D. Subbaram Naidu

Advanced control strategies for heating, ventilation, air-conditioning, and refrigeration systems—An overview: Part I: Hard control

A chronological overview of the advanced control strategies for heating, ventilation, air-conditioning, and refrigeration (HVAC&R) is presented in this article. The overview focuses on hard-computing or control techniques, such as proportional-integral-derivative, optimal, nonlinear, adaptive, and robust; soft-computing or control techniques, such as neural networks, fuzzy logic, genetic algorithms; and on the fusion or hybrid of hard- and soft-control techniques. Thus, it is to be noted that the terminology “hard” and “soft” computing/control has nothing to do with the “hardware” and “software” that is being generally used. Part I of a two-part series focuses on hard-control strategies, and Part II focuses on soft- and fusion-control in addition to some future directions in HVAC&R research. This overview is not intended to be an exhaustive survey on this topic, and any omission of other works is purely unintentional.

Advanced control strategies for wind energy systems: An overview

Wind energy systems have been emerging as a highly significant solution to the problem of limited traditional energy sources. In this paper, control methodologies adapted to wind energy systems are topically reviewed. oHard computing or control techniques such as proportional-integral-derivative (PID), optimal, nonlinear, adaptive and robust and soft computing or control techniques such as neural networks, fuzzy logic, genetic algorithms and on the fusion or hybrid of hard and soft control techniques are primarily focused. This overview concludes with some possible future directions are also suggested. This overview is not intended to be an exhaustive survey on this topic and any omissions of other works is purely unintentional.

Control strategies for smart prosthetic hand technology: An overview

A chronological overview of the applications of control theory to prosthetic hand is presented. The overview focuses on hard computing or control techniques such as multivariable feedback, optimal, nonlinear, adaptive and robust and soft computing or control techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms and on the fusion of hard and soft control techniques. This overview is not intended to be an exhaustive survey on this topic and any omissions of other works is purely unintentional.

Advanced control strategies for HVAC&R systems—An overview: Part II: Soft and fusion control

A chronological overview of the advanced control strategies for HVAC&R is presented. The overview focuses on hard-computing or control techniques, such as proportional-integral-derivative, optimal, nonlinear, adaptive, and robust; soft-computing or control techniques, such as neural networks, fuzzy logic, genetic algorithms; and the fusion or hybrid of hard and soft control techniques. Part I focused on hard-control strategies; Part II focuses on soft and fusion control and some future directions in HVA&R research. This overview is not intended to be an exhaustive survey on this topic, and any omissions of other works is purely unintentional.

A Hybrid Optimal Control Strategy for a Smart Prosthetic Hand

This paper presents a hybrid of a soft computing or control technique of adaptive neuro-fuzzy inference system (AN-FIS) and a hard computing or control technique of the hybrid finite-time linear quadratic optimal control for a two-fingered (thumb and index) prosthetic hand. In particular, the ANFIS is used for inverse kinematics, and the optimal control is used to minimize tracking error utilizing feedback linearized dynamics. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance. Work is underway to extend this methodology to a five-fingered, three-dimensional prosthetic hand.Copyright

Frequency domain surface EMG sensor fusion for estimating finger forces

Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

Hybrid control strategies for a five-finger robotic hand

Abstract This paper presents a hybrid controller of soft control techniques, adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL), and hard control technique, proportional-derivative (PD), for a five-finger robotic hand with 14-degrees-of-freedom (DoF). The ANFIS is used for inverse kinematics of three-link fingers and FL is used for tuning the PD parameters with 2 input layers (error and error rate) using 7 triangular membership functions and 49 fuzzy logic rules. Simulation results with the hybrid of FL-tuned PD controller exhibit superior performance compared to PD, PID and FL controllers alone.

Time scale analysis and control of Wind Energy Conversion Systems

This paper presents a control method to design low-order optimal controllers for a high-order Wind Energy Conversion Systems (WECS) with Permanent Magnet Synchronous Generators (PMSG). Based on the nature of the WECS which consists of different time-scale (slow and fast) dynamics, the WECS is decoupled into slow and fast subsystems using time-scale analysis. Separate low-order optimal controllers are then designed for the slow and fast subsystems based on the Linear Quadratic Regulator (LQR) theory. The reduced-order optimal control of separate subsystems is compared with the high-order optimal control of the original system to show the superiority of the proposed method in terms of separation of dynamics and reduced computational effort.

A hybrid adaptive control strategy for a smart prosthetic hand

This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance. Work is in progress to extend this methodology to a five-fingered, three-dimensional prosthetic hand.

A study of Particle Swarm Optimization on leukocyte adhesion molecules and control strategies for smart prosthetic hand

Hard computing based optimization algorithms usually require a lot of computational resources and generally do not have the ability to arrive at the global optimum solution. Soft computing algorithms on the other hand negate these deficiencies, by allowing for reduced computational loads and the ability to find global optimal solutions, even for complex cost surfaces. This paper presents two numerical case studies where a particle swarm optimization (PSO) algorithm is applied to biomedical problems. In particular, the problem of identifying the rupture force for leukocyte adhesion molecules and the problem of finding the correct control parameters of a robotic hand, are addressed. Simulation results indicate that PSO is a feasible alternative to the computational expensive hard computing algorithms.