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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Maximum Power Point Tracking Implementation by Dspace Controller Integrated Through Z-Source Inverter Using Particle Swarm Optimization Technique for Photovoltaic Applications

As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

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System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

This work presents a Linear Quadratic Gaussian (LQG) controller in cascade loop applied to a Two‐Switch Forward Converter (2SFC). This arrange is suitable for low power battery chargers an...

LQG controller in cascade loop tuned by PSO applied to a DC–DC converter

Concerning actuator gain faults and strong system nonlinearity in batch processes, a design method of a kind of 2D fuzzy constrained model fault-tolerant predictive controller is proposed. Firstly, introduce two errors: state error and output error, after that the original system model is converted into a 2D Roesser fault model. Meanwhile, the design of iterative learning fault tolerant control under constraints has been transformed into the determination of the constrained update law. Subsequently, real-time on-line design of the fuzzy fault-tolerant update law that ensures the closed-loop system robustly asymptotic stability is presented by taking the appearance of linear matrix inequalities (LMIs) with constraint subject to the designed infinite optimization performance index and Lyapunov stability theory. Finally, taking a three-tank case as an example to compare with the 1D of the method proposed in this paper, the comparison results illustrated the 2D method of this paper has better control effects.

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2D Fuzzy Constrained Fault-Tolerant Predictive Control of Nonlinear Batch Processes

The continuous stirred-tank reactors have complex dynamic behavior. In the reactor, the residual concentration decreases during an exothermic chemical reaction. In this transition proper cooling is essential to stabilize the reaction, and to prevent reactor overheating. This paper presents the data-driven metaheuristic state-dependent parameter proportional-integration-plus (M-SDP-PIP) control as an alternative of gain-scheduled control for adjusting the coolant temperature of the reactor. In fact, the parameters of the discrete transfer function were identified in the non-minimal state space using state-dependent parameter which is a nonlinear method; and weighting matrixes of linear quadratic servomechanism cost function of the optimal controller were achieved by metaheuristic methods, genetic algorithm, and particle swarm optimization, according to the minimization of the integral absolute error index and energy consumption. The proposed controller was compared to gain-scheduled and non-gain-scheduled controllers in the closed-loop condition in terms of tracking performance. The results show particle swarm optimization algorithm converge to the same optimal solution as genetic algorithm using fewer number of function evaluation, so that proposed controller has more effective offset-free servo-regulatory performance than other control schemes. In addition, it is simpler and more comprehensible because it requires no auxiliary equations and models to determine and manage the control gain, as opposed to the gain-scheduled structure. 

Optimal metaheuristic state-dependent parameter proportional-integral-plus control: Alternative to gain-scheduled controller for control of a nonlinear continuous stirred tank reactor

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).

Particle Swarm Optimization-Based Closed-Loop Optimal State Feedback Control for CSTR

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

Integrating Fuzzy based Fault diagnosis with Constrained Model Predictive Control for Industrial Applications

The 2 Degree of Freedom (DoF) ball balancer system is a highly nonlinear system where the position of the ball is controlled by controlling the two servo motors simultaneously. In general, conventional Proportional Integral Derivative (PID) controller and Proportional Velocity (PV) is used to control the location of the ball on a plate in $\pmb{X}$ and $\pmb{Y}$ axes. But the major concerns are both classic PID and PV fails to track the ball position accurately due to non-linearity, process parameters variations and uncertainties. The above demerits are overcome by implementing Model Reference Adaptive Controller (MRAC) based PID controller. The closed loop reference model is chosen based on desired time domain specifications and by dominant pole placement technique. The gain values of the PID controller are tuned by MRAC to obtain optimal performance. MRAC based PID controller with MIT rule, modified MRAC with MIT approach, and modified MRAC with the Lyapunov rule are implemented in Simulink. The controller's performance on the benchmark Quanser 2 Dof ball balancer system is evaluated in real-time and comparative analysis is made. From the comparison, it is found modified MRAC based Lyapunov approach suppress the overshoot, provides better stability and tracks the system set-point accurately. On the other hand modified MRAC offers less Root Mean Square (RMS) value and faster tracking with less adaptation gain.

Natarajan Sivaraman

Papers

Particle Swarm Optimization-Based Closed-Loop Optimal State Feedback Control for CSTR

Integrating Fuzzy based Fault diagnosis with Constrained Model Predictive Control for Industrial Applications

Visual Servoing Based Model Reference Adaptive Control with Lyapunov Rule for a Ball on Plate Balancing System