Showing papers on "PID controller published in 2011"

A Robust Control Scheme for Grid-Connected Voltage-Source Inverters

Abstract: This paper analyzes the stability problem of the grid-connected voltage-source inverter (VSI) with LC filters, which demonstrates that the possible grid-impedance variations have a significant influence on the system stability when conventional proportional-integrator (PI) controller is used for grid current control. As the grid inductive impedance increases, the low-frequency gain and bandwidth of the PI controller have to be decreased to keep the system stable, thus degrading the tracking performance and disturbance rejection capability. To deal with this stability problem, an H∞ controller with explicit robustness in terms of grid-impedance variations is proposed to incorporate the desired tracking performance and the stability margin. By properly selecting the weighting functions, the synthesized H∞ controller exhibits high gains at the vicinity of the line frequency, similar to the traditional proportional-resonant controller; meanwhile, it has enough high-frequency attenuation to keep the control loop stable. An inner inverter-output-current loop with high bandwidth is also designed to get better disturbance rejection capability. The selection of weighting functions, inner inverter-output-current loop design, and system disturbance rejection capability are discussed in detail in this paper. Both simulation and experimental results of the proposed H∞ controller as well as the conventional PI controller are given and compared, which validates the performance of the proposed control scheme.

Receding Horizon Control

Abstract: In this article we have shown that receding horizon control offers a straightforward method for designing feedback controllers that deliver good performance while respecting complex constraints. A designer specifies the RHC controller by specifying the objective, constraints, prediction method, and horizon, each of which has a natural choice suggested directly by the application. In more traditional approaches, such as PID control, a designer tunes the controller coefficients, often using trial and error, to handle the objectives and constraints indirectly. In contrast, RHC con trollers can often obtain good performance with little tuning. In addition to the straightforward design process, we have seen that RHC controllers can be implemented in real time at kilohertz sampling rates. These speeds are useful for both real-time implementation of the controller as well as rapid Monte Carlo simulation for design and testing purposes. Thus, receding horizon control can no longer be considered a slow, computationally intensive policy. Indeed, RHC can be applied to a wide range of control problems, including applications involving fast dynamics.

Dynamic analysis and PID control for a quadrotor

Abstract: In order to analyze the dynamic characteristics and PID controller performance of a quadrotor, this paper firstly describes the architecture of the quadrotor and analyzes the dynamic model of it. Then, based on the classic scheme of PID control, this paper designs a controller, which aims to regulate the posture (position and orientation) of the 6 d.o.f. quadrotor. Thirdly, the dynamic model is implemented in Matlab/Simulink simulation, and the PID control parameters are obtained according to the simulation results. Finally, a quadrotor with PID controllers is designed and made. In order to do the experiment, a flying experiment for the quadrotor has been done. The results of flying experiment show that the PID controllers robustly stabilize the quadrotor.

Robust Static Output Feedback Control and Remote PID Design for Networked Motor Systems

Abstract: In this paper, we investigate the problem of robust static output feedback (SOF) control for networked control systems (NCSs) subject to network-induced delays and missing data. The uncertain system matrices are assumed to lie in a convex polytope. The network-induced delays are time varying but within a given interval. The random data missing is characterized by the Bernoulli random binary distribution. Delay-dependent conditions for the exponential mean-square stability are first established in terms of matrix inequalities. Then, for the robust stabilization problem, the design of an SOF controller is presented by solving bilinear matrix inequalities (BMIs). In order to efficiently solve a nonconvex BMI, we propose an approach based on the linear matrix inequality technique. Furthermore, the developed approach is employed to design the remote proportional-integral-derivative (PID) controller for NCSs. The design of a digital PID controller is formulated as a synthesis problem of the SOF control via an augmentation method. Simulation examples illustrate the effectiveness of the proposed methods.

Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.

Abstract: An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS) The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers

FOMCON: Fractional-order modeling and control toolbox for MATLAB

Abstract: FOMCON is a new fractional-order modeling and control toolbox for MATLAB. It offers a set of tools for researchers in the field of fractional-order control. In this paper we present all the major modules comprising the toolbox and discuss the corresponding mathematical concepts. Fractional-order system analysis, identification and fractional PID controller design, tuning and optimization in the context of the toolbox are presented and discussed.

A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control

Abstract: In this paper, a 2 DOF planar robot was controlled by Fuzzy Logic Controller tuned with a particle swarm optimization. For a given trajectory, the parameters of Mamdani-type-Fuzzy Logic Controller (the centers and the widths of the Gaussian membership functions in inputs and output) were optimized by the particle swarm optimization with three different cost functions. In order to compare the optimized Fuzzy Logic Controller with different controller, the PID controller was also tuned with particle swarm optimization. In order to test the robustness of the tuned controllers, the model parameters and the given trajectory were changed and the white noise was added to the system. The simulation results show that Fuzzy Logic Controller tuned by particle swarm optimization is better and more robust than the PID tuned by particle swarm optimization for robot trajectory control.

On the selection of tuning methodology of FOPID controllers for the control of higher order processes.

Abstract: In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modelling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.

Real-Time PID Control Strategy for Maglev Transportation System via Particle Swarm Optimization

Abstract: This paper focuses on the design of a real-time particle-swarm-optimization-based proportional-integral-differential (PSO-PID) control scheme for the levitated balancing and propulsive positioning of a magnetic-levitation (maglev) transportation system. The dynamic model of a maglev transportation system, including levitated electromagnets and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics, is first constructed. The control objective is to design a real-time PID control methodology via PSO gain selections and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. The effectiveness of the proposed PSO-PID control scheme for the maglev transportation system is verified by numerical simulations and experimental results, and its superiority is indicated in comparison with PSO-PID in previous literature and conventional sliding-mode (SM) control strategies. With the proposed PSO-PID control scheme, the controlled maglev transportation system possesses the advantages of favorable control performance without chattering phenomena in SM control and robustness to uncertainties superior to fixed-gain PSO-PID control.

Tuning of PID Controller Using Particle Swarm Optimization (PSO)

Abstract: The aim of this research is to design a PID Controller using PSO algorithm. The model of a DC motor is used as a plant in this paper. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as genetic algorithm (GA) and particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. However, due to the computational efficiency, only PSO will be used in this paper. The comparison between PSO-based PID (PSO-PID) performance and the ZN-PID is presented. The results show the advantage of the PID tuning using PSO-based optimization approach.

Neuro-Control and its Applications

Abstract: 1 Introduction.- 1.1 Introduction to Intelligent Control.- 1.2 References.- 2 Neural Networks.- 2.1 Historical Review of Neural Networks.- 2.2 Backpropagation Algorithm.- 2.2.1 Notation.- 2.2.2 Derivation of the Backpropagation Algorithm.- 2.2.3 Algorithm: Backpropagation Method.- 2.2.4 Some Discussions on the Backpropagation Algorithm.- 2.3 Conclusions.- 2.4 References.- 3 Traditional Control Schemes.- 3.1 Introduction.- 3.2 Discrete-Time PI and PID Controllers.- 3.3 Self-Tuning Control.- 3.4 Self-Tuning PI and PID Controllers.- 3.4.1 Closed Loop System.- 3.4.2 Some Interpretations Based on a Simulation Example.- 3.5 Self-Tuning PID Control - A Multivariable Approach.- 3.5.1 Simulation Example.- 3.6 Generalized Predictive Control - Some Theoretical Aspects.- 3.6.1 Cost Criterion.- 3.6.2 The Plant Model and Optimization Solution.- 3.7 Fuzzy Logic Control.- 3.7.1 Brief Overview of Fuzzy Set and Fuzzy System Theory.- 3.7.2 Basic Concept of Fuzzy Logic Controller.- 3.8 Conclusions.- 3.9 References.- 4 Neuro-Control Techniques.- 4.1 Introduction.- 4.2 Overview of Neuro-Control.- 4.2.1 Neuro-Control Approaches.- 4.2.2 General Control Configuration.- 4.3 Series Neuro-Control Scheme.- 4.4 Extensions of Series Neuro-Control Scheme.- 4.4.1 Some Discussions on On-Line Learning.- 4.4.2 Neuromorphic Control Structures.- 4.4.3 Training Configurations.- 4.4.4 Efficient On-Line Training.- 4.4.5 Training Algorithms.- 4.4.6 Evaluation of the Training.- Algorithms through Simulations.- 4.5 Parallel Control Scheme.- 4.5.1 Learning Algorithm for Parallel Control Scheme.- 4.6 Feedback Error Learning Algorithm.- 4.7 Extension of the Parallel Type Neuro-Controller.- 4.7.1 Description of Control System.- 4.7.2 Linearized Control System.- 4.7.3 Control Systems with Neural Networks.- 4.7.4 Nonlinear Observer by Neural Network.- 4.7.5 Nonlinear Controller by Neural Network.- 4.7.6 Numerical Simulations.- 4.8 Self-Tuning Neuro-Control Scheme.- 4.9 Self-Tuning PID Neuro-Controller.- 4.9.1 Derivation of the Self-Tuning PID Type Neuro-Controller.- 4.9.2 Simulation Examples.- 4.10 Emulator and Controller Neuro-Control Scheme.- 4.10.1 Off-Line Training of the Neuro-Controller and Emulator.- 4.10.2 On-Line Learning.- 4.11 Conclusions.- 4.12 References.- 5 Neuro-Control Applications.- 5.1 Introduction.- 5.2 Application of Neuro-Control to a Water-Bath Process and Comparison with Alternative Control Schemes.- 5.2.1 Introduction.- 5.2.2 Description of the Water Bath Temperature Control System.- 5.2.3 Neuro-Control Scheme.- 5.2.4 Fuzzy Logic Control Scheme.- 5.2.5 Generalized Predictive Control Scheme.- 5.2.6 Experimental Results and Discussions.- 5.2.7 Conclusions.- 5.3 Stabilizing an Inverted Pendulum by Neural Networks.- 5.3.1 Introduction.- 5.3.2 Description of the Inverted Pendulum System.- 5.3.3 Initial Start-Up Control Using Fuzzy Logic.- 5.3.4 Using Optimal Control Strategy for the Stabilization of the Inverted Pendulum.- 5.3.5 Fine Improvement by Using Neural Networks.- 5.3.6 Conclusions.- 5.4 Speed Control of an Electric Vehicle by Self-Tuning PID Neuro-Controller.- 5.4.1 Introduction.- 5.4.2 The Electric Vehicle Control System.- 5.4.3 Self-Tuning PID Type Neuro-Controller.- 5.4.4 Application to Speed Control of Electric Vehicle.- 5.4.5 Conclusions.- 5.5 MIMO Furnace Control with Neural Networks.- 5.5.1 Introduction.- 5.5.2 Description of Furnace Control System.- 5.5.3 The Neuro-Control Scheme.- 5.5.4 Experiments and Discussions.- 5.5.5 Conclusions.- 5.6 Concluding Remarks.- 5.7 References.- Program List.

Neural Network Assisted Computationally Simple PI $^\lambda$ D $^\mu$ Control of a Quadrotor UAV

Abstract: The applications of Unmanned Aerial Vehicles (UAVs) require robust control schemes that can alleviate disturbances such as model mismatch, wind disturbances, measurement noise, and the effects of changing electrical variables, e.g., the loss in the battery voltage. Proportional Integral and Derivative (PID) type controller with noninteger order derivative and integration is proposed as a remedy. This paper demonstrates that a neural network can be trained to provide the coefficients of a Finite Impulse Response (FIR) type approximator, that approximates to the response of a given analog PIλDμ controller having time varying action coefficients and differintegration orders. The results obtained show that the neural network aided FIR type controller is very successful in driving the vehicle to prescribed trajectories accurately. The response of the proposed scheme is highly similar to the response of the target PIλDμ controller and the computational burden of the proposed scheme is very low.

Note on fractional-order proportional–integral–differential controller design

Abstract: This study deals with the design of fractional-order proportional-integral-differential (PID) controllers. Two design techniques are presented for tuning the parameters of the controller. The first method uses the idea of the Ziegler-Nichols and the A-stro-m-Ha-gglund methods. In order to achieve required performances, two non-linear equations are derived and solved to obtain the fractional orders of the integral term and the derivative term of the fractional-order PID controller. Then, an optimisation strategy is applied to obtain new values of the controller parameters, which give improved step response. The second method is related with the robust fractional-order PID controllers. A design procedure is given using the Bode envelopes of the control systems with parametric uncertainty. Five non-linear equations are derived using the worst-case values obtained from the Bode envelopes. Robust fractional-order PID controller is designed from the solution of these equations. Simulation examples are provided to show the benefits of the methods presented.

On Zero Steady-State Error Voltage Control of Single-Phase PWM Inverters With Different Load Types

Abstract: This paper comprehensively investigates and compares different multiloop linear control schemes for single-phase pulsewidth modulation inverters, both in stationary and synchronous (d -q) frames, by focusing on their steady-state error under different loading conditions. Specifically, it is shown how proportional plus resonant (P + R) control and load current feedback (LCF) control can, respectively, improve the steady-state and transient performance of the inverter, leading to the proposal of a PID + R + LCF control scheme. Furthermore, the LCF control and capacitive current feedback control schemes are shown to be subject to stability issues under second and higher order filter loads. Additionally, the equivalence between the stationary frame and d -q frame controllers is discussed depending on the orthogonal term generation method, and a d-q frame voltage control strategy is proposed eliminating the need for the generation of this orthogonal component. This is achieved while retaining all the advantages of operating in the synchronous d-q frame, i.e., zero steady-state error and ease of implementation. All theoretical findings are validated experimentally using a 1.5 kW laboratory prototype.

Introduction to single-input, single-output fractional control

Abstract: This is a tutorial study to introduce fractional control to a reader with a background in control theory. Fractional controllers are those making use of fractional-order derivatives and integrals, and have been receiving increased attention over the last few years because of the robust performance (in the face of plant gain variations and even plant uncertainties in general) they can achieve. The study covers the fundamentals of the theory of derivatives and integrals with arbitrary real or complex orders, fractional transfer functions and their approximations, identification of fractional transfer function models from experimental data, first- and second-generation Crone controller, fractional proportional-integral-derivative (PID) control and third-generation Crone control.

Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators

Abstract: Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. This paper presents a control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so-called sliding mode control, SMC, approach. The motivation for using SMC in robotics mainly relies on its appreciable features, such as design simplicity and robustness. Yet, the chattering effect, typical of the conventional SMC, can be destructive. In this paper, this problem is suitably circumvented by adopting an adaptive fuzzy sliding mode control, AFSMC, approach with a proportional-integral-derivative, PID sliding surface. For this proposed approach, we have used a fuzzy logic control to generate the hitting control signal. Moreover, the output gain of the fuzzy sliding mode control, FSMC, is tuned on-line by a supervisory fuzzy system, so the chattering is avoided. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. Numerical simulations using the dynamic model of a 3 DOF planar rigid robot manipulator with uncertainties show the effectiveness of the approach in trajectory tracking problems. The simulation results that are compared with the results of conventional SMC with PID sliding surface indicate that the control performance of the robot system is satisfactory and the proposed AFSMC can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances.

Performance Investigation of Neural-Network-Based Unified Power-Quality Conditioner

Abstract: The application of artificial intelligence is growing fast in the area of power electronics and drives. The artificial neural network (ANN) is considered as a new tool to design control circuitry for power-quality (PQ) devices. In this paper, the ANN-based controller is designed for the current control of the shunt active power filter and trained offline using data from the conventional proportional-integral controller. A digital-signal-processor-based microcontroller is used for the real-time simulation and implementation of the control algorithm. An exhaustive simulation study is carried out to investigate the performance of the ANN controller and compare its performance with the conventional PI controller results. The system performance is also verified experimentally on a prototype model developed in the laboratory.

Linear and Nonlinear Control of Small-Scale Unmanned Helicopters

Abstract: 1 Introduction.- 1.1 Background Information.- 1.2 The Mathematical Problem ..- 1.3 Controller Designs.- 1.3.1 Linear Controller Design.- 1.3.2 Nonlinear Controller Design.- 1.4 Outline of the Book.- 2 Review of Linear and Nonlinear Controller Designs.- 2.1 Linear Controller Designs.- 2.2 Nonlinear Controller Design.- 2.3 Remarks.- 3 Helicopter Basic Equations of Motion.- 3.1 Helicopter Equations of Motion.- 3.2 Position and Orientation of the Helicopter.- 3.2.1 Helicopter Position Dynamics.- 3.2.2 Helicopter Orientation Dynamics.- 3.3 Complete Helicopter Dynamics.- 3.4 Remarks.- 4 Simplified Rotor Dynamics.- 4.1 Introduction.- 4.2 Blade Motion.- 4.3 Swashplate Mechanism.- 4.4 Fundamental Rotor Aerodynamics.- 4.5 Flapping Equations of Motion.- 4.6 Rotor Tip-Path-Plane Equation.- 4.7 First Order Tip-Path-Plane Equations.- 4.8 Main Rotor Forces and Moments.- 4.9 Remarks.- 5 Frequency Domain System Identification.- 5.1 Mathematical Modeling.- 5.1.1 First Principles Modeling.- 5.1.2 System Identification Modeling.- 5.2 Frequency Domain System Identification.- 5.3 Advantages of the Frequency Domain Identification.- 5.4 Helicopter Identification Challenges.- 5.5 Frequency Response and the Coherence Function.- 5.6 The CIFER c Package.- 5.7 Time History Data and Excitation Inputs.- 5.8 Linearization of the Equations of Motion.- 5.9 Stability and Control Derivatives.- 5.10 Model Identification.- 5.10.1 Experimental Platform.- 5.10.2 Parametrized State Space Model.- 5.10.3 Identification Setup.- 5.10.4 Time Domain Validation.- 5.11 Remarks.- 6 Linear Tracking Controller Design for Small-Scale Unmanned Helicopters.- 6.1 Helicopter Linear Model.- 6.2 Linear Controller Design Outline.- 6.3 Decomposing the System.- 6.4 Velocity and Heading Tracking Controller Design.- 6.4.1 Lateral-Longitudinal Dynamics.- 6.4.2 Yaw-Heave Dynamics.- 6.4.3 Stability of the Complete System Error Dynamics.- 6.5 Position and Heading Tracking.- 6.6 PID Controller Design.- 6.7 Experimental Results.- 6.8 Remarks.- 7 Nonlinear Tracking Controller Design for Unmanned Helicopters.- 7.1 Introduction.- 7.2 Helicopter Nonlinear Model.- 7.2.1 Rigid Body Dynamics.- 7.2.2 ExternalWrench Model.- 7.2.3 Complete Rigid Body Dynamics.- 7.3 Translational Error Dynamics.- 7.4 Attitude Error Dynamics.- 7.4.1 Yaw Error Dynamics.- 7.4.2 Orientation Error Dynamics.- 7.4.3 Angular Velocity Error Dynamics.- 7.5 Stability of the Attitude Error Dynamics.- 7.6 Stability of the Translational Error Dynamics.- 7.7 Numeric Simulation Results.- 7.8 Remarks.- 8 Time Domain Parameter Estimation and Applied Discrete Nonlinear Control for Small-Scale Unmanned Helicopters.- 8.1 Introduction.- 8.2 Discrete System Dynamics.- 8.3 Discrete Backstepping Algorithm.- 8.3.1 Angular Velocity Dynamics.- 8.3.2 Translational Dynamics.- 8.3.3 Yaw Dynamics.- 8.4 Parameter Estimation Using Recursive Least Squares.- 8.5 Parametric Model.- 8.6 Experimental Results.- 8.6.1 Time History Data and Excitation Inputs.- 8.6.2 Validation.- 8.6.3 Control Design.- 8.7 Remarks.- 9 Time Domain System Identification for Small-Scale Unmanned Helicopters Using Fuzzy Models.- 9.1 Introduction.- 9.2 Takagi-Sugeno Fuzzy Models.- 9.3 Proposed Takagi-Sugeno System for Helicopters.- 9.4 Experimental Results.- 9.4.1 Tunning of the Membership Function Parameters.- 9.4.2 Validation.- 10 Comparison Studies.- 10.1 Summary of the Controller Designs.- 10.2 Experimental Results.- 10.3 First Maneuver: Forward Flight.- 10.4 Second Maneuver: Aggressive Forward Flight.- 10.5 Third Maneuver: 8 Shaped Trajectory.- 10.6 Fourth Maneuver: Pirouette Trajectory.- 10.7 Remarks.- 11 Epilogue.- 11.1 Introduction.- 11.2 Advantages and Novelties of the Designs.- 11.3 Testing and Implementation.- 11.4 Remarks.- A Fundamentals of Backstepping Control.- A.1 Integrator Backstepping.- A.2 Example of a Recursive Backstepping Design.- References.

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

Abstract: In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.