PID adaptation process are presented by comparing the obtained results with the adaptive PID controller based on BPNN and a conventional PID controller. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Design Controller for a Class of Nonlinear Pendulum Dynamical System

Designing proportional integral derivative (PID), Linear Quadratic Regulator (LQR), Fuzzy Logic Controller (FLC) and Self-Tuning Fuzzy PID (STFP) controller is used for nonlinear pendulum dynamic system in this paper.The promising performance of the proposed controllers investigates in simulation. The effectiveness, robustness against noise and the comparison of the controller methods for Nonlinear Pendulum Dynamical System are delivered in this paper. DOI: http://dx.doi.org/10.11591/ij-ai.v2i4.4164

Robust adaptive speed control of uncertain hybrid electric vehicle using electronic throttle control with varying road grade

The design objective of this paper is to apply various control techniques to control the speed of a hybrid electric vehicle (HEV) using an electronic throttle control system (ETCS). The DC servo motor is used for controlling the angular position of the throttle valve. A proportional-integral-derivative (PID), a self-tuning fuzzy PID (STF-PID) controller and a model reference adaptive system (MRAS) with a sliding mode (SM) adaptation mechanism are used for controlling the speed of the nonlinear vehicle. The integral error performance indices (IEPI) such as the integral of the absolute error (IAE), the integral of the square of error (ISE) and the time domain performance specifications such as overshoot (OS), settling time (ST) and rise time (RT) are taken into consideration for the performance analysis of HEV. The robust H∞ controller using mixed sensitivity approach is designed and implemented for the linearized HEV. The robust stability of uncertain HEV with H∞ controller using Kharitonov’s theorem is analyzed, and the stability margin of the linearized vehicle system is determined. These control techniques are developed to achieve the robust performance of the throttle controlled HEV with the target to achieve a wide range of speed, fuel economy, reduced pollution and improved efficiency.

Designing and Simulation for Vertical Moving Control of UAV System using PID, LQR and Fuzzy logic

This paper presents the designing and simulation for vertical moving control of Unmanned Air Vehicles (UAV) system using Proportional Integral Derivative (PID) control method, Linear Quadratic Regulator (LQR) control method and fuzzy logic control technique.The UAV system pose a challenger control problem that in it precise model of system is non-linear. The conventional PID controller, the LQR controller, Fuzzy Logic Controller (FLC) and Self-Tuning Fuzzy PID controller are designned based on the dynamic modeling of the system is derived from a suitable mathematical model to describe the vertical motion of a UAV. Simulation results in Matlab and Simulink show that by using self-tuning Fuzzy PID, the performance of vertical moving control system is improved significantly compared to Other controllers. DOI: http://dx.doi.org/10.11591/ijece.v3i5.3440

Design and experimentation of a self-tuning PID control applied to the 3DOF helicopter

Abstract The paper presents design and experimental validation of a stable self-tuning PID controller for three degrees of freedom (3-DOF) helicopter. At first, it is proposed a self-tuned proportional-integral-derivative (PID) controller for a class of uncertain second order multiinput multi-output nonlinear dynamic systems to which the 3-DOF helicopter dynamic model belongs. Within this scheme, the PID controller is employed to approximate unknown ideal controller that can achieve control objectives. PID controller gains are the adjustable parameters and they are updated online with a stable adaptation mechanism designed to minimize the error between the unknown ideal controller and the used by PID controller. The stability analysis of the closed-loop system is performed using Lyapunov approach. It is proven that all signals in the closed-loop system are uniformly ultimately bounded. The proposed approach can be regarded as a simple and effective model-free control since the mathematical model of the system is assumed unknown. Experimental results are presented to verify the effectiveness of the proposed controller.

Fuzzy Self-Tuning PID Control of Hydrogen-Driven Pneumatic Artificial Muscle Actuator

In this paper, a fuzzy self-tuning Proportional-Integral-Derivative (PID) control of hydrogen-driven Pneumatic Artificial Muscle (PAM) actuator is presented. With a conventional PID control, non-linear thermodynamics of the hydrogen-driven PAM actuator still highly affects the mechanical actuations itself, causing deviation of desired tasks. The fuzzy self-tuning PID controller is systematically developed so as to achieve dynamic performance targets of the hydrogen-driven PAM actuator. The fuzzy rules based on desired characteristics of closed-loop control are designed to finely tune the PID gains of the controller under different operating conditions. The empirical models and properties of the hydrogen-driven PAM actuator are used as a genuine representation of mechanical actuations. A mass-spring-damper system is applied to the hydrogen-driven PAM actuator as a typical mechanical load during actuations. The results of the implementation show that the viability of the proposed method in actuating the hydrogen-driven PAM under mechanical loads is close to desired performance.

Self-tuning control with a filter and a neural compensator for a class of nonlinear systems.

Considering the mismatching of model-process order, in this brief, a self-tuning proportional-integral-derivative (PID)-like controller is proposed by combining a pole assignment self-tuning PID controller with a filter and a neural compensator. To design the PID controller, a reduced order model is introduced, whose linear parameters are identified by a normalized projection algorithm with a deadzone. The higher order nonlinearity is estimated by a high order neural network. The gains of the PID controller are obtained by pole assignment, which together with other parameters are tuned on-line. The bounded-input bounded-output stability condition and convergence condition of the closed-loop system are presented. Simulations are conducted on the continuous stirred tank reactors system. The results show the effectiveness of the proposed method.

Switching-Type Self-Tuning Fuzzy PID Control of an Active Magnetic Bearing System

This paper presents a new switching control scheme for an active magnetic bearing (AMB) system using self-tuning fuzzy proportional-integral-derivative (PID) control. The research process consists of three stages. First, four types of self-tuning fuzzy PID-type controllers (FPIDCs) consisting of two most commonly used fuzzy inference systems: Mamdani and Takagi-Sugeno types, and two efficient parameter adaptive methods: function tuner and relative rate observer, are used to control a highly nonlinear AMB system, respectively. Hence, there are two kinds of FPIDCs can be obtained by comparing experimental results of these tests: one has the fastest transient response and the other has the minimum steady-state error. Next, the switching-type self-tuning FPIDC is proposed by combining the two kinds of FPIDCs. Namely, the AMB system is dominated by the scheme with the fastest transient response when the rotor is at rest and by the one with the best steady-state performance when the rotor is in rotation. Finally, experimental results demonstrate that the proposed switching-type self-tuning FPIDC performs better overall performance than the other self-tuning FPIDCs, particularly when controlling an AMB system.

Application of Fuzzy PID Control in Sluice Control

Fuzzy control, based on traditional Proportional-Integral-Derivative (PID) control, is used to improve the management of a hydro-junction’s sluice scheduling. In this study, we combined the PID and Fuzzy control theories and determined the PID parameters of the fuzzy self-tuning method of a hydro-junction’s sluice. A fuzzy self-tuning PID controller and its algorithm were designed. In hydro-junction sluice control, the Fuzzy PID controller can modify PID parameters in real-time, resulting in a more dynamic response. The application of the fuzzy self-tuning PID controller in the CiHuai River project information integration system yielded very good results.

A Novel Modified Fly Optimization Algorithm for Designing the Self-Tuning Proportional Integral Derivative Controller

A Novel Modified Fly Optimization Algorithm for Designing the Self-Tuning Proportional Integral Derivative Controller

Self-tuning PID Controller using Genetic Algorithm

This work presents design, modeling, simulation and hardware implementation of a separately excited DC motor speed control using Field Programmable Analog Array (FPAA) Technology. The framework presents a low power self-tuning analog Proportional-Integral-Derivative (PID) controller using a model-free tuning method, this overcomes the problems associated with reconfigurable analog arrays. In comparison with a self-tuning digital PID controller, the analog self-tuning PID controller combines the advantages of low power, no quantization noise, high bandwidth and high speed. The prototype hardware uses a commercially available field programmable analog array and Genetic Algorithm as tuning method. The practical results show that a self-tuned controller can outperform a hand tuned solution and demonstrate adaptability to plant drift, also it shows enhancement in the reduction of overshoot, settling time and the steady-state transient response of the controlled plant.

Motion control for a two-wheeled vehicle using a self-tuning PID controller

This paper presents the motion control and stability analysis of a two-wheeled vehicle (TWV). The TWV is driven using two independent wheel motors, upon which a vehicle body is mounted. A mathematical model of the TWV is obtained using dynamic analysis. The TWV is inherently unstable and its motion is controlled through the actions of the wheel motors. Vehicle action depends on both the desired wheel response and the tilt angle. A self-tuning proportional-integral-derivative (PID) control strategy, based on a deduced model, is proposed for implementing a motion control system that stabilizes the TWV and follows the desired motion commands. The controller parameters are tuned automatically, on-line, to overcome the disturbances and parameter variations. Experimental results are presented to demonstrate the reliability and effectiveness of the proposed control scheme.

Self-tuning PID control of a plant with under-damped response with specifications on gain and phase margins

The effectiveness of the proportional-integral-derivative (PID) controller for plants with under-damped step response is not well understood. In practice, one may have to tune the PID controller manually through trial and error for such a plant. In this paper, a gain and phase margin tuning formula is derived for PID control of a second-order plus dead time plant with under-damped step response. A self-tuning controller incorporating this PID tuning formula has been implemented and tested on a pilot plant which consists of a hinged rectangular plate whose angular deflection from the vertical is controlled by an air stream generated by a variable speed fan. The results are given in the paper.

Self-tuning PID controller using Newton-Raphson search method

A new algorithm for self tuning of proportional-integral-derivative (PID) controllers is proposed. A combined least-squares estimation and Newton-Raphson search technique is used to determine the ultimate gain and period of an unknown system for the purpose of automatic tuning of PID controllers based on Ziegler and Nichols (ZN) or refined Ziegler and Nichols (RZN) formulas. The proposed algorithm can be applied to systems with known time delay, as well as those with unknown dead time. Simulation studies are used to demonstrate the performance of this algorithm. The performance of this PID self tuner is also compared with a popular commercial auto-tuner for simulated systems and a laboratory-scale real plant.