Chong-Wei Zheng

Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization

Design of an effective and efficient fractional order PID (FOPID) controller, as a generalization of a standard PID controller based on fractional order calculus, for an industrial control system to obtain high-quality performances is of great theoretical and practical significance. From the perspective of multi-objective optimization, this paper presents a novel FOPID controller design method based on an improved multi-objective extremal optimization (MOEO) algorithm for an automatic regulator voltage (AVR) system. The problem of designing FOPID controller for AVR is firstly formulated as a multi-objective optimization problem with three objective functions including minimization of integral of absolute error (IAE), absolute steady-state error, and settling time. Then, an improved MOEO algorithm is proposed to solve this problem by adopting individual-based iterated optimization mechanism and polynomial mutation (PLM). From the perspective of algorithm design, the proposed MOEO algorithm is relatively simpler than NSGA-II and single-objective evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), chaotic anti swarm (CAS) due to its fewer adjustable parameters. Furthermore, the superiority of proposed MOEO-FOPID controller to NSGA-II-based FOPID, single-objective evolutionary algorithms-based FOPID controllers, MOEO-based and NSGA-II-based PID controllers is demonstrated by extensive experimental results on an AVR system in terms of accuracy and robustness.

Design of multivariable PID controllers using real-coded population-based extremal optimization

Abstract The issue of designing and tuning an effective and efficient multivariable PID controller for a multivariable control system to obtain high-quality performance is of great theoretical importance and practical significance. As a novel evolutionary algorithm inspired from statistical physics and co-evolution, extremal optimization (EO) has successfully applied to a variety of optimization problems while the applications of EO into the design of multivariable PID and PI controllers are relatively rare. This paper presents a novel real-coded population-based EO (RPEO) method for the design of multivariable PID and PI controllers. The basic idea behind RPEO is based on population-based iterated optimization process consisting of the following key operations including generation of a real-coded random initial population by encoding the parameters of a multivariable PID or PI controller into a set of real values, evaluation of the individual fitness by using a novel and reasonable control performance index, generation of new population based on multi-non-uniform mutation and updating the population by accepting the new population unconditionally. From the perspectives of simplicity and accuracy, the proposed RPEO algorithm is demonstrated to outperform other reported popular evolutionary algorithms, such as real-coded genetic algorithm (RGA) with multi-crossover or simulated binary crossover, differential evolution (DE), modified particle swarm optimization (MPSO), probability based discrete binary PSO (PBPSO), and covariance matrix adaptation evolution strategy (CMAES) by the experimental results on the benchmark multivariable binary distillation column plant.

An improved multi-objective population-based extremal optimization algorithm with polynomial mutation

As a recently developed evolutionary algorithm inspired by far-from-equilibrium dynamics of self-organized criticality, extremal optimization (EO) has been successfully applied to a variety of benchmark and engineering optimization problems. However, there are only few reported research works concerning the applications of EO in the field of multi-objective optimization. This paper presents an improved multi-objective population-based EO algorithm with polynomial mutation called IMOPEO-PLM to solve multi-objective optimization problems (MOPs). Unlike the previous multi-objective versions based on EO, the proposed IMOPEO-PLM adopts population-based iterated optimization, a more effective mutation operation called polynomial mutation, and a novel and more effective mechanism of generating new population. From the design perspective of multi-objective evolutionary algorithms (MOEAs), IMOPEO-PLM is relatively simpler than other reported competitive MOEAs due to its fewer adjustable parameters and only mutation operation. Furthermore, the extensive experimental results on some benchmark MOPs show that IMOPEO-PLM performs better than or at least competitive with these reported popular MOEAs, such as MOPEO, MOEO, NSGA-II, A-MOCLPSO, PAES, SPEA, SPEA2, SMS-EMOA, SMPSO, and MOEA/D-DE, by using nonparametric statistical tests, e.g., Kruskal-Wallis test, Mann-Whitney U test, Friedman and Quade tests, in terms of some commonly-used quantitative performance metrics, e.g., convergence, diversity (spread), hypervolume, generational distance, inverted generational distance.

Binary-coded extremal optimization for the design of PID controllers

Abstract Design of an effective and efficient PID controller to obtain high-quality performances such as high stability and satisfied transient response is of great theoretical and practical significance. This paper presents a novel design method for PID controllers based on the binary-coded extremal optimization algorithm (BCEO). The basic idea behind the proposed method is encoding the PID parameters into a binary string, evaluating the control performance by a more reasonable index than the integral of absolute error (IAE) and the integral of time weighted absolute error (ITAE), updating the solution by the selection based on power-law probability distribution and binary mutation for the selected bad elements. The experimental results on some benchmark instances have shown that the proposed BCEO-based PID design method is simpler, more efficient and effective than the existing popular evolutionary algorithms, such as the adaptive genetic algorithm (AGA), the self-organizing genetic algorithm (SOGA) and probability based binary particle swarm optimization (PBPSO) for single-variable plants. Moreover, the superiority of the BCEO method to AGA and PBPSO is demonstrated by the experimental results on the multivariable benchmark plant.

An Improved Real-Coded Population-Based Extremal Optimization Method for Continuous Unconstrained Optimization Problems

As a novel evolutionary optimization method, extremal optimization (EO) has been successfully applied to a variety of combinatorial optimization problems. However, the applications of EO in continuous optimization problems are relatively rare. This paper proposes an improved real-coded population-based EO method (IRPEO) for continuous unconstrained optimization problems. The key operations of IRPEO include generation of real-coded random initial population, evaluation of individual and population fitness, selection of bad elements according to power-law probability distribution, generation of new population based on uniform random mutation, and updating the population by accepting the new population unconditionally. The experimental results on 10 benchmark test functions with the dimension have shown that IRPEO is competitive or even better than the recently reported various genetic algorithm (GA) versions with different mutation operations in terms of simplicity, effectiveness, and efficiency. Furthermore, the superiority of IRPEO to other evolutionary algorithms such as original population-based EO, particle swarm optimization (PSO), and the hybrid PSO-EO is also demonstrated by the experimental results on some benchmark functions.

Data Reconciliation and Parameter Estimation for Air Separation Process with Multiple Operation Conditions

The economic performance of real-time optimization system is influenced by the accuracy of the model. If the process model is not consistent with the plant measurement data, it would lead to the offset between the true plant optimum and the predicted optimum. So it is important to use data reconciliation and parameter estimation to make sure that the process model is consistent with the true plant. Data reconciliation and parameter estimation for air separation process with multiple operations is needed in industrial process. The methodology of data reconciliation and parameter estimation for air separation process with multiple operation conditions has been presented. First, the characteristics and problems in air separation process are described. And then, methodology, based on multiple operation conditions clustering, is proposed and used in air separation process. The effectiveness of proposed methodology can be demonstrated by the results of numerical experiment.

Implementation of 4-axis stepper motor control system based on TC5540

Framework of 4-axis stepper motor control system was constructed based on the characteristics of TC5540 motion controller in this paper, and then the wiring diagram for TC5540, stepper motor diver and 4-axis stepper motor was designed and implemented. From the example, it can be demonstrated that the control system can be worked by programming. The proposed control system is effective, flexible, reliable, and accurate.

Application of Petri net based deadlock prevention method to a real spectacles production system

Designing deadlock prevention controllers for flexible manufacturing systems (FMS) is of theoretical and practical importance. This paper extends the basic idea of our recently proposed deadlock prevention method to a real spectacle production system, a typical FMS. First of all, this real system is modeled and analyzed by Petri net (PN). Then, we apply our recently proposed method to design the deadlock prevention PN-based controller for this real system. The finally obtained controller is deadlock-free and maximally permissive.