Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

This paper examines the performance of Model Predictive Control (MPC) scheme for an Active suspension. A vehicle suspension is designed to provide superior ride comfort and road handling characteristics. Unlike passive suspensions, the Active suspension can change the dynamic of suspension in real-time by injecting force into the system. MPC allows the active suspension to provide better and consistent passenger comfort and road handling capabilities for different road profile. Even though long back, the idea of active suspension conceived, the prohibitive cost and complexity restricted its usage. In recent years active suspension is receiving more and more attention with users preferring a high-end car. In an active suspension for the real-time adjustment of the control force, need a design of a controller. In literature, many controllers used such as Proportional Integral Derivative (PID), Linear Quadratic regulators (LQR), Fuzzy logic controller, Artificial Neural Networks (ANN). In this paper, revealed a model predictive control arrangement for Active suspension model. MPC is an optimal control scheme which uses a model of plant for predicting the future output. The control inputs are optimized such that these predicted outputs meet the desired level of performance. Tested the MPC control scheme is using a benchscale replica of Quarter active suspension model from QUANSER. To better appreciate the capabilities of the MPC Control Scheme, compared the performance of the active suspension with that of an LQR control scheme, and passive suspension.

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

International Journal of Engineering and Advanced Technology Regular Issue

Active Suspension with Model Predictive Control

This paper deals with the development of a Simulink model for the control, protection and monitoring system of a diesel engine. Various sensors and switches involved in the engine protection and monitoring systems such as throttle position sensor (TPS), lube oil pressure sensor (OPS), coolant temperature sensor (CTS) and idle validation switch (IVS) are discussed in detail. Then, a comparison study of the speed control of the diesel engine was done using P, PI and PID controllers. Finally, a mathematical model of the diesel engine is derived using the System Identification Technique. The results obtained by formulating an accurate mathematical model and a precise controller allow the operator to establish the required engine protection parameters such as the coolant temperature, oil pressure and the speed of the engine.

World University root node

2019 Innovations in Power and Advanced Computing Technologies (i-PACT)

Diesel Engine Control and Protection Monitoring using PID Controller

This paper presents the reference tracking and regulatory performance of servo motor using an internal model control technique. The linear model of the servo motor is obtained using frequency response analysis and the position control is implemented using an IMC principle. The IMC controller is designed based on the Q parameterization and the trade off between servo and disturbance rejection characteristics is experimentally validated using Hardware in loop (HIL) testing. Moreover, the performance of IMC is compared with that of the auto tuned PID controller in MATLAB. Experimental results substantiate that the tracking and regulatory performance of IMC is better than that of the PID controller. © 2019 IEEE.

VIT Unitversity

Internal Model Control for Regulatory and Reference Tracking of Servo Motor

Neural Networks (NN) have been the forefront of growth in recent years due to their variety, the opportunities they provide and most importantly their dynamic nature. A control system for catering robots for path planning is proposed with the help of neural networks as a comparative study. Various parameters such as training time, performance of the network, forecasted distance are considered after iterating to obtain the optimal dataset using Probabilistic Roadmap (PRM) algorithm. Approximately 36% improvement in forecasted distance was obtained using neural networks when compared to the traditional PRM algorithm. © 2018 The Authors. Published by Elsevier Ltd.

Fulltext

Procedia Computer Science

Comparative study of neural networks in path planning for catering robots

This paper deals with the attitude tracking control problem for a 2 DoF laboratory helicopter using optimal linear quadratic regulator (LQR). As the performance of the LQR controller greatly depends on the weighting matrices (Q and R), it is important to select them optimally. However, normally the weighting matrices are selected based on trial and error approach, which not only makes the controller design tedious but also time consuming. Hence, to address the weighting matrices selection problem of LQR, in this paper we propose an adaptive particle swarm optimization (APSO) method to obtain the elements of Q and R matrices. Moreover, to enhance the convergence speed and precision of the conventional PSO, an adaptive inertia weight factor (AIWF) is introduced in the velocity update equation of PSO. One of the key features of the AIWF is that unlike the standard PSO in which the inertia weight is kept constant throughout the optimization process, the weights are varied adaptively according to the success rate of the particles towards the optimum value. The proposed APSO based LQR control strategy is applied for pitch and yaw axes control of 2 Degrees of Freedom (DoF) laboratory helicopter workstation, which is a highly nonlinear and unstable system. Experimental results substantiate that the weights optimized using APSO, compared to PSO, result in not only reduced tracking error but also improved tracking response with reduced oscillations. © 2015 Elsevier B.V. All rights reserved.

Applied Soft Computing

Adaptive PSO for optimal LQR tracking control of 2 DoF laboratory helicopter

This paper employs an adaptive particle swarm optimization (APSO) algorithm to solve the weighting matrices selection problem of linear quadratic regulator (LQR). One of the important challenges in the design of LQR for real time applications is the optimal choice state and input weighting matrices (Q and R), which play a vital role in determining the performance and optimality of the controller. Commonly, trial and error approach is employed for selecting the weighting matrices, which not only burdens the design but also results in non-optimal response. Hence, to choose the elements of Q and R matrices optimally, an APSO algorithm is formulated and applied for tracking control of inverted pendulum. One of the notable changes introduced in the APSO over conventional PSO is that an adaptive inertia weight parameter (AIWP) is incorporated in the velocity update equation of PSO to increase the convergence rate of PSO. The efficacy of the APSO tuned LQR is compared with that of the PSO tuned LQR. Statistical measures computed for the optimization algorithms to assess the consistency and accuracy prove that the precision and repeatability of APSO is better than those of the conventional PSO. © Springer India 2016.

Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics Smart Innovation, Systems and Technologies

APSO Based Weighting Matrices Selection of LQR Applied to Tracking Control of SIMO System

The design of robust controller for nonlinear uncertain system has three important challenges, namely, nonlinearity, parameter uncertainty and external disturbance. Hence implementing a control algorithm for nonlinear unstable system is always a challenging task. In this paper, we propose a nonlinear controller using State Dependent Riccati Equation (SDRE) technique for a ball and beam system, which is inherently nonlinear and unstable system. Using the first principles, the equation of motion of the ball and beam system is obtained, and the nonlinear dynamics of the system is transformed into State Dependent Coefficient (SDC) matrices using Extended Linearization Technique. One of the key advantages of expressing the dynamics of system in SDC is that it fully captures the nonlinear characteristics of the system. Moreover, in SDRE control, since the weighing matrices are also expressed as a function of state variables, it provides better tracking response than that of its linear counterpart, LQR. Simulations are carried out to assess the trajectory tracking response, disturbance rejection property of controller and robustness of the system against parameter uncertainty. Simulation results prove that the SDRE based control cannot only provide improved transient response but also make the system more robust against disturbance and parameter uncertainty. © 2014 Published by Elsevier Ltd.

Procedia Engineering

State dependent riccati equation based nonlinear controller design for ball and beam system

This paper deals with the sensor fault diagnosis in a DC servo system using Unknown Input Observer (UIO) by residual analysis approach. The basic principle of UIO is to decouple disturbances from the state estimation error. A single full order Unknown input observer is designed to detect sensor faults in the presence of unknown inputs (disturbances). The basic idea behind the use of observers for fault detection is to form residuals from the difference between the actual system outputs and the estimated outputs using an observer. Once a fault occurs, the residuals are expected to react by becoming greater than a prespecified threshold. When the system under consideration is subject to unknown disturbances or unknown inputs, their effect has to be decoupled from the residuals to avoid false alarms. By conducting proper residual analysis, a fault in the system can be identified. Simulations are carried out to verify the effectiveness of the proposed framework for sensor fault detection and estimation.

Journal of Electrical Engineering

Sensor fault detection in DC servo system using Unknown Input Observer with structured residual generation

In this paper, we consider the stabilization and trajectory tracking of magnetic levitation system using PID controller whose controller gains are determined via Linear Quadratic Regulator (LQR) approach. Firstly, the nonlinear mathematical model of the system is obtained from the first principle. Then by applying Taylor's series, the non linear equation of motion is linearized around the equilibrium point to implement the stabilizing controller. Finally, the gains of the PID controller to achieve the desired response are determined using the LQR theory. Based on the natural frequency and damping ratio of the closed loop system, a new criterion for selecting the weighting matrices of LQR is proposed in this paper. Experiments are conducted on a Quanser magnetic levitation system to evaluate the performance of the proposed methodology and the experimental results prove that the proposed control strategy is effective not only in stabilizing the ball but also in rejecting the disturbance present in the system. © 2013 The Authors. Published by Elsevier Ltd.

LQR based optimal tuning of PID controller for trajectory tracking of magnetic levitation system

This paper describes the method for stabilizing and trajectory tracking of Self Erecting Single Inverted Pendulum (SESIP) using Linear Quadratic Regulator (LQR). A robust LQR is proposed in this paper not only to stabilize the pendulum in upright position but also to make the cart system to track the given reference signal even in the presence of disturbance. The control scheme of pendulum system consists of two controllers such as swing up controller and stabilizing controller. The main focus of this work is on the design of stabilizing controller which can accommodate the disturbance present in the system in the form of wind force. An optimal LQR controller with well tuned weighting matrices is implemented to stabilize the pendulum in the vertical position. The steady state and dynamic characteristics of the proposed controller are investigated by conducting experiments on benchmark linear inverted pendulum system. Experimental results prove that the proposed LQR controller can guarantee the inverted pendulum a faster and smoother stabilizing process with less oscillation and better robustness than a Full State Feedback (FSF) controller by pole placement approach. © 2013 The Authors. Published by Elsevier Ltd.