Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

Model-Free Control Design for Nonlinear Mechanical Systems

In order to alleviate the congestion between the side road and the freeway, a novel balancing control scheme is proposed based on model free adaptive predictive balancing control (MFAPBC). The proposed MFAPBC is a typical data driven control method, which merely utilize the measured input and output (I/O) data of side road and freeway to update the control input signal. Model free adaptive predictive control (MFAPC) has the advantages of model predictive control (MPC) and model free adaptive control (MFAC); that is, only the I/O data of the controlled plant are used to predict the system output sequence within the prediction horizon and to calculate the control input sequence within the control input horizon, without the controlled plant model involved. The effectiveness of the proposed MFAPBC method is test in simulation for side road and freeway system.

pH neutralization reaction process plays a crucial role in Waste Water Treatment Process (WWTP). Traditional PID Proportion Integral Differential, (or even advanced PID control) algorithms have poor performance on WWTP due to the strong non-linearity, large time lag, and large inertia characteristics of pH neutralization. Therefore, finding a superior control method to maintain the pH value of wastewater within the normal range will greatly help to improve the efficiency and effectiveness of wastewater treatment. The chemical reaction mechanism of pH neutralization reaction process is first analyzed, and a mechanistic model of pH neutralization reaction process is developed based on the reaction of ions during acid-alkali neutralization and the electric balance equation. Then, combining the characteristics of generalized predictive control and Model-Free Adaptive Control (MFAC), a Model-Free Adaptive Predictive Control (MFAPC) method based on compact format dynamic linearization is introduced. An Improved Model Free Adaptive PI Predictive Control algorithm (IMFAPC) with proportional (P) and integral (I) algorithms is proposed to further improve the control performance. IMFAPC is proposed on the basis of MFAPC, combining the advantages of generalized predictive control, introducing a PI module consisting of error and error sum, and predicting the PI module, making it possible to produce more accurate constraints on the control inputs, avoiding increasing errors, and improving the control effect of delayed systems at the same time. pH neutralization process simulation experimental results show that compared with the ordinary Model-Free Adaptive Control (MFAC) and MFAPC, the IMFAPC control algorithms has the best performance in terms of accuracy, overshoot, and the robustness.

The complicated physical and chemical reactions in the internal complex operating environment of smelting process and the Blast Furnace (BF) have led to the difficulty of establishing the model-based controllers. Therefore, model free control methods should be used that meet the actual needs of the engineering systems. However, due to the sparse characteristic of the molten iron quality (MIQ) data in BF ironmaking, traditional model free adaptive control based MIQ control methods cannot control such a complex industrial system with strong nonlinear time-varying dynamics. In this paper, an extended and compact form dynamic linearization (CFDL) based model free adaptive predictive control (MFAPC) scheme (CFDL-MFAPC) is proposed for multivariate MIQ indices by generalizing the CFDL-MFAPC method only for SISO system to MIMO system. Two groups of verification experiments are performed to evaluate the performance of the controller. The results show that the proposed method has not only a better control performance than the compared traditional CFDL based model free adaptive control method and data-driven model predictive control (MPC) method, but also can guarantee the bounded-input bounded-output stability of the MIQ output of the control system for BF ironmaking process.

In control theory, traditional methods are basically relied on the mathematical model of a plant to design suitable control schemes. First, the model has to be successfully developed which reflects precisely the system dynamic behaviors within certain operating conditions. Theoretically, based on the true assumed plant model, the controller design and system stability analysis can be carried out. On the other hand, since the last few decades an alternative control strategy, which only utilizes the available input-output information from the closed-loop system to analyze and design controllers, has been proposed. This novel data-driven or model-free control method can reduce efforts spending on the system modeling tasks. In addition, by using directly the updated system data the unknown time-varying parameters of the given system/process and design controller are estimated and corrected continuously at each operating point. These updated parameters are necessary to determine the required control input energy. In this thesis, a recently developed data-driven control method called model-free adaptive control (MFAC) will be intensively investigated to acquire further control performance improvements by applying the method to the field of vibration reduction. The main principle of MFAC is replacement of the unknown complicated dynamical characteristics of the initial (nonlinear) system by an equivalent linearized model based on the on-line updated system input-output data. Hence, the assumed system model is built up at each discrete-time instant during the system operation. To design control, the identified parameters from the local dynamic model should be utilized explicitly. This research will develop different modified/improved MFAC strategies which can be effectively applied to a class of complex mechanical systems for vibration reduction purpose. Traditional MFAC often uses conventional projection algorithm to estimate and update the unknown system parameters of the linearized data model. To improve on-line estimation accuracy, in this thesis, recursive least-squares algorithm (RLSA) will be applied. Furthermore, the tracking control performance of MFAC can be improved by minimizing not only the current output error amplitudes, but also the error variations within a fixed-length of time window from the past. As a result, a modified control input law will be generated. In addition, compact-form dynamic linearization (CFDL) has been considered in MFAC design as a simplified technique for system linearization. In this work, the CFDL concept will be applied not only to the unknown (nonlinear) plant but also to an assumed nonlinear controller. Subsequently, a linearized controller structure is derived, in which a matrix of unknown controller parameters needs to be estimated. By proposing a modified objective function of the controller parameter matrix, an improved estimation algorithm for updating these parameters on-line is introduced. Moreover, based on the fundamentals of MFAC and generalized model predictive control, modified model-free adaptive predictive control programs are proposed, in which RLSA and its modification can be implemented for parameter estimation instead of using traditional projection algorithm. Another dynamic linearization technique called partial-form dynamic linearization (PFDL) is implemented to the MFAC design for multivariable systems. In this contribution, an improved PFDL-based data-driven control strategy will be developed. A partial-form data model of the original system is constructed locally which contains a set of unknown parameter matrices namely pseudo-jacobian matrix. These matrices are recursively updated by using the measured system input-output signals. In addition to known approaches, in this study, on-line parameter estimation based on the recursive least-squares method is applied to the PFDL model. For control realization, a modified PFDL-based control input equation is proposed by considering minimization of the tracking error differences. To verify control effectiveness, the proposed controllers will be executed to reduce the free-vibrations of an elastic ship-mounted crane due to the non-zero initial excitation of the payload. The crane is represented as a typical complex and flexible system, in which the in-plane oscillations of the elastic boom and the payload must be reduced or eliminated to increase the crane safety operation. Simulation results demonstrate that, the angular displacements of the output signals as well as the payload are reduced significantly within a short length of time by using the modified model-free controllers. Additionally, the proposed MFAC programs work effectively and better control results are obtained when varying several design controller parameters in comparison with conventional methods.

A model-free adaptive predictive control algorithm based on an improved extended state observer (IESO) is proposed to solve the problem that the primary permanent magnet linear motor is susceptible to time-varying parameters and unknown disturbances. Firstly, a model-free adaptive control algorithm based on compact format is designed to achieve high control precision of the system and reduce thrust fluctuation, only through the input/output data of the system. Because the traditional model-free adaptive control is too sensitive to the internal parameters of the controller, a combination of model-free adaptive control and predictive control is further developed. By predicting the data for a future time in advance, the sensitivity to the internal parameters of the controller is reduced and the control performance is further improved. Since the load change and other nonlinear disturbances in practical applications have a great impact on the control effect of the system, an improved extended state observer is further used to compensate for the impact of nonlinear disturbances on the control system. In addition, the stability of the closed-loop system is analyzed. Comparable simulation results clearly demonstrate the good tracking performance and strong robustness of the proposed control.

In this paper, the current application status of gas turbine control technology is introduced, it presents the model free control theory and method in perspective of definition of model free control and model free adaptive control algorithms mainly existed and its advantages. This paper introduces some unsolved problems by using traditional control approaches. It gives reasons why the model free is suitable for the control of gas turbine. Afterwards, it describes how the model free adaptive control technology is applied to gas turbine and its related techniques in detail in this paper. In this part, it could provide theoretical foundation for applying model free control in actual gas turbine control system. Finally, conclusions and issues needed further research are put forward at the end of this paper.

Perimeter Control for Two-region Urban Traffic System Based on Model Free Adaptive Predictive Control with Constraints

Perimeter control (PC) and route guidance (RG) have become two powerful traffic control methods to address the urban traffic congestion, especially for multi-region urban traffic systems (MRUTS). Accurate traffic system model is necessary in majority of the existing PC and RG methods. If the traffic model is inaccurate or unavailable, the aforementioned PC and RG strategies may not work well or cannot be applied. In this paper, a novel data driven scheme called constrained model free adaptive predictive control (cMFAPC) is provided for PC and RG of MRUTS. Two outstanding advantages of this method are that it can only use the input and output (I/O) data of the controlled MRUTS to design the PC and RG strategies, instead of utilizing the traffic dynamics model, and the merits of model free adaptive control (MFAC) method and model predictive control (MPC) approach are combined in the proposed cMFAPC strategy. The effectiveness of cMFAPC strategy and its superiority over other commonly used PC and RG methods are verified via simulation.

This paper investigates state feedback control for a class of discrete-time multiple input and multiple output nonlinear systems from the perspective of model-free adaptive control and state observation. The design of a dynamic state feedback control can be efficiently carried out using dynamic linearization and state observation. The stability of the proposed method is guaranteed by theoretical analysis. Numerical simulation tests and experimentation on a continuous stirred tank reactor are carried out to validate the effectiveness of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The growth in the scale of factories and the complexity of associated production processes increases the complexity and time involved in associated mathematical modelling. Data driven approaches to control remove the need to model processes. To the best of the authors’ knowledge, existing approaches to model-free adaptive control (MFAC) of general systems are all based on an input-output control paradigm. These methods thus cannot guarantee the stability of the system state. The purpose of this study is to develop a novel Model-Free Adaptive Control (MFAC) approach to achieve control of the system state. In this paper, the assumptions required to achieve model-free adaptive control by state feedback are presented mathematically. A controller design and the associated stability proof are then presented. Numerical simulation and experimentation is conducted to validate the effectiveness of the proposed approach. In future research, state feedback data control in the presence of random disturbances will be investigated.

This book categorizes the wide area of data-driven model-free controllers, reveals the exact benefits of such controllers, gives the in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the theory and mathematical definitions of one of the above mentioned algorithms. The second section of each chapter is dedicated to the examples and applications of the corresponding control algorithms in practical engineering problems. This book proposes to avoid complex mathematical equations, being generic as it includes several types of data-driven model-free controllers, such as Iterative Feedback Tuning controllers, Model-Free Controllers (intelligent PID controllers), Model-Free Adaptive Controllers, model-free sliding mode controllers, hybrid model‐free and model‐free adaptive‐Virtual Reference Feedback Tuning controllers, hybrid model-free and model-free adaptive fuzzy controllers and cooperative model-free controllers. The book includes the topic of optimal model-free controllers, as well. The optimal tuning of model-free controllers is treated in the chapters that deal with Iterative Feedback Tuning and Virtual Reference Feedback Tuning. Moreover, the extension of some model-free control algorithms to the consensus and formation-tracking problem of multi-agent dynamic systems is provided. This book can be considered as a textbook for undergraduate and postgraduate students, as well as a professional reference for industrial and academic researchers, attracting the readers from both industry and academia.

In this paper, the problem of model-free adaptive predictive control (MFAPC) under denial-of-service attacks and quantization effects for high-speed trains with unknown models is investigated. Since the system model of the high-speed train is unknown, the data-relational description of a high-speed train system is obtained by using the dynamic linearization technique. Secondly, the challenge of periodic denial-of-service (DoS) attacks in the network channel is considered, and, assuming that the DoS attack obeys the Bernoulli distribution, a model-free adaptive predictive control scheme based on quantized signals is proposed. Then, through rigorous theoretical analyses, it is proven that the tracking error is bounded, and the final bound depends on the desired trajectory. Finally, the correctness of these theoretical analyses is verified through numerical simulation.

In order to solve the dynamic modeling and parameter identification problems of unmanned vehicles trajectory tracking control, a mathematical model of unmanned vehicle trajectory tracking is designed based on the data-driven model-free adaptive control method, which does not depend on the precise dynamic model of the unmanned vehicle. The model-free adaptive control method is extended to the unmanned vehicle trajectory tracking control, and the model-free controller is designed and applied to the driverless vehicle trajectory tracking control. Aiming at the problem that the general compact form dynamic linearization model-free adaptive control (CFDL-MFAC) algorithm cannot converge in vehicle trajectory tracking control, combined with the dynamic characteristics of unmanned vehicles, an improved model-free adaptive control algorithm is proposed in this paper. The simulation results verify the effectiveness and feasibility of the algorithm. Mathematical simulation results show that the improved model-free adaptive algorithm of the designed unmanned vehicle is effective and can effectively implement the trajectory tracking control of the unmanned vehicle. At the same time, the design of the controller does not depend on the kinematics and dynamics models of the unmanned vehicle, and it has high control accuracy.

The design of an accurate model often appears as the most challenging tasks for control engineers especially focusing to the control of nonlinear systems with unknown parameters or effects to be identified in parallel. For this reason, development of model-free control methods is of increasing importance. The class of model-free control approaches is defined by the non-use of any knowledge about the underlying structure and/or related parameters of the dynamical system. Therefore the major criteria to evaluate model-free control performance are aspects regarding robustness against unknown inputs and disturbances to achieve a suitable tracking performance including ensuring stability. Consequently it is assumed that the system plant model to be controlled is unknown, only the inputs and outputs are used as measurements. In this contribution a modified model-free adaptive approach is given as the extended version of existing model-free adaptive control to improve the performance according to the tracking error at each sample time. Using modified model-free adaptive controller, the control goal can be achieved efficiently without an individual control design process for different kinds unknown nonlinear systems. The main contribution of this paper is to extend the modified model-free adaptive control method to unknown nonlinear multi-input multi-output (MIMO) systems. A numerical example is shown to demonstrate the successful application and performance of this method.

Based on the model-free adaptive control (MFAC) theory, the temperature tracking control problem of single-effect LiBr/H2O absorption chiller is explored. Due to the complex nonlinearity and strong coupling characteristics of the absorption refrigeration system, model-free adaptive control strategy is designed for its temperature tracking control. Nevertheless, the traditional model-free adaptive control has a slow tracking speed and poor denoising ability. In order to improve its control effect, output error rate is added to the objective function and new control laws of model-free adaptive control with output error rate (MFAC-OER) have been derived through an exhaustive convergence and stability analysis. The input information and output information of the absorption refrigeration system, namely the hot water pump frequency and chilled water outlet water temperature, are combined. The data model of the absorption refrigeration system is subsequently deduced using a compact format dynamic linearization method. Next, based on the single-effect absorption chiller experimental platform in our laboratory, its sixth-order dynamic model is built. Finally, the effectiveness and practicability of the improved control strategy are illustrated by numerical simulations and experimental operating data from our laboratory as well as by the dynamical model of the absorption chiller.