Dayaram Sonawane

College of Engineering, Pune | coep · Department of Instrumentation and Control Engineering

In this paper, the Nonlinear Model Predictive Control (NMPC) technique is proposed for the control of BrushLess Direct Current (BLDC) motors to address the problem of over-excitation, specifically in Electric Vehicle (EV) applications. This over-excitation increases the overall energy consumption of the machine and eventually reduces the vehicle’s...

Brushless DC (BLDC) motor is the first choice for lightweight electric vehicles because of its high torque, high power density, and compatible speed range. The vehicle environment is very dynamic, nonlinear, and noisy. It is challenging to design a BLDC motor control for high-performance operation. Therefore this paper presents Nonlinear Model Pred...

This paper presents a framework for the implementation of Nonlinear Model Predictive Control (NMPC) on Field Programmable Gate Array (FPGA). We show the step-by-step procedure of FPGA implementation of NMPC considering the case study of the 2D-crane system. In the implementation, we use GRAMPC Toolbox to construct NMPC and subsequently generate an...

Hybrid systems can be controlled by a Hybrid Model Predictive Control (HMPC) with an accurate prediction model and proper formulation. In the real-time evolution of HMPC, an online optimization (mostly, Mixed Integer Quadratic Programming (MIQP)) problem needs to be solved at each sample time. Online implementation of MIQP solver on resource-limite...

In this paper, we present the offset-free Nonlinear Model Predictive Controller (NMPC) for the control of drum-boiler pilot-plant at College of Engineering Pune (COEP), India. We consider a laboratory-scale pilot plant available in the process control lab designed to generate steam up to 0.0083 kg/s at the pressure 0.4 MPa. To design an NMPC, we de...

The maximum distance that can be traveled at a stretch is the major limitation of today’s electric vehicle (EV). This is due to the need for maximum current, torque, and, the total onboard energy storage etc. The distance can be increased by efficiently using the available power resources. In this paper, we present a nonlinear model predictive cont...

For explicit model predictive control (EMPC), off-line pre-computed optimal feedback laws need to be stored in a look-up table for on-line evaluation. The need for memory to store the look-up table on embedded hardware limits applicability of EMPC to systems with few states, a small number of constraints, and short prediction horizons. In this pape...

Model predictive control (MPC) has emerged as an excellent control strategy owing to its ability to include constraints in the control optimization and robustness to linear as well as highly non-linear systems. There are many challenges in real-time implementation of MPC on embedded devices, including computational complexity, numerical instability...

Today, various nonlinear programming problem (NLP) solvers and C/C ++ code generation frameworks are available as open source for solving nonlinear model predictive control (NMPC). Almost all the solvers are written in C/C ++ code which are more compatible for the PC-based simulation environment. These codes are not directly compatible for embedded...

It is very well-known that the implementation of Model Predictive Controller (MPC) on embedded platforms is challenging due to computational complexities associated while solving an optimization problem. Although, there are many efficient embedded implementations existing by now, but for faster, more dynamic and non-linear control applications, no...

It is well-known that the real-time implementation of MPC is cumbersome because of the huge burden of solving QP problem on-line at each sample time. Due to this, traditionally MPC has been mainly restricted to processes with rather slow dynamics, such as the ones encountered in the oil and gas refineries. However, recent algorithmic advances (such...

This paper deals with the education of Instrumentation and Control Engineering students in the field of process control. A laboratory scale drum-boiler pilot plant is utilized to serve the teaching purpose. The emphasis of the paper is to give handson experience in designing and implementing widely used controllers in many process industries, such...

Model predictive control (MPC) is a predictive class of control algorithm which has been widely adopted for slow dynamic, constrained, multi-variable process control problems. The applicability of MPC for real-time control is restricted due to the associated computational complexity of quadratic programming (QP) problem and the choice of appropriat...

The polymer electrolyte membrane fuel cell (PEMFC) has emerged as a promising fuel cell technology for a varied range of stationary as well as transportation applications. This paper presents a dynamic model of a PEMFC system which can be used for further development of a control system for the same. The proposed model incorporates various dynamic...

This paper deals with the hardware implementation of customized Proportional-Integral-Derivative (PID) architecture using FPGA for the speed control of permanent magnet DC motor. This architecture is embedded in FPGA using Verilog to implement speed control loop. Controller design, synthesis and analysis are completed by Xilinx ISE software and chi...

Safety and driver comfort are the essential goals of new trends in automobile industry. An automatic wiper controller, helps not only in increasing safety by reducing distractions but also increases the overall comfort. Such an automatic control is available however it has limitations of high cost and low efficiency. In this work, we have proposed...

This paper presents the design and real-time hardware implementation of Active Set Method (ASM) based Linear Model Predictive Controller (MPC) for position control of DC Servomotor. A key component of MPC is to solve an online convex Quadratic Programming (QP) optimization problem at each sampling instance within specified sample period. In view of...

Linear model predictive control (MPC) assumes a linear system model, linear inequality constraints and a convex quadratic cost function. Thus, it can be formulated as a quadratic programming (QP) problem. Due to associated computational complexity of QP solving algorithms, its applicability is restricted to relatively slow dynamic systems. This pap...

In this paper we have designed a simple, robust and energy efficient street light control system which requires minimum maintenance. The principle used is sensing time for deciding the intensity of the street light. The astronomical clock depending on geographical area is studied to generate statistical data. The intensity is varied in five steps a...

The proposed system focuses on Remote Monitoring of patients to ensure the availability of quality medical services. The system comprises of Electrocardiogram (ECG) electrodes, SpO2 probes, signal conditioning units, micro controller unit, and a GSM Module. The weak signals obtained from the sensors are amplified, filtered, current to voltage conve...

This paper presents an interior-point method (IPM) based quadratic programming (QP) solver for the solution of optimal control problem in linear model predictive control (MPC). LU factorization is used to solve the system of linear equations efficiently at each iteration of IPM, which renders faster execution of QP solver. The controller requires i...

During intravenous anesthesia, anesthetic drugs must be administered at a suitable rate to prevent over dosing and under dosing in a patient. A developed Pharmacokinetic- Pharmacodynamic (PK/PD) model, which has been used to study the relationship between administered anesthetic dose and its effect on the patient in terms of hypnosis, is considered...

This paper focuses on the design and implementation of model- predictive controller (MPC) for the close loop control of anesthesia for a patient undergoing surgery. A single input (Propofol infusion rate) single output (bispectral index (BIS)) model of patient has been assumed which includes variable dead time caused by measurement of bispectral in...

Ziegler Nichols is oldest and widely accepted PID tuning method. Due to excessive overshoot in Ziegler-Nichol tuned PIDs (ZNPID), their performance is usually not acceptable for applications where small error tolerance band and precise control is required. To overcome this problem, we propose the gain updating method called as Augmented Ziegler-Nic...

The use of infrared radiation for medical diagnosis is a very new concept in the field of medical technology that promises to deliver high-end results. While a very few devices based on infrared technique have been implemented, there still exists a strong need to develop such medical devices. Therefore, our aim is to develop a low-cost subcutaneous...

The paper presents a novel methodology to implement resource efficient 64-bit floating point matrix multiplication algorithm using FPGA. Approach uses systolic architecture using four processing element (PE's) that gives tradeoffs between resource utilization and execution time, results in reducing the routing complexity for dense matrix multiplica...

The paper presents a systolic architecture for integer point matrix multiplication algorithm using FPGA. Approach uses four processing elements that minimizes resources, reduces the routing complexity and improves Area/Speed metric.

Cholesky factorization is the computationally most expensive step in numerically solving positive definite systems. Due to inherently recursive computation process and associated floating point division and square root operations in Cholesky factorization, it is very difficult to obtain acceleration by exploiting parallelism on FPGA's. To solve thi...