Multi-objective PID Controller Research Articles

Comparison of Different New Chaotic Systems in NSGAⅡ for Multi-Objective PID Controller Tuning of Hydropower Unit

Comparison of Different New Chaotic Systems in NSGAⅡ for Multi-Objective PID Controller Tuning of Hydropower Unit

Multi-objective PID Controller Tuning for Multi-model Control of Nonlinear Systems

Multi-objective PID Controller Tuning for Multi-model Control of Nonlinear Systems

Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following

This paper presents a multi-objective digital PID controller design method using the parameter space approach of robust control. Absolute stability is treated first by finding the digital PID controller gain parameter space corresponding to closed loop poles being inside the unit circle. Additionally, phase margin, gain margin and a mixed sensitivity bound are treated as frequency domain constraints. Determination of digital PID controller parameter space regions satisfying these constraints is presented. All of these regions are superimposed to obtain a multi-objective digital PID controller gain parameter space solution region. The path following controller design of an automated driving vehicle is used as an example to illustrate the method. This multi-objective parameter space design approach can be extended to other digital controller forms also.

Improving ride comfort for vibratory roller utilizing semi-active hydraulic cab mounts with control optimization

Hydraulic mounts can provide a better vibration attenuation performance than elastomeric mounts especially in the low frequency range. However, it is incapable of providing a response-dependent damper for the mount system to improve the ride comfort. In this study, a semi-active hydraulic cab mount (SHM) with control optimization was designed to improve ride comfort for the heavy vibratory roller. And a 7-DOF non-linear dynamic model of the vehicle was established for evaluation of the performance of hydraulic cab mounts based on different control optimization algorithms. To simulate roughness height the ISO level D road surface and deformed soil model were employed under the compaction work condition. The optimization study for two performance objectives as measured by responses of the vertical driver’s seat and cab pitch angle was carried out. It was shown that the SHM optimized by the fuzzy logic and proportional, integral, derivative controller methods (FLC-PID) giving best optimum values of the objective vector as compare to by multi-objective genetic algorithm (MOGA) and PID controller based on genetic algorithm (GA-PID).

Multiobjective PID controller design for active suspension system: scalarization approach

In this study, the PID tuning method (controller design scheme) is proposed for a linear quarter model of active suspension system installed on the vehicles. The PID tuning scheme is considered as a multiobjective problem which is solved by converting this multiobjective problem into single objective problem with the aid of scalarization approaches. In the study, three different scalarization approaches are used and compared to each other. These approaches are called linear scalarization (weighted sum), epsilon-constraint and Benson’s methods. The objectives of multiobjective optimization are selected from the time-domain properties of the transient response of the system which are overshoot, rise time, peak time and error (in total there are four objectives). The aim of each objective is to minimize the corresponding property of the time response of the system. First, these four objective is applied to the scalarization functions and then single objective problem is obtained. Finally, these single objective problems are solved with the aid of heuristic optimization algorithms. For this purpose, four optimization algorithms are selected, which are called Particle Swarm Optimization, Differential Evolution, Firefly, and Cultural Algorithms. In total,twelve implementations are evaluated with the same number of iterations. In this study, the aim is to compare the scalarization approaches and optimization algorithm on active suspension control problem. The performance of the corresponding cases (implementations) are numerically and graphically demonstrated on transient responses of the system.

Set-Point Tracking and Multi-Objective Optimization-Based PID Control for the Goethite Process

The goethite process is complicated since its chemical reactions interact with each other, making its control and optimization in industrial production difficult. The goal of the goethite process is to make the outlet ion concentration satisfy the technical requirements with minimal process consumption. To simplify the difficulties of optimization in the goethite process, an optimization method based on a set-point tracking strategy is proposed. The set-point tracking strategy is used to transform the complex state constraints into an additional objective. Therefore, the single optimization control problem for the goethite process is transformed into a bi-objective optimization control problem. Furthermore, PID controllers are adopted to control the addition amounts of zinc oxide and oxygen in the goethite process. The optimal parameters of the PID controllers are obtained via a multi-objective state transition algorithm (MOSTA). The performance of MOSTA is verified by several benchmark test functions with performance matrices. The control performance reveals that the proposed method is an effective way to control the process and can not only reduce the zinc oxide and oxygen addition amounts compared with manual operation and traditional PID control but also reject disturbances. The proposed method can satisfy the industrial requirements with less energy consumption.

Application of Intensified Current Search to Multiobjective PID Controller Optimization

Application of Intensified Current Search to Multiobjective PID Controller Optimization

Optimal Design of PID Controller for Doubly-Fed Induction Generator-Based Wave Energy Conversion System Using Multi-Objective Particle Swarm Optimization

This paper presents the complete modeling and simulation of Wave Energy Conversion System (WECS) driven doubly-fed induction generator with a closed-loop vector control system. Two Pulse Width Modulated voltage source (PWM) converters for both rotor- and stator-side converters have been connected back to back between the rotor terminals and utility grid via common dc link. The closed-loop vector control system is normally controlled by a set of PID controllers which have an important influence on the system dynamic performance. This paper presents a Multi-objective optimal PID controller design of a doubly-fed induction generator (DFIG) wave energy system connected to the electrical grid using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). PSO and GA are used to optimize the controller parameters of both the rotor and grid-side converters to improve the transient operation of the DFIG wave energy system under a fault condition as compared with the conventional methods to design PID controllers.

A multi-objective optimal PID control for a nonlinear system with time delay

It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design.

Tuning of a Proportional-Integral-Derivative Controller using Multi-Objective Non Dominated Sorting Particle Swarm Optimization Applied to pH Control in Continuous Stirred Tank Reactor

Problem statement: Most of the control engineering problems are characterized by several, contradicting, conflicting objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion and using Pareto-optimal solutions. Approach: Non-Dominated Sorting Particle Swarm Optimization algorithm (NSPSO) based approach is used in the design of multiobjective PID controller to find the constant proportional-integral-derivative gains for a chemical neutralization plant. The plant considered in this study is highly non-linear and with varying time delay, provides a challenging test bed for nonlinear control problems. Results: Experimental results confirm that a multi-objective, Paretobased GA search gives a better performance than a single objective GA. Conclusion: Finally, the results for single objective and multiobjective optimization using NSPSO for the neutralization plant are compared. Gain scheduled PID controllers are designed from Pareto front obtained with NSPSO which exhibit good disturbance rejection capability.

NSGA-II Based Multiobjective Pid Controller Tuning for Speed Control of DC Motor Drives

NSGA-II Based Multiobjective Pid Controller Tuning for Speed Control of DC Motor Drives

Multi-objective PID control for non-Gaussian stochastic distribution system based on two-step intelligent models

A new method for controlling the shape of the conditional output probability density function (PDF) for general nonlinear dynamic stochastic systems is proposed based on B-spline neural network (NN) model and T-S fuzzy model. Applying NN approximation to the measured PDFs, we transform the concerned problem into the tracking of given weights. Meanwhile, the complex multi-delay T-S fuzzy model with exogenous disturbances, parametric uncertainties and state constraints is used to represent the nonlinear weight dynamics. Moreover, instead of the non-convex design algorithms and PI control, the improved convex linear matrix inequality (LMI) algorithms and the generalized PID controller are proposed such that the multiple control objectives including stability, robustness, tracking performance and state constraint can be guaranteed simultaneously. Simulations are performed to demonstrate the efficiency of the proposed approach.

Load Frequency Control in Interconnected Power System Using Multi-Objective PID Controller

Load Frequency Control in Interconnected Power System Using Multi-Objective PID Controller

GA-based multiobjective PID control for a linear brushless DC motor

This paper presents a robust output tracking control design method for a linear brushless DC motor with modeling uncertainties. Two frequency-domain specifications directly related to the mixed sensitivity function and control energy consumption are imposed to ensure stability and performance robustness. With regard to time-domain specifications, the rise time, maximum overshoot and steady-state error of the step response are considered. A generalized two-parameters proportional, integral, and derivative (PID) control framework is developed via a genetic searching approach ensuring the specifications imposed. The proposed design method is intuitive and practical that offers an effective way to implement simple but robust solutions covering a wide range of plant perturbation and, in addition, provides excellent tracking performance without resorting to excessive control. Extensive experimental and numerical results for a linear brushless motor confirm the proposed control design approach.

PERFORMANCE AND ROBUSTNESS DESIGN OF CONTROL SYSTEMS VIA GENETIC-SEX MULTI-OBJECTIVE OPTIMIZATION

Abstract. In this paper new evolutionary method of solving multi-objective optimization problems is presented. This method utilizes an information about an individual sex for the purpose of distinction between different groups of objectives. In particular, this information is extracted from the fitness of individuals and applied during the parental crossovers in a multi-objective optimization process. Characteristics of this mechanism are discussed and its performance in an exemplary multi-objective PID control optimization task is presented.

Multiobjective PID control for a linear brushless DC motor: an evolutionary approach

A robust output tracking control design for a linear brushless DC motor with modelling uncertainties is presented. Frequency-domain design specifications directly related to the mixed sensitivity function and control energy consumption are imposed to ensure stability and performance robustness. A generalised two-parameter PID control framework is developed via an evolution algorithm, which searches the available solutions over a certain specified domain. The proposed design paradigm is intuitive and practical in the sense that it offers an effective way to implement simple but robust solutions covering a wide range of plant perturbation and, in addition, provides excellent tracking performance without resorting to excessive control. Experimental and numerical studies have been presented to confirm the proposed control design.

Optimal-tuning PID control for industrial systems

Abstract Three optimal-tuning PID controller design schemes are presented for industrial control systems in this paper. They are time-domain optimal-tuning PID control, frequency-domain optimal-tuning PID control and multiobjective optimal-tuning PID control. These schemes can provide optimal PID parameters so that the desired system specifications are satisfied even in case where the system dynamics are time variant or the system operating points change. They are applied to three industrial systems, a hydraulic position control system, a rotary hydraulic speed control system and a gasifier, respectively.

Multiobjective PID control design in uncertain robotic systems using neural network elimination scheme

A PID-type controller incorporating neural network elimination scheme and sliding-mode control action for different objectives including H/sub 2/ tracking performance, H/sub /spl infin// tracking performance, and regional pole constraints is developed in robotic systems under plant uncertainties and external disturbances. The adaptive neural networks are used to compensate the plant uncertainties. The sliding-mode control action is included to eliminate the effect of approximation error via neural network approximation. The sufficient conditions are developed for different objectives in terms of linear matrix inequality (LMI) formulations. The interesting combinations of different objectives are considered in this paper, which include H/sub /spl infin// PID tracking control design with regional pole constraints and mixed H/sub 2//H/sub /spl infin// PID tracking control design with regional pole constraints. These multiobjective PID control problems are characterized in terms of eigenvalue problem (EVP). The EVP can be efficiently solved by the LMI toolbox in Matlab. The proposed methods are simple and the PID control gain for different objectives can be obtained systematically.

Optimal-Tuning PID Control for Industrial Systems

Three optimal-tuning PID controller design schemes are considered for industrial process control in this paper, which are time-domain optimal-tuning PID control, frequency-domain optimal-tuning PID control and multiobjective optimal-tuning PID control. These schemes can provide optimal PID parameters even in case where the process dynamics are time variant. They are applied to three industrial systems: a hydraulic position control system, a rotary hydraulic speed control system and a gasifier.