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Journal ArticleDOI

Tuning of PID controller using optimization techniques for a MIMO process

01 Oct 2017-Vol. 263, Iss: 5, pp 052019
TL;DR: In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process, which are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.
Abstract: In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process. These are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.At first mathematical model of both the process is to be done followed by linearization of the system due to MIMO process and controllers are the major part to control the whole process to our desired point as per the applications so the tuning of the controller plays a major role among the whole process. For tuning of parameters we use two optimizations techniques like Particle Swarm Optimization, Genetic Algorithm. The above techniques are majorly used in different applications to obtain which gives the best among all, we use these techniques to obtain the best tuned values among many. Finally, we will compare the performance of the each process with both the techniques.
Citations
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Journal ArticleDOI
TL;DR: Applying this algorithm to several unconstrained and constrained general benchmarks along with several chemical engineering optimization problems reveal that AMTOA outperforms other well-known methods such as genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and conventional MTOA.
Abstract: This paper presents an adaptive multi-tracker optimization algorithm (AMTOA) for global optimization problems with an emphasis on applications in chemical engineering. To obtain the AMTOA, first, several modifications are performed on the conventional multi-tracker optimization algorithm (MTOA). Then a number of its parameters are considered to be adaptive. The modifications include a novel way of determining the search radius of each global tracker ( $$G_{\text{T}}$$ ), and introducing a more efficacious technique of searching for a new solution by $$G_{\text{T}}$$ s. $$G_{\text{T}}$$ s are the main components of the MTOA which look for the global optimal point ( $${\text{GOP}}$$ ). Additionally, the adaptation rules are employed for $$G_{\text{T}}$$ s search radii and their searching parameters. These modifications lead to increasing the precision of the solution and reliability of the algorithm, both of which are the most important properties of an optimizer. Reducing the number of parameters of MTOA is another advantage of AMTOA. The results of applying this algorithm to several unconstrained and constrained general benchmarks along with several chemical engineering optimization problems reveal that AMTOA outperforms other well-known methods such as genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and conventional MTOA. Additionally, comparing the results of AMTOA to other advanced optimization algorithms such as LSHADE44, MA-ES, and IUDE show its superiority for chemical engineering optimization problems. Thus, the development of AMTOA could be advantageous to the area of chemical engineering.

6 citations

Journal ArticleDOI
TL;DR: The results clearly exhibit the capability of TPO algorithm towards finding the optimum PID parameters for SISO and MIMO process with faster settling time and better performance with respect to other methods.
Abstract: The tuning of proportional–integral–derivative (PID) controller is essential for any control application in order to ensure the best performance by step change or disturbance. This paper presents the tuning of PID controller for single-input single-output (SISO) and multiple-input multiple-output (MIMO) control systems using tree physiology optimization (TPO). TPO is a metaheuristic algorithm inspired from a plant growth system derived based on the idea of plant architecture and Thornley model (TM). The basic principle of TM simplifies the plant growth into shoots and roots part. The plant shoots grow towards sunlight with the help of nutrients supplied by the root system in order to undergo photosynthesis process, a process of converting light photon into carbon. The carbon gain from the shoots extension will be supplied to the root system in order for the root to grow and search for water plus nutrients. As a result, the nutrients are supplied upwards towards shoot system for further extension. This concept runs iteratively in order to ensure optimum plant growth. The iterative search of shoot towards better light supported by the root counterparts leads to an optimization idea of TPO algorithm. TPO also has a unique exploration strategy due to its multiple branches and shoots that can be defined by user. This concept may improve the search mechanism with a better trade-off between diversification and intensification search. A simulation of SISO control system and an industrial application of MIMO control are applied to demonstrate the effectiveness of the proposed algorithm and compared with other optimization methods such as particle swarm optimization, Ziegler–Nichols, Tyreus–Luyben and Chien–Hrones–Reswick methods. The results clearly exhibit the capability of TPO algorithm towards finding the optimum PID parameters for SISO and MIMO process with faster settling time and better performance with respect to other methods.

4 citations


Cites methods from "Tuning of PID controller using opti..."

  • ...There are also several MIMO PID controller tuning methods using metaheuristic algorithms proposed using GA for twin-rotor MIMO system [30], artificial fish algorithm to optimize the PID neural network of coordinated control system [31], PSO to tune quadruple tank reactor [32] and cuckoo search (CS) to tune PID controller of binary distillation column [33]....

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Book ChapterDOI
01 Jan 2019
TL;DR: Many optimization problems are usually NP-hard problems which prevent the implementation of exact solution methodologies, so engineers prefer to use metaheuristics which are able to produce good solutions in a reasonable computation time.
Abstract: Many optimization problems are usually NP-hard problems which prevent the implementation of exact solution methodologies. This is the reason why engineers prefer to use metaheuristics which are able to produce good solutions in a reasonable computation time. The metaheuristic approaches can be separated into two classes: the local search techniques and the global ones. Among the local search techniques, the taboo search and the simulated annealing are the most known. A possible acceleration of the convergence can be obtained by using tunneling algorithms. Concerning the global methods, the Genetic or Evolution Algorithms (GA), Ant Colony Optimization (ACO), and the Particle Swarm Optimization (PSO) are the most known.
References
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Book
01 Jan 1984
TL;DR: This paper presents a meta-analysis of the Dynamic and Static Behavior of Chemical Processes and the design of Control Systems for Multivariable Processes using digital computers.
Abstract: 1 The Control of a Chemical Process: Its Characteristics and Associated Problems 2 Modeling the Dynamic and Static Behavior of Chemical Processes 3 Analysis of the Dynamic Behavior of Chemical Processes 4 Analysis and Design of Feedback Control Systems 5 Analysis and Design of Advanced Control Systems 6 Design of Control Systems for Multivariable Processes 7 Process Control Using Digital Computers

796 citations

Dissertation
01 Jan 2012
TL;DR: The development of a multi-objective genetic algorithm to optimise the PID controller parameters for a complex and unstable system is presented and a new genetic algorithm, called the Global Criterion Genetic Algorithm (GCGA) has been proposed and is compared with the state-of-the-art Non-dominated Sorting Genetic Al algorithm (NSGA-II) in several standard test problems.
Abstract: Proportional-Integral-Derivative (PID) controller is one of the most popular controllers applied in industries. However, despite the simplicity in its structure, the PID parameter tuning for high-order, unstable and complex plants is difficult. When dealing with such plants, empirical tuning methods become ineffective while analytical approaches require tedious mathematical works. As a result, the control community shifts its attention to stochastic optimisation techniques that require less interaction from the controller designers. Although these approaches manage to optimise the PID parameters, the combination of multiple objectives in one single objective function is not straightforward. This work presents the development of a multi-objective genetic algorithm to optimise the PID controller parameters for a complex and unstable system. A new genetic algorithm, called the Global Criterion Genetic Algorithm (GCGA) has been proposed in this work and is compared with the state-of-the-art Non-dominated Sorting Genetic Algorithm (NSGA-II) in several standard test problems. The results show the GCGA has convergence property with an average of 35.57% in all problems better than NSGA-II. The proposed algorithm has been applied and implemented on a rotary inverted pendulum, which is a nonlinear and under-actuated plant, suitable for representing a complex and unstable high-order system, to test its effectiveness. The set of pareto solutions for PID parameters generated by the GCGA has good control performances (settling time, overshoot and integrated time absolute errors) with closed-loop stable property.

3 citations

Dissertation
16 May 2011
TL;DR: Particle Swarm Optimization and Bacterial Foraging Optimization techniques are implemented to tune the parameters of the PID for a fifth order low damping plant and a comparative study of the performance of both the techniques is done.
Abstract: PID controllers have been extensively used for a long time for the purpose of process controls.Efficient methods for tuning of PID controllers is still a challange to designers. This project work is based on the development of PID controller for a low damping plant using Bio inspired evolutionary soft computational techniques.We have implemented Particle Swarm Optimization and Bacterial Foraging Optimization techniques to tune the parameters of the PID for a fifth order low damping plant and have done a comparative study of the performance of both the techniques.

1 citations