Temperature control of a building using backstepping technique

The reconstruction of deep in situ environment up to 95 °C and 70 MPa using water is critical for the fidelity testing of deep Earth rocks. The temperature and pressure of the water have strong coupling in such an environment, which makes the control of temperature and pressure very difficult. The paper firstly presents the design of the system of deep in situ environment reconstruction and core transfer (SERCT); secondly, for the problem of high temperature and pressure control, a pressure-temperature (P-T) interpolation control algorithm based on the iso–density P-T curves of water is proposed. A P-T coupling control path is decomposed into two independent interpolation paths: an iso–thermal pressure control and an iso–mass temperature control, which realizes the decoupling control of temperature and pressure. Then, a fuzzy-PID dual mode method is adopted for the pressure control after decoupling, which reduces the overshoot and the dynamic response time of the system. For temperature control, a segmented and grouped electric heating mode is designed to improve the uniformity of the temperature field. A fuzzy PID temperature control algorithm based on grey prediction is proposed to achieve high precision temperature control with small overshoot. Finally, the effectiveness of the proposed methods is verified by experiments.

China's greenhouse crop cultivation, flower cultivation and so on are still in a slightly backward stage. As a large agricultural country, it is very important to produce a thermostatic greenhouse with low price, strong environmental universality and stable performance. The development of artificial intelligence technology promotes the development of intelligent agriculture [1]. The agricultural expert decision-making system based on artificial intelligence technology improves the accuracy of agricultural knowledge acquisition and decision-making expression, and promotes the development of agriculture towards precision [2]. Therefore, it is very important to realize intelligent control of ambient temperature in order to meet the demand of growing environment in strawberry greenhouses. Based on the problem of temperature control, a fuzzy PID control method is proposed to realize the self-tuning of PID parameters and meet the requirements of PID parameters regulation in the temperature control process. In addition, compared with the conventional PID algorithm, the fuzzy PID algorithm has the characteristics of short rise time, fast response speed and high control accuracy. The fuzzy PID algorithm has good control performance on the temperature control system.

This paper presents the design of an intelligent controller, which has comfort control, for an air-conditioning system. The traditional air-conditioning control only focuses on temperature control problems, but does not consider the human sensation of thermal comfort. In this paper, not only temperature control problems are considered but also a PMV index is proposed which is influenced mostly by indoor temperature, relative humidity, air flow velocity, mean radiant temperature, clothing condition, and activity level. This predicts the human sensation of thermal comfort. Since the air-conditioning system is nonlinear and time-varying, fuzzy theory is proposed for the design of this intelligent controller. In addition to the traditional fuzzy control, the PMV index is also considered and comfort control by digital signal processor (DSP) is achieved

ABSTRACTIn this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.

A PID - based instant heat heater measurement and control system is designed. The problem of temperature control is solved for the water heater on the current market that cannot set a specific water temperature and achieve temperature control. In this work, PID control strategy is adopted, the algorithm design is divided into sub-modules, the high cohesion and low coupling between modules is realized, and the design of consistency compensation scheme for water heaters of different equipment is carried out in this paper. The temperature control system designed in this paper can achieve rapid heating in 5-8s, steady-state accuracy can be controlled steadily in the ±0.5°C, and can provide a specific water temperature fonction, the system designed in this paper also has good equipment consistency. The implementation of water heater measurement and control system and the experimental verification of related performance are introduced.

Traditional control of nonlinear processes is based on conventional linear control theories, due to existence of wealthy tools for the linear design and analysis. Nonlinear control theories are always deal the concept of nonlinear control design. Recursive (backstepping) technique is a novel framework for nonlinear control design. Backstepping is recently developed design tool for constructing globally stabilized control laws for a certain class of nonlinear dynamic systems. Backstepping technique is designed by breaking down a complex nonlinear system into smaller subsystems and design control Lyapunov function and virtual controls for each and every subsystem individually. Finally, combined the individual controllers designed for the subsystem and make a single one. Lower triangular form can only handle with this technique. A trademark of backstepping is that allows us to benefit from useful nonlinearities. This can be done by choosing virtual control laws properly. Effectiveness of the controller is demonstrated through simulations for large perturbations in plant parameters. The main advantage of backstepping technique is that it can use everywhere irrespective of the operating points.

This paper addresses the problem of adaptive fuzzy tracking control for aclass of nonlinearly parameterized systems with unknown control directions.In this paper, the nonlinearly parameterized functions are lumped into the unknown continuous functionswhich can be approximated by using the fuzzy logic systems (FLS) in Mamdani type. Then, the Nussbaum-type function is used to detect the unknown control direction and based on the backstepping technique, the adaptive fuzzycontroller is designed. The main advantages of this paper are that (1) in the existing results the separation principle is used to deal with the nonlinearly parameterized functions, unlike them in this paper, the FLS are applied to approximate the nonlinearly parameterized functions, (2) by using the minimal learning parameters (MLP) algorithm, onlyone parameter needs to be adjusted online in the controller design procedure, which reduces the onlinecomputation burden greatly, (3) the Nussbaum-gain technique is introduced to resolve the unknown control direction problems. It is proven that the proposed control scheme renders the closed-loopsystem stable in the sense of semiglobal uniformly ultimately bounded (UUB). Finally, simulation results are provided to show the effectiveness of the proposed approach.

This paper addresses the problem of adaptive output feedback control for a class of non-triangular time-varying delay system with input constraints and full-state constraints. A variable separation approach is adopted to overcome the design difficulty from the non-triangular structure. A novel Lyapunov function is introduced to compensate the time-delay terms. Unknown functions are approximated by the radial basis function neural networks. Only one parameter needs to be adjusted online, and a dynamic surface control technique is employed to reduce the computation burden. Combining the barrier Lyapunov function with a backstepping technique in the controller design procedure, the proposed controller guarantees that all the signals in the closed-loop system are uniformly ultimately bounded and the full-state constraints are met. The simulation results demonstrate the effectiveness of the proposed approach.

This paper develops a fractional order adaptive fuzzy backstepping control scheme for an incommensurate fractional order uncertain nonlinear multiple-input multiple-output (MIMO) systems with external disturbance and input saturation. Combined with the adaptive and backstepping technique, the fuzzy logic system is used to approximate the unknown nonlinear uncertainties in each step of the backstepping, and the incommensurate fractional order parameters update laws for fuzzy logic system, unknown parameters as well as the external disturbances are proposed in the controller to compensate the unknown nonlinearities, unknown parameters and disturbances. With the aids of the frequency distributed model of fractional integrator for the fractional order systems in the procedure of controller design, the stability of the closed loop system is established. At last, two simulation examples are demonstrated to verify the robustness and effectiveness of the proposed controller.

This paper is concerned with the problem of output feedback adaptive tracking control for a class of uncertain nonlinear time-delay systems (NTDS) with both uncertain output function and unknown control directions. By introducing a linear state transformation, the original system is transformed into a new system for which output feedback control design becomes feasible. Without the precise information on output function, an input-driven high-gain state observer is presented to estimate the unavailable states of the new system. Moreover, some radial basis function neural networks (RBFNNs) are employed to approximate the lumped unknown nonlinear functions during the procedure of control design. Then, by combining the Lyapunov–Krasovskii functional method, the Nussbaum gain function approach and the backstepping technique, a novel adaptive output feedback control scheme including only one parameter to be updated is developed for such systems. As a result, all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error can remain in a small neighbourhood of zero. Finally, two simulation examples are given to validate the effectiveness and applicability of the proposed design method.

In this paper, a new temperature control strategy is proposed by analyzing the working mechanism of the kiln and the temperature control problem in it. First of all, fuzzy neural network is applied to model the foam glass kiln, and optimize weights and threshold value of the neural network with the aid of the Clonal Selection Algorithm. Single and changeless control strategy is no longer applied in the controller design. The structure of the controller is modified at any time in the process of production according to the temperature output error, and its aim is to achieve the best optimal control of the foam glass kiln. The application of the model in the control system is successful and effective. Good control effect makes the product quality has been greatly improved.

This paper studies the temperature control for an automobile air-conditioning (AC) system, especially the control of air flow rate in the circulation. Based on energy balance and mass conservation laws, the control strategy is proposed by analyzing the heat exchange between heat gains form outdoor environment and the AC equipment. To be more precise, sensible heat exchange characterized by room sensible heat factor (RSHF) responses to the change in temperature; whereas latent heat exchange characterized by apparatus sensible heat factor (ASHF) responses to the variation in humidity. The problem of temperature control is to assess not only the sensible heat transfer, resulting from the mismatching between RSHF and ASHF while the system attains its equilibrium state, but also the extra cooling load while the system is on the transient stage. Simulation results show a good agreement that the car compartment condition will converge into the comfort zone defined by ASHRAE Standard, regardless of initial conditions.

In aerospace instruments, the problem of temperature control can become severe because convettive cooling cannot be used; heat must be transferred by radiative means alone. Systems are exposed to a rigorous thermal environment and there are strict limitations on the power, weight and volumes available. A surprisingly high degree of temperature control can be accomplished, however, by the use of a passive system of spectrally selective surfaces. In this paper, one approach to the design of passive temperature control systems is discussed by detailed reference to an instrument intended for use on the lunar surface.

Microwave is widely used in our daily lives and industrial production processes. Due to the uncertainty, nonlinearity and time-varying in the microwave heating process, establishing an accuracy mathematical model to control the microwave heating process is hard. To address this issue, this paper proposes an intelligent temperature control scheme for microwave heating process by adaptive dynamic programming(ADP). Firstly, neural networks based temperature predictive model is established by experimental process data. Secondly the problem of temperature control in microwave heating process can be transformed into the problem of solving error adjustment. ADP control algorithm is used to solve the error adjustment problem. Finally, simulation results demonstarte that our proposed control scheme can effectively control the heating temperature while maintaining a stable microwave power adjustment.

Describes the application of an advanced model predictive adaptive controller to the problem of batch reactor temperature control. Temperature control on these systems is difficult for conventional proportional-integral-derivative (PID) controllers because the response is characterized by an open loop integrator with long delay and time constant. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products and reaction rates can be highly temperature dependent. The applications discussed in the paper include a PVC reactor and an Ethoxylated fatty acid reactor. In each case, the variability of the reactor temperature was reduced by 60% or more. Improved temperature control permitted operation at higher reaction temperatures with higher sustained feed rates of reactants and catalysts while remaining within product temperature limits. Batch cycle times were reduced by as much as 35% due to the higher sustained reaction rates. The applications demonstrate the attractive economics for optimization of batch reactors with model predictive controls and highlight the opportunity for tremendous improvements in batch consistency, reduced batch cycle times, and improved productivity.