Closed Loop Fuzzy Logic Control Research Articles

Optimal adaptive intuitionistic fuzzy logic control of anti-cancer drug delivery systems

This paper introduces a new closed loop fuzzy logic controller for regulating intravenous anti-cancer drug delivery, based on intuitionistic fuzzy sets and invasive weed optimization (IWO) algorithm. The developed intuitionistic fuzzy logic controller (IFLC) contributes the following advancements: First, the parameters of IFLC are adaptive and optimally tuned to achieve desired drug concentrations at the tumor site, killing almost all cancer cells at the end of treatment time. Second, drug delivery constraints; namely allowed levels of the drug dose and cumulative toxicity are naturally implied in the design of the optimized IFLC to ensure the clinical safety of cancer patients. Finally, the developed drug delivery control system is robust to handle all possible physiological conditions during treatment such as patient sensitivity in response to toxicity. To test and validate the performance of developed IFLC, extensive simulation results and comparative evaluation have been done on a mathematical patient model. It is proved that our optimal adaptive IFLC is superior to other methods in previous related studies, resulting in the best performance index and the minimum number of remaining cancer cells of 27.63 and 0.806, respectively.

Locomotion control of a biomimetic robotic fish based on closed loop sensory feedback CPG model

ABSTRACT This paper presents mechatronic design and hierarchical locomotion control of a biomimetic robotic fish for three-dimensional swimming modes. Inspired by biological features of Lamprey, a closed loop sensory feedback Central Pattern Generator (CPG) model is adapted to hierarchical control mechanism in order to provide robust and effective biomimetic control structure. A sensory feedback mechanism plays an important role to react external stimuli from environment. In addition, a closed loop fuzzy logic control structure is developed as a brain model to decide adaptive swimming modes according to sensory information. In order to provide three-dimensional motion abilities, the Centre of Gravity (CoG) control mechanism is designed and controlled by a back-forth proportional control structure. Experimental results are obtained to prove the CPG-based closed loop sensory feedback control structure with the developed robotic fish prototype.

Experimental Characterization and Control of a Force Actuator Based on Shape Memory Alloy Wire

This paper presents an experimental design of a force and position controller for a Ni–Ti shape memory alloy actuator. The focus of this work is a measurement device for the characterization of a SMA spring together with the fuzzy controller for position and force that can be quickly and easily implemented. This device makes it possible to develop a mechanism for robotic applications with slow actuation speed, e.g., to mimic a robotic finger. The contribution of our work to the scientific literature is an easy control of force and position, as well as weight reduction when compared to a similar actuator controlled by a traditional electric motor. Ni–Ti wire was winded up to form a coil; an electric current passing through the actuator changes its temperature, which leads to a phase transformation resulting in a length change of the wire. Thus, the actuator behaves as a spring, whose force exerted can be adjusted by an electric current. A closed-loop fuzzy logic controller was programmed in the LabVIEW environment, allowing the control of the force exerted by the actuator, as well as the position of an attached load. The actuator performance was evaluated experimentally before and after a training session.

Closed Loop Fuzzy Logic Control of an Open Ended Induction Machine using a Dual Inverter System with a Floating Capacitor Bridge

Closed Loop Fuzzy Logic Control of an Open Ended Induction Machine using a Dual Inverter System with a Floating Capacitor Bridge

Self-Tuning Closed-Loop Fuzzy Logic Control Algorithm for Adaptive Prestressed Structures

Constantly increasing traffic loads, unpredictable influences on and unplanned conditions of bridges represent new challenges in the field of engineering. Innovative strategies and construction techniques are required to meet these and future challenges. Adaptive prestressing offers great potential to increase a structure’s load-carrying capacity and to optimize its load-induced stresses. In this manner, cost efficiency and sustainability of constructions can be enhanced. Therefore, a control algorithm with high reliability and redundancy is required to meet the new challenges. In this paper, an adaptive fuzzy logic-based closed-loop control system for realization of adaptive prestressed structures is presented. Based on the new concept, two different specialized control algorithms are developed. In order to decrease deflections, the prestressing force of an aluminium truss is controlled. The algorithm includes a fuzzy system with model-based knowledge base. The learning process is established on direct synchronization of the calculated deflections of the model with the measured reactions of the structure. Another fuzzy system for adaptive prestressing of a reinforced concrete T-beam uses a model-free knowledge base. Optimization of load-induced stresses is accomplished utilizing expert knowledge. An incremental learning process is performed in accordance with the algorithm’s basic rules. Experiments on the two prototypes are conducted to investigate the functionality of the developed algorithms. In addition to efficiency, high reliability and redundancy are shown by the control system. In this article, the potential of fuzzy-controlled adaptive prestressing is illustrated.

Power Quality Improvement from Grid Connected Renewable Energy Sources At Distribution Level Using Fuzzy Logic Controller

Harmonic pollution of the power supply system has risen significantly in recent years due primarily to an increase of non-linear loads connected to the utility through residential, commercial and industrial customers. Power supply voltages become distorted due to the high level of undesired harmonic current drawn from the utility. Additionally, drops in fundamental voltage are caused by the interaction between the reactive current and network impedances. Renewable energy resources (RES) are being increasingly connected in distribution systems utilizing power electronic converters. This paper presents a novel control strategy for achieving maximum benefits from these grid-interfacing inverters using the closed loop fuzzy logic control, when installed in 3-phase 4-wire distribution systems. The inverter is controlled to perform as a multi-function device by incorporating active power filter functionality. The inverter can thus be utilized as: power converter to inject power generated from RES to the grid, and 1) shunt APF to compensate current unbalance, load current harmonics, load reactive power demand and load neutral current. All of these functions may be accomplished either individually or simultaneously. This new control concept is demonstrated with extensive MATLAB/Simulink .Finally the proposed scheme is implemented using conventional DC link controller and intelligence controller. Index Terms: Point of Common Coupling (PCC), power quality, Photo Voltaic (PV) System, Fuzzy Logic Controller (FLC).

A fuzzy controller design for nuclear research reactors using the particle swarm optimization algorithm

In this paper, a closed-loop fuzzy logic controller based on the particle swarm optimization algorithm is proposed for controlling the power level of nuclear research reactors. The principle of the fuzzy logic controller is based on the rules constructed from numerical experiments made by means of a computer code for the core dynamics calculation and from human operator's experience and knowledge. In addition to these intuitive and experimental design efforts, consequent parts of the fuzzy rules are optimally (or near optimally) determined using the particle swarm optimization algorithm. The contribution of the proposed algorithm to a reactor control system is investigated in details. The performance of the controller is also tested with numerical simulations in numerous operating conditions from various initial power levels to desired power levels, as well as under disturbance. It is shown that the proposed control system performs satisfactorily under almost all operating conditions, even in the case of very small initial power levels.

Computer simulation of FES standing up in paraplegia: a self-adaptive fuzzy controller with reinforcement learning.

Using computer simulation, the theoretical feasibility of functional electrical stimulation (FES) assisted standing up is demonstrated using a closed-loop self-adaptive fuzzy logic controller based on reinforcement machine learning (FLC-RL). The control goal was to minimize upper limb forces and the terminal velocity of the knee joint. The reinforcement learning (RL) technique was extended to multicontroller problems in continuous state and action spaces. The validated algorithms were used to synthesize FES controllers for the knee and hip joints in simulated paraplegic standing up. The FLC-RL controller was able to achieve the maneuver with only 22% of the upper limb force required to stand-up without FES and to simultaneously reduce the terminal velocity of the knee joint close to zero. The FLC-RL controller demonstrated, as expected, the closed loop fuzzy logic control and on-line self-adaptation capability of the RL was able to accommodate for simulated disturbances due to voluntary arm forces, FES induced muscle fatigue and anthropometric differences between individuals. A method of incorporating a priori heuristic rule based knowledge is described that could reduce the number of the learning trials required to establish a usable control strategy. We also discuss how such heuristics may also be incorporated into the initial FLC-RL controller to ensure safe operation from the onset.

Pneumatic Positioner with Fuzzy Control

Abstract A pneumatic positioner with closed loop fuzzy logic control is described. The system consists of a pneumatic cylinder, two proportional valves, a position and speed sensor, a fuzzy controller and an A/D-D/A conversion board. No braking devices for maintaining position are provided on the cylinder rod. The control considers position error and actuator speed as antecedents, and valve control voltages as consequents. Membership functions, the set of rules and the weights of each were obtained by considering the component characteristics and the relationships between the antecedents and the flow rates to the cylinder chambers with varying control voltages. The latter were calculated using the min-max-center of gravity criteria. Experimental tests were carried out on the positioning system with different reference signal shapes and with different applied loads. Results show good positioning accuracy and repeatability, absence of overshoot and system stability under varying operating conditions.