Self-tuning Proportional Integral Controller Research Articles

Scanning Tunneling Microscope Control: A Self-Tuning PI Controller Based on Online Local Barrier Height Estimation

We identify the dynamics of a scanning tunneling microscope (STM) in closed loop and show that the plant dc gain is proportional to the square root of local barrier height (LBH), a quantum mechanical property of the sample and/or tip that affects the tunneling current. We demonstrate that during a scan, the LBH may undergo significant variations and this can adversely affect the closed-loop stability if the controller parameters remain fixed. Feedback instabilities increase the risk of tip-sample crash in STMs. In order to improve the closed-loop performance, we estimate the LBH, on the fly, and use that to adaptively tune the proportional-integral (PI) controller parameters. Experimental results obtained with the self-tuning PI controller confirm the improved STM performance compared to the conventional fixed-gain PI controller. Additional experiments confirm effectiveness of the proposed method in extending the tip lifetime by lowering the chance of a tip/sample crash.

Design a fuel cell based drive dc motor for an electric vehicle applications

The Applications of a Fuel cell (FC) in the field of the electric vehicle has realized wide popularity since the past few years because of the problem of relative increases in fossil fuel prices in addition to depletion of this fuel. The objective of this study is to design DC drivers based on DC-DC buck converter for separately excited DC motor as a traction motor used for an electric vehicle. This motor is fed by Polymer Electrolyte Membrane (PEM) FC as a renewable energy source. Many control strategies are implemented to produce the suitable gating signal for the buck converter and get the best motor operation response which where Proportional Integral (PI) controller, Fuzzy Logic (FL) controller, and FL based self-tuning PI controller. The proposed FC power system is modeled and simulated using Matlab-Simulink and Fuzzy toolbox. The performance and accuracy of the proposed algorithm have been investigated in different situations. The simulation results showed that the speed response of the DC motor controlled by the FL based self-tuning PI controller had improved the performance (overshoot, rising time, settling time and steady-state error) when compared to the typical PI controller, and FL controller.

Self-tuning proportional integral control for consensus in heterogeneous multi-agent systems

In this paper, we present a distributed Proportional-Integral (PI) strategy with self-tuning adaptive gains for reaching asymptotic consensus in networks of non-identical linear agents under constant disturbances. Alternative adaptive strategies are presented, based on global or local measures of the agents' disagreement. The proposed approaches are validated on a representative numerical example. Preliminary analytical results further confirm the viability of the self-tuning strategies.

A Self-Tuning zSlices-Based General Type-2 Fuzzy PI Controller

The interval type-2 fuzzy Proportional-Integral (PI) controller (IT2-FPI) might be able to handle high levels of uncertainties to produce a satisfactory control performance, which could be potentially due to the robust performance as a result of the smoother control surface around the steady state. However, the transient state and disturbance rejection performance of the IT2-FPI may degrade in comparison with the type-1 fuzzy PI (T1-FPI) counterpart. This drawback can be resolved via general type-2 fuzzy PI controllers which can provide a tradeoff between the robust control performance of the IT2-FPI and the acceptable transient and disturbance rejection performance of the type-1 PI controllers. In this paper, we will present a zSlices-based general type-2 fuzzy PI controller (zT2-FPI), where the secondary membership functions (SMFs) of the antecedent general type-2 fuzzy sets are adjusted in an online manner. We will examine the effect of the SMF on the closed-system control performance to investigate their induced performance improvements. This paper will focus on the case followed in conventional or self-tuning fuzzy controller design strategies, where the aim is to decrease the integral action sufficiently around the steady state to have robust system performance against noises and parameter variations. The zSlices approach will give the opportunity to construct the zT2-FPI controller as a collection of IT2-FPI and T1-FPI controllers. We will present a new way to design a zT2-FPI controller based on a single tuning parameter where the features of T1-FPI (speed) and IT2-FPI (robustness) are combined without increasing the computational complexity much when compared with the IT2-FPI structure. This will allow the proposed zT2-FPI controller to achieve the desired transient state response and provide an efficient disturbance rejection and robust control performance. We will present several simulation studies on benchmark systems, in addition to real-world experiments that were performed using the PIONEER 3-DX mobile robot that will act as a platform to evaluate the proposed systems. The results will show that the control performance of the self-tuning zT2-FPI control structure enhances both the transient state and disturbance rejection performances when compared with the type-1 and IT2-FPI counterparts. In addition, the self-tuning zT2-FPI is more robust to disturbances, noise, and uncertainties when compared with the type-1 and interval type-2 fuzzy counterparts.

Critic-Based Self-Tuning PI Structure for Active and Reactive Power Control of VSCs in Microgrid Systems

Traditional proportional integral (PI) control has been extensively used for power control of voltage source converters in microgrid systems. Previous studies show that fixed-gain PI controllers cannot easily adapt to power changes, disturbances, and parameters variation, especially in large microgrids; hence the need for continuous algorithms to adjust the controller gains over the transients cannot be neglected. In this paper, a novel online tuning algorithm for PI controllers is proposed and implemented in a microgrid system. In this algorithm, which is based on the neuro-dynamic programming concept, a fuzzy critic is employed to evaluate the credibility of the control system performance and provide an evaluation signal, which is then used in the gain-tuning process. The PI controller gains are updated in an optimization process based on steepest decent rule so that the evaluation signal produced by the critic is minimized. The developed control structure, which is named critic-based self-tuning PI controller, is tested in a microgrid system with different penetrations of distributed generators and operational scenarios. The simulation results verify that implementation of a heuristic gain-tuning algorithm results in a model-independent controller with increased adaptivity compared with conventional PI control. Furthermore, due to simple learning rules, the convergence time is significantly reduced and the transient response is improved. The proposed gain-tuning algorithm can also be applied to PI controllers in other applications of controllable systems.

Design and analysis of an intelligent controller for active geometry suspension systems

An active geometry suspension (AGS) system is a device to optimise suspension-related factors such as toe angle and roll centre height by controlling vehicle's suspension geometry. The suspension geometry could be changed through control of suspension mounting point's position. In this paper, analysis and control of an AGS system is addressed. First, the effects of suspension geometry change on roll centre height and toe angle are studied. Then, based on an analytical approach, the improvement of the vehicle's stability and handling due to the control of suspension geometry is investigated. In the next section, an eight-degree-of-freedom handling model of a sport utility vehicle equipped with an AGS system is introduced. Finally, a self-tuning proportional–integral controller has been designed, using the fuzzy control theory, to control the actuator that changes the geometry of the suspension system. The simulation results show that an AGS system can improve the handling and stability of the vehicle.

Design of a Self-Tuning PI Controller for a STATCOM Using Particle Swarm Optimization

A self-tuning proportional-integral (PI) controller in which the controller gains are adapted using the particle swarm optimization (PSO) technique is proposed for a static synchronous compensator (STATCOM). An efficient formula for the estimation of system load impedance using real-time measurements is derived. Based on the estimated system load, a PSO algorithm, which takes the best particle gains, the best global gains, and previous change of gains into account, is employed to reach the desired controller gains. To demonstrate the effectiveness of the proposed PSO self-tuning PI controller for a STATCOM, experimental results for a system under different loading conditions are presented. Results from the self-tuning PI controller are compared with those from the fixed-gain PI controllers.

Simple Fuzzy Self-tuning PI Controller for Multi-terminal HVDC Transmission Systems

This paper describes fuzzy logic-based self-tuning controller parameters for the rectifier side current regulators and inverter side gamma regulators in a multi-terminal high-voltage direct current (MTDC) transmission system. A four-terminal MTDC system is considered, with the detailed modeling of converters, transformers, source impedances, transmission lines, and filters. Simulations are conducted using MATLAB/SIMULINK® to generate transient responses. The simulation results using a fuzzy self-tuning proportional-integral (PI) controller and a fixed-parameter PI controller are compared. The transient simulation studies presented reveal the superior performance of the fuzzy self-tuning PI controller over the fixed-parameter PI controller in the MTDC transmission systems.

Using interval phase margin assignment to self-tune a PI AQM controller for TCP traffic

We propose a self-tuning PI (Proportional-Integral) controller for an AQM (Active Queue Management) router supporting TCP traffic in the Internet. Classical control theory is applied in the controller design to meet the phase margin specification in the frequency domain. By assigning a proper interval of the phase margin, we can achieve good AQM performance by making the control system adapt to dramatic load changes. Our self-tuning PI controller self-tunes only when there is a great change in the network environment that would cause the phase margin of the AQM control system to drift outside the specified interval. Based on the knowledge of the queue size, our PI controller can regulate the TCP source window size by adjusting the packet drop probability, thus clamping the steady queue size around a desirable target buffer occupancy. We demonstrate by OPNET® simulations that with our self-tuning PI controller applied, the network exhibits a good transient behavior. A simple PID (Proportional-Integral-Derivative) controller design method is also provided.