Showing papers on "PID controller published in 2016"

Vehicle Lateral Dynamics Control Through AFS/DYC and Robust Gain-Scheduling Approach

Abstract: In this paper, we investigate the combined active front-wheel steering/direct yaw-moment control for the improvement of vehicle lateral stability and vehicle handling performance. A more practical assumption in this work is that the longitudinal velocity is not constant but varying within a range. Both the nonlinear tire model and the variation of longitudinal velocity are considered in vehicle system modeling. A linear-parameter-varying model with norm-bounded uncertainties is obtained. To track the system reference, a generalized proportional-integral (PI) control law is proposed. Since it is difficult to get the analytic solution for the PI gains, an augmented system is developed, and the PI control is then converted into the state-feedback control for the augmented system. Both the stability and the energy-to-peak performance of the augmented system are explored. Based on the analysis results, the controller-gain tuning method is proposed. The proposed control law and controller design method are illustrated via an electric vehicle model.

Load frequency control of interconnected power system using grey wolf optimization

Abstract: In this article an attempt has been made to solve load frequency control (LFC) problem in an interconnected power system network equipped with classical PI/PID controller using grey wolf optimization (GWO) technique. Initially, proposed algorithm is used for two-area interconnected non-reheat thermal-thermal power system and then the study is extended to three other realistic power systems, viz. (i) two-area multi-units hydro-thermal, (ii) two-area multi-sources power system having thermal, hydro and gas power plants and (iii) three-unequal-area all thermal power system for better validation of the effectiveness of proposed algorithm. The generation rate constraint (GRC) of the steam turbine is included in the system modeling and dynamic stability of aforesaid systems is investigated in the presence of GRC. The controller gains are optimized by using GWO algorithm employing integral time multiplied absolute error (ITAE) based fitness function. Performance of the proposed GWO algorithm has been compared with comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) and other similar meta-heuristic optimization techniques available in literature for similar test system. Moreover, to demonstrate the robustness of proposed GWO algorithm, sensitivity analysis is performed by varying the operating loading conditions and system parameters in the range of ± 50 % . Simulation results show that GWO has better tuning capability than CLPSO, EPSDE and other similar population-based optimization techniques.

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO.

Abstract: This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.

Adaptive Incremental Nonlinear Dynamic Inversion for Attitude Control of Micro Air Vehicles

Abstract: Incremental nonlinear dynamic inversion is a sensor-based control approach that promises to provide high-performance nonlinear control without requiring a detailed model of the controlled vehicle. In the context of attitude control of micro air vehicles, incremental nonlinear dynamic inversion only uses a control effectiveness model and uses estimates of the angular accelerations to replace the rest of the model. This paper provides solutions for two major challenges of incremental nonlinear dynamic inversion control: how to deal with measurement and actuator delays, and how to deal with a changing control effectiveness. The main contributions of this article are 1) a proposed method to correctly take into account the delays occurring when deriving angular accelerations from angular rate measurements; 2) the introduction of adaptive incremental nonlinear dynamic inversion, which can estimate the control effectiveness online, eliminating the need for manual parameter estimation or tuning; and 3) the incorp...

A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System

Abstract: This paper presents a new optimization algorithm based on human society’s intelligent contests. FIFA World Cup is an international association football competition competed by the senior men’s national teams. This contest is one of the most significant competitions among the humans in which people/teams try hard to overcome the others to earn the victory. In this competition there is only one winner which has the best position rather than the others. This paper introduces a new technique for optimization of mathematic functions based on FIFA World Cup competitions. The main difficulty of the optimization problems is that each type of them can be interpreted in a specific manner. World Cup Optimization (WCO) algorithm has a number of parameters to solve any type of problems due to defined parameters. For analyzing the system performance, it is applied on some benchmark functions. It is also applied on an optimal control problem as a practical case study to find the optimal parameters of PID controller with considering to the nominal operating points $$(K_{g}$$ , $$T_{g})$$ changes of the AVR system. The main objective of the proposed system is to minimize the steady-state error and also to improve the transient response of the AVR system by optimal PID controller. Optimal values of the PID controller which are achieved by WCO algorithm are then compared with particle swarm optimization and imperialist competitive algorithm in different situations. Finally for illustrating the system capability against the disturbance, it is applied on a generator with disturbance on it and the results are compared by the other algorithms. The simulation results show the excellence of WCO algorithm performance into the nature base and other competitive algorithms.

Model-free–based terminal SMC of quadrotor attitude and position

Abstract: In this paper, a model-free–based terminal sliding-mode control (MFTSMC) strategy is developed to control the attitude and position of a quadrotor whose model includes parameter variations, uncertainties, and external disturbances. The proposed MFTSMC combines a model-free control approach with a sliding-mode technique and makes possible to eliminate the tracking error in a finite time. To demonstrate the performance and effectiveness of the proposed MFTSMC, numerical simulation results have been obtained and compared with corresponding results for PID, backstepping and sliding-mode controls.

Design and analysis of tilt integral derivative controller with filter for load frequency control of multi-area interconnected power systems.

Abstract: In this paper, a novel Tilt Integral Derivative controller with Filter (TIDF) is proposed for Load Frequency Control (LFC) of multi-area power systems. Initially, a two-area power system is considered and the parameters of the TIDF controller are optimized using Differential Evolution (DE) algorithm employing an Integral of Time multiplied Absolute Error (ITAE) criterion. The superiority of the proposed approach is demonstrated by comparing the results with some recently published heuristic approaches such as Firefly Algorithm (FA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) optimized PID controllers for the same interconnected power system. Investigations reveal that proposed TIDF controllers provide better dynamic response compared to PID controller in terms of minimum undershoots and settling times of frequency as well as tie-line power deviations following a disturbance. The proposed approach is also extended to two widely used three area test systems considering nonlinearities such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB). To improve the performance of the system, a Thyristor Controlled Series Compensator (TCSC) is also considered and the performance of TIDF controller in presence of TCSC is investigated. It is observed that system performance improves with the inclusion of TCSC. Finally, sensitivity analysis is carried out to test the robustness of the proposed controller by varying the system parameters, operating condition and load pattern. It is observed that the proposed controllers are robust and perform satisfactorily with variations in operating condition, system parameters and load pattern.

Inner–Outer Loop Control for Quadrotor UAVs With Input and State Constraints

Abstract: The constrained control of unmanned aerial vehicles (UAVs) is a challenging task due to their nonlinear and underactuacted dynamics. This brief focuses on the position control of a quadrotor UAV with state and input constraints using an inner–outer loop control structure. The outer loop generates a saturated thrust, and the reference roll and pitch angles, while the inner loop is designed to follow these reference angles using a traditional PID controller. Assuming perfect inner loop tracking, the outer loop nested saturation controller guarantees global asymptotic stability for output regulation and tracking. The effect of nonideal inner loop tracking on closed-loop stability is analyzed. The proposed method is experimentally validated on an indoor quadrotor platform.

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Abstract: Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%.

An Adaptive Control Strategy for Low Voltage Ride Through Capability Enhancement of Grid-Connected Photovoltaic Power Plants

Abstract: This paper presents a novel application of continuous mixed $p$ -norm (CMPN) algorithm-based adaptive control strategy with the purpose of enhancing the low voltage ride through (LVRT) capability of grid-connected photovoltaic (PV) power plants. The PV arrays are connected to the point of common coupling (PCC) through a DC-DC boost converter, a DC-link capacitor, a grid-side inverter, and a three-phase step up transformer. The DC-DC converter is used for a maximum power point tracking operation based on the fractional open circuit voltage method. The grid-side inverter is utilized to control the DC-link voltage and terminal voltage at the PCC through a vector control scheme. The CMPN algorithm-based adaptive proportional-integral (PI) controller is used to control the power electronic circuits due to its very fast convergence. The proposed algorithm updates the PI controller gains online without the need to fine tune or optimize. For realistic responses, the PV power plant is connected to the IEEE 39-bus New England test system. The effectiveness of the proposed control strategy is compared with that obtained using Taguchi approach-based an optimal PI controller taking into account subjecting the system to symmetrical, unsymmetrical faults, and unsuccessful reclosing of circuit breakers due to the existence of permanent fault. The validity of adaptive control strategy is extensively verified by the simulation results, which are carried out using PSCAD/EMTDC software. With the proposed adaptive-controlled PV power plants, the LVRT capability of such system can be improved.

Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

Abstract: For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

A novel auto-tuning method for fractional order PI/PD controllers.

Abstract: Fractional order PID controllers benefit from an increasing amount of interest from the research community due to their proven advantages. The classical tuning approach for these controllers is based on specifying a certain gain crossover frequency, a phase margin and a robustness to gain variations. To tune the fractional order controllers, the modulus, phase and phase slope of the process at the imposed gain crossover frequency are required. Usually these values are obtained from a mathematical model of the process, e.g. a transfer function. In the absence of such model, an auto-tuning method that is able to estimate these values is a valuable alternative. Auto-tuning methods are among the least discussed design methods for fractional order PID controllers. This paper proposes a novel approach for the auto-tuning of fractional order controllers. The method is based on a simple experiment that is able to determine the modulus, phase and phase slope of the process required in the computation of the controller parameters. The proposed design technique is simple and efficient in ensuring the robustness of the closed loop system. Several simulation examples are presented, including the control of processes exhibiting integer and fractional order dynamics.

MIMO PID tuning via iterated LMI restriction

Abstract: We formulate multi-input multi-output (MIMO) proportional-integral-derivative (PID) controller design as an optimization problem that involves nonconvex quadratic matrix inequalities. We propose a simple method that replaces the nonconvex matrix inequalities with a linear matrix inequality (LMI) restriction, and iterates to convergence. This method can be interpreted as a matrix extension of the convex-concave procedure, or as a particular majorization-minimization (MM) method. Convergence to a local minimum can be guaranteed. While we do not know that the resulting controller is globally optimal, the method works well in practice, and provides a simple automated method for tuning MIMO PID controllers. The method is readily extended in many ways, for example to the design of more complex, structured controllers.

Design of a fractional order PID controller using GBMO algorithm for load-frequency control with governor saturation consideration.

Abstract: Load-frequency control is one of the most important issues in power system operation. In this paper, a Fractional Order PID (FOPID) controller based on Gases Brownian Motion Optimization (GBMO) is used in order to mitigate frequency and exchanged power deviation in two-area power system with considering governor saturation limit. In a FOPID controller derivative and integrator parts have non-integer orders which should be determined by designer. FOPID controller has more flexibility than PID controller. The GBMO algorithm is a recently introduced search method that has suitable accuracy and convergence rate. Thus, this paper uses the advantages of FOPID controller as well as GBMO algorithm to solve load-frequency control. However, computational load will higher than conventional controllers due to more complexity of design procedure. Also, a GBMO based fuzzy controller is designed and analyzed in detail. The performance of the proposed controller in time domain and its robustness are verified according to comparison with other controllers like GBMO based fuzzy controller and PI controller that used for load-frequency control system in confronting with model parameters variations.