A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis. A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A shape memory alloy adaptive tuned vibration absorber: design and implementation

In this paper a tuned vibration absorber (TVA) is realized using shape memory alloy (SMA) elements. The elastic modulus of SMA changes with temperature and this effect is exploited to develop a continuously tunable device.A TVA with beam elements is described, a simple two-degree-of-freedom model developed and the TVA characterized experimentally. The behaviour during continuous heating and cooling is examined and the TVA is seen to be continuously tunable. A change in the tuned frequency of 21.4% is observed between the cold, martensite, and hot, austenite, states. This corresponds to a change in the elastic modulus of about 47.5%, somewhat less than expected.The response time of the SMA TVA is long because of its thermal inertia. However, it is mechanically simple and has a reasonably good performance, despite the tuning parameters depending on the current in a strongly nonlinear way

An ARTMAP-incorporated Multi-Agent System for Building Intelligent Heat Management

This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach

Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategyComment: Submitted to Energies - MDPI.co

Robust control of room temperature and relative humidity using advanced nonlinear inverse dynamics and evolutionary optimisation

A robust controller is developed, using advanced nonlinear inverse dynamics (NID) controller design and genetic algorithm optimisation, for room temperature control. The performance is evaluated through application to a single zone dynamic building model. The proposed controller produces superior performance when compared to the NID controller optimised with a simple optimisation algorithm, and classical PID control commonly used in the buildings industry. An improved level of thermal comfort is achieved, due to fast and accurate tracking of the setpoints, and energy consumption is shown to be reduced, which in turn means carbon emissions are reduced

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators