Tuning of PID Controller Using Particle Swarm Optimization (PSO)

The aim of this research is to design a PID Controller using PSO algorithm. The model of a DC motor is used as a plant in this paper. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as genetic algorithm (GA) and particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. However, due to the computational efficiency, only PSO will be used in this paper. The comparison between PSO-based PID (PSO-PID) performance and the ZN-PID is presented. The results show the advantage of the PID tuning using PSO-based optimization approach

On the Computational Analysis of the Genetic Algorithm for Attitude Control of a Carrier System

This chapter intends to cover three main topics. First, a fuzzy-PID controller is designed to control the propulsion vector of a launch vehicle, accommodating a CanSaT. Then, the genetic algorithm (GA) is employed to optimize the controller’s performance. Finally, through adjusting the algorithm parameters, their effect on the optimization process is examined. In this regard, the motion vector control is programmed based on the governing system’s dynamic equations of motion for payload delivery in the desired altitude and flight-path angle. This utilizes one single input and one preference fuzzy inference engine, where the latter acts to avoid the system instability in high angles for the propulsion vector. The optimization objective functions include the deviations of the thrust vector and the system from the stability path, which must be met simultaneously. Parameter sensitivity analysis of the genetic algorithm involves examining nine different cases and discussing their effect on the optimization results

A genetic based neuro-fuzzy controller for thermal processes

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.Facultad de Informátic

Optimum Setting of PID Controller using Particle Swarm Optimization for a Position Control System

The goal of this paper is to present a study of tuning the Proportional–Integral-Derivative (PID) controller for control the position of a DC motor by using the Particle Swarm Optimization (PSO) technique as well as the Ziegler & Nichols (ZN) technique. The conventional Ziegler & Nichols (ZN) method for tuning the PID controller gives a big overshoot and large settling time, so for this reason a modern control approach such as particle swarm optimization (PSO) is used to overcome this disadvantage. In this work, a third order system is considered to be the model of a DC motor. Four types of performance indices are used when using the particle swarm optimization technique. These indices are ISE, IAE, ITAE and ITSE. Also study the effect of each one of these performance indices by obtaining the percentage overshoot and settling time when a unit step input is applied to a DC motor. A comparison is made between the two methods for tuning the parameters of PID controller for control the position of a DC motor is considered. The first one is tuning the controller by using the Particle Swarm Optimization technique where the second is tuning by using the Ziegler & Nichols method. The proposed PID parameters adjustment by the Particle Swarm Optimization technique showed better results than the Ziegler & Nichols’ method. The obtained simulation results showed good validity of the proposed method. MATLAB programming and Simulink were adopted in this work

Genetic algorithm optimization and control system design of flexible structures

This paper presents an investigation into the deployment of genetic algorithm (GA)-based controller design and optimization for vibration suppression in flexible structures. The potential of GA is explored in three case studies. In the first case study, the potential of GA is demonstrated in the development and optimization of a hybrid learning control scheme for vibration control of flexible manipulators. In the second case study, an active control mechanism for vibration suppression of flexible beam structures using GA optimization technique is proposed. The third case study presents the development of an effective adaptive command shaping control scheme for vibration control of a twin rotor system, where GA is employed to optimize the amplitudes and time locations of the impulses in the proposed control algorithm. The effectiveness of the proposed control schemes is verified in both an experimental and a simulation environment, and their performances are assessed in both the time and frequency domains

A genetic based neuro-fuzzy controller for thermal processes

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.Facultad de Informátic

Improvements in Genetic Approach to Pole Placement in Linear State Space Systems Through Island Approach PGA with Orthogonal Mutation Vectors

This thesis describes a genetic approach for shaping the dynamic responses of linear state space systems through pole placement. This paper makes further comparisons between this approach and an island approach parallel genetic algorithm (PGA) which incorporates orthogonal mutation vectors to increase sub-population specialization and decrease convergence time. Both approaches generate a gain vector K. The vector K is used in state feedback for altering the poles of the system so as to meet step response requirements such as settling time and percent overshoot. To obtain the gain vector K by the proposed genetic approaches, a pair of ideal, desired poles is calculate first. Those poles serve as the basis by which an initial population is created. In the island approach, those poles serve as a basis for n populations, where n is the dimension of the necessary K vector. Each member of the population is tested for its fitness (the degree to which it matches the criteria). A new population is created each “generation” from the results of the previous iteration, until the criteria are met, or a certain number of generations have passed. Several case studies are provided in this paper to illustrate that this new approach is working, and also to compare performance of the two approaches

Global fuzzy control based on cell mapping

Cell mapping methods can generate global optimal controls for nonlinear dynamic systems. However, the implementation of this approach for on-line control is difficult due to its use of a table-based controller which requires a large amount of memory. In this dissertation, a cell mapping based systematic method is developed to construct a fuzzy controller to replace the table-based controller for global optimal control of dynamic systems. The method consists of four steps: 1) An optimal control table is generated using a cell mapping algorithm. 2) The cells are linked into trajectories to characterize the optimal dynamic evolution of various cell. 3) The trajectories are classified into groups of similar trajectories so as to represent global optimal controls for the entire cell space with a number of representative trajectory groups. 4) Based on the input-output relations of these trajectories groups, which represent the states and controls of the corresponding cells, a set of fuzzy rules are generated for a fuzzy controller. With the method of grouping trajectories developed in this dissertation, controls for the entire space can be expressed corresponding to trajectory groups instead of cells with a reduced number of trajectory groups. This reduces the complexity of constructing the global fuzzy controller compared with developing rules based on the control data of all the cells. The developed method is applied to the ship navigation and car parking problems to show the applicability of the method to two and three dimensional problems

On Stabilization of Cart-Inverted Pendulum System: An Experimental Study

The Cart-Inverted Pendulum System (CIPS) is a classical benchmark control problem. Its dynamics resembles with that of many real world systems of interest like missile launchers, pendubots, human walking and segways and many more. The control of this system is challenging as it is highly unstable, highly non-linear, non-minimum phase system and underactuated. Further, the physical constraints on the track position control voltage etc. also pose complexity in its control design. The thesis begins with the description of the CIPS together with hardware setup used for research, its dynamics in state space and transfer function models. In the past, a lot of research work has been directed to develop control strategies for CIPS. But, very little work has been done to validate the developed design through experiments. Also robustness margins of the developed methods have not been analysed. Thus, there lies an ample opportunity to develop controllers and study the cart-inverted pendulum controlled system in real-time. The objective of this present work is to stabilize the unstable CIPS within the different physical constraints such as in track length and control voltage. Also, simultaneously ensure good robustness. A systematic iterative method for the state feedback design by choosing weighting matrices key to the Linear Quadratic Regulator (LQR) design is presented. But, this yields oscillations in cart position. The Two-Loop-PID controller yields good robustness, and superior cart responses. A sub-optimal LQR based state feedback subjected to H∞ constraints through Linear Matrix Inequalities (LMIs) is solved and it is observed from the obtained results that a good stabilization result is achieved. Non-linear cart friction is identified using an exponential cart friction and is modeled as a plant matrix uncertainty. It has been observed that modeling the cart friction as above has led to improved cart response. Subsequently an integral sliding mode controller has been designed for the CIPS. From the obtained simulation and experiments it is seen that the ISM yields good robustness towards the output channel gain perturbations. The efficacies of the developed techniques are tested both in simulation and experimentation. It has been also observed that the Two-Loop PID Controller yields overall satisfactory response in terms of superior cart position and robustness. In the event of sensor fault the ISM yields best performance out of all the techniques