Particle Swarm Optimization-Based Closed-Loop Optimal State Feedback Control for CSTR
01 Jan 2018-Vol. 442, pp 469-479
TL;DR: In this paper, swarm intelligence is used to obtain the optimal weights, which provide superior performance than the conventional trial-and-error approach, and the proposed approach performance is assessed by weight selection using PSO, which is compared with manual tuning that satisfies the closed-loop stability criteria.
Abstract: Complete state vector information is necessary for implementing the state feedback control via algebraic Riccati equation (ARE). However, all the states are usually not available for feedback because it is often expensive and impractical to include a sensor for each variable. Hence, to estimate the unmeasured variables, a state estimation technique is formulated to estimate all the states of the process. One of the major problems of closed-loop optimal control design is the choice of weighted matrices, which will result in optimal response. The conventional approach involves trial-and-error method to choose the weighted matrices in the cost function to determine the state feedback gain. Some of the drawbacks of this method are as follows: it is tedious, time-consuming, optimal response is not obtained, and manual selection of weighting matrices is also not straightforward. To overcome the above shortcomings, swarm intelligence is used to obtain the optimal weights, which provide superior performance than the conventional trial-and-error approach. The proposed approach performance is assessed by weight selection using PSO, which is compared with manual tuning that satisfies the closed-loop stability criteria. Further, the proposed controller performance is evaluated not only for stabilizing the disturbance rejection, but also for tracking the given reference temperature in a continuous stirred tank reactor (CSTR).
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Citations
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TL;DR: An enhanced Particle Swarm Optimization (PSO) based MPPT method for the photovoltaic system integrated through Z-Source inverter, which has the capability to track the maximum power point (MPP) during an extreme environmental condition.
Abstract: Maximum Power Point Tracking (MPPT) technique is used to extract maximum power from the photovoltaic system. This paper involves working on an enhanced Particle Swarm Optimization (PSO) based MPPT method for the photovoltaic (PV) system integrated through Z-Source inverter. The main benefit of the proposed method is the diminishing of the steady-state oscillation when the maximum power point (MPP) is located. Additionally, during an extreme environmental condition, such as partial shading and large fluctuations of irradiance and temperature, the proposed method has the capability to track the MPP. This algorithm is implemented in dspace 1104 controller. MATLAB simulations are carried out under varying irradiance and temperature conditions to evaluate its effectiveness. Its performance is compared with a conventional method like Perturb and observe (P&O) method.
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...Movement of particles in the optimization process is shown in Figure 7 [8,20]....
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References
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TL;DR: The proposed PSO method was indeed more efficient and robust in improving the step response of an AVR system and had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.
Abstract: In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.
1,367 citations
TL;DR: This book discusses Classical and Modern Control Optimization Optimal Control Historical Tour, Variational Calculus for Discrete-Time Systems, and more.
Abstract: INTRODUCTION Classical and Modern Control Optimization Optimal Control Historical Tour About This Book Chapter Overview Problems CALCULUS OF VARIATIONS AND OPTIMAL CONTROL Basic Concepts Optimum of a Function and a Functional The Basic Variational Problem The Second Variation Extrema of Functions with Conditions Extrema of Functionals with Conditions Variational Approach to Optimal Systems Summary of Variational Approach Problems LINEAR QUADRATIC OPTIMAL CONTROL SYSTEMS I Problem Formulation Finite-Time Linear Quadratic Regulator Analytical Solution to the Matrix Differential Riccati Equation Infinite-Time LQR System I Infinite-Time LQR System II Problems LINEAR QUADRATIC OPTIMAL CONTROL SYSTEMS II Linear Quadratic Tracking System: Finite-Time Case LQT System: Infinite-Time Case Fixed-End-Point Regulator System Frequency-Domain Interpretation Problems DISCRETE-TIME OPTIMAL CONTROL SYSTEMS Variational Calculus for Discrete-Time Systems Discrete-Time Optimal Control Systems Discrete-Time Linear State Regulator Systems Steady-State Regulator System Discrete-Time Linear Quadratic Tracking System Frequency-Domain Interpretation Problems PONTRYAGIN MINIMUM PRINCIPLE Constrained Systems Pontryagin Minimum Principle Dynamic Programming The Hamilton-Jacobi-Bellman Equation LQR System using H-J-B Equation CONSTRAINED OPTIMAL CONTROL SYSTEMS Constrained Optimal Control TOC of a Double Integral System Fuel-Optimal Control Systems Minimum Fuel System: LTI System Energy-Optimal Control Systems Optimal Control Systems with State Constraints Problems APPENDICES Vectors and Matrices State Space Analysis MATLAB Files REFERENCES INDEX
1,256 citations
TL;DR: In this paper, the important role of evolutionary algorithms in multi-objective optimisation is highlighted, and evolutionary advances in adaptive control and multidisciplinary design are predicted, as well as significant applications in parameter and structure optimisation for controller design and model identification, in addition to fault diagnosis, reliable systems, robustness analysis, and robot control.
Abstract: Challenging optimisation problems, which elude acceptable solution via conventional methods, arise regularly in control systems engineering. Evolutionary algorithms (EAs) permit flexible representation of decision variables and performance evaluation and are robust to difficult search environments, leading to their widespread uptake in the control community. Significant applications are discussed in parameter and structure optimisation for controller design and model identification, in addition to fault diagnosis, reliable systems, robustness analysis, and robot control. Hybrid neural and fuzzy control schemes are also described. The important role of EAs in multiobjective optimisation is highlighted. Evolutionary advances in adaptive control and multidisciplinary design are predicted.
585 citations
TL;DR: In this article, the authors investigated the use of GAs for optimization in nonlinear model-based predictive control, where advanced genetic operators and other new features were introduced to increase the efficiency of the genetic search in order to deal with real-time constraints.
Abstract: Genetic algorithms (GAs) are optimization methods inspired by natural biological evolution GAs have been successfully applied to a variety of complex optimization problems where other techniques have often failed The aim of this paper is to investigate the use of GAs for optimization in nonlinear model-based predictive control Advanced genetic operators and other new features are introduced to increase the efficiency of the genetic search In order to deal with real-time constraints, termination conditions are proposed to abort the evolution, once a defined level of optimality is reached Simulated pressure dynamics of a batch fermenter are considered as an example of a highly nonlinear system Simulation results with GAs are compared with the branch-and-bound method, in terms of the control accuracy and computational costs achieved
114 citations
05 Mar 2007
TL;DR: A new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA), based upon using complex-valued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller.
Abstract: Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA). The design method is based upon using complex-valued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller. Specific cross-over and mutation operators were implemented in simple but efficient ways. The performance is tested on a mixed multi-objective H2/H∞ control problem.
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