Unknown Plant Dynamics Research Articles

Periodic event-triggered controller design with Bayesian optimization: An emulation-based approach

Periodic event-triggered controller design with Bayesian optimization: An emulation-based approach

RNN-Based Learning of Nonlinear Dynamic System Using Wireless IIoT Networks

We consider the recurrent neural network (RNN)-based remote state estimation for nonlinear dynamic systems with unknown state dynamics. The nonlinear dynamic plant is monitored by multiple distributed IIoT sensors over a random access wireless network with shared common spectrum. We focus on the remote state estimation algorithm design so as to achieve remote state estimation stability subject to noninvertible nonlinear sensor state observations, imperfect channel state information (CSI) at the remote estimator, and various wireless impairments, such as multisensor interference, wireless fading, and additive channel noise. Utilizing a state diffeomorphism, the original system is transformed into a canonical form with a linear rank deficient observation matrix. We propose a novel RNN remote state estimator based on the pole placement design associated with the transformed rank deficient state measurement matrices. We further propose a novel online training algorithm such that the RNN at the remote estimator can not only address the divergence issue over wireless networks but also effectively learn the unknown nonlinear plant dynamics despite rank deficiency and imperfect CSI. Using the Lyapunov drift analysis approach, we establish closed-form sufficient requirements on the communication resources needed to achieve almost sure stability of both state estimation and RNN online training in the high signal-to-noise ratio (SNR) regime. As a result, our proposed scheme is asymptomatic optimal for large SNR in the sense that both the plant state and the unknown plant nonlinearity can be perfectly recovered at the remote estimator. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.

Grey Wolf and Weighted Whale Algorithm Optimized IT2 Fuzzy Sliding Mode Backstepping Control with Fractional-Order Command Filter for a Nonlinear Dynamic System

In this study, a fractional-order sliding mode backstepping control method was proposed, which involved the use of a fractional-order command filter, an interval type-2 fuzzy logic system approximation method, and a grey wolf and weighted whale optimization algorithm for multi-input multi-output nonlinear dynamic systems. For designing the stabilizing controls of the backstepping control, a novel fractional-order sliding mode surface was suggested. Further, the transformed errors that occurred during the recursive design steps were easily compensated by the controllers constructed using a new fractional-order command filter. Thus, the differentiation issue of the virtual control in the conventional backstepping control design could be bypassed with a simpler controller structure. Subsequently, the unknown plant dynamics were approximated by an interval type-2 fuzzy logic system. The uncertainties, such as the approximation error and the external disturbance, were compensated by the fractional-order sliding mode control that was added in the backstepping controller. Furthermore, the controller parameters and the fuzzy logic system were optimized via a grey wolf and weighted whale optimization algorithm to obtain a faster tuning process and an improved control performance. Simulation results demonstrated that the fractional-order sliding mode backstepping control scheme provides enhanced control performance over the conventional backstepping control system. Thus, in this paper, a fractional-order sliding mode surface and fractional-order backstepping control are studied, which provide more rapid convergence and enhanced robustness. Furthermore, a hybrid grey wolf and weighted whale optimization algorithm are proposed to provide an improved learning performance than those of conventional grey wolf optimization and weighted whale optimization methods.

Fuzzy Supertwisting Dynamic Surface Control for MIMO Strict-Feedback Nonlinear Dynamic Systems With Supertwisting Nonlinear Disturbance Observer and a New Partial Tracking Error Constraint

This paper proposes a hybrid fuzzy dynamic surface control (DSC) method combined with a supertwisting control (STC) method and a supertwisting nonlinear disturbance observer (STO) for multi-input multi-output nonlinear strict-feedback systems is proposed, in addition to a new partial tracking error constraining method. For the design of the stabilizing controls of the DSC, the virtual tracking errors in recursive procedures were transformed into the design variables of the STC. The uncertainties were estimated by an STO, which includes the approximation error between the unknown plant dynamics and fuzzy optimization function, in addition to the external disturbances. Furthermore, the proposed DSC combined with the STC scheme can prevent the complex analysis problem of the filtered output error in conventional DSC schemes. Moreover, a novel convenient tracking error constraining method is also presented. Simulation results revealed that the proposed scheme has an improved tracking error performance and robustness to uncertainty than control systems with a conventional nonlinear disturbance observer and several other DSC schemes.

A Nonlinear Fuzzy Neural Network Modeling Approach Using an Improved Genetic Algorithm

Fuzzy neural networks (FNNs) are quite useful for nonlinear system identification when only the input/output information is available. A new FNN framework is first proposed by combining an AutoRegressive with exogenous input (ARX) with the nonlinear Tanh function in the Takagi–Sugeno (T-S) type fuzzy consequent part. An improved genetic algorithm is then designed to optimize the structure and parameters of the FNN simultaneously under unknown plant dynamics. The hybrid encoding/decoding, neighborhood search operator, and maintain operator are presented to optimize the input structure of the ARX plus the nonlinear function submodel, the number of the fuzzy rules, and the parameters of the membership function. Three benchmarks and a liquid level modeling problem in an industrial coke furnace are utilized to compare the performance of several typical methods. Simulation results show that the proposed method is superior in structure simplification, modeling precision, and generalization capability.

Controller design for personalized drug administration in cancer therapy: Successive approximation approach

SummaryA novel controller design approach is developed to determine personalized chemotherapy drug delivery protocols for cancer treatment. The methodology combines successive approximation approach for optimal control and model reference adaptive control to realize the proposed drug administration scenario for patients without prior knowledge of model parameters in the nonlinear cancer dynamics. Although many approaches have been proposed to determine the optimal drug delivery protocol for eradicating tumor in the nonlinear cancer model, the main shortcoming of these approaches is the requirement of nonlinear model dynamics, which is unknown to physicians in reality. To overcome this deficiency, we first determine the optimal drug delivery protocol for a reference patient with known mathematical model and parameters via successive approximation approach technique. Then, using the proposed approach, we adapt the reference patient's drug administration scenario to unknown cancer patients by suggesting a new adaptation mechanism for the unknown nonlinear plant dynamics. An efficient and robust approach is proposed here for the physicians to prescribe a personalized chemotherapy protocol for a cancer patient by regulating the drug delivery protocol of a reference patient. The efficacy of the proposed algorithm in eradicating the tumor lumps with different sizes in several patients is verified using numerical simulations in which the unknown parameters are randomly selected in the Monte Carlo approach.

Enhanced Active Disturbance Rejection Control for Time-Delay Systems

Abstract In this paper, a further enhanced active disturbance rejection control method is proposed for time-delay systems, which can be generally applied for open-loop stable, integrating and unstable processes. The plant model is adopted to design an extended state observer (ESO) such that both the disturbance and the unknown plant dynamics could be estimated. Then a prediction filter is designed to estimate the delay-free output response for control design, while satisfying the asymptotic stability constraints. A notable merit is that there is a single tuning parameter in the proposed ESO or controllers, which can be monotonically tuned to achieve a good trade-off between the control performance and robustness. The robust stability constraint of the closed-loop system is derived in terms of the linear matrix inequality and the Wirtinger inequality. An illustrative example is given to demonstrate the effectiveness of the proposed method.

Adaptive fuzzy sliding mode control of input-delayed uncertain nonlinear systems through output-feedback

This paper deals with the design of stabilizing controllers for a class of uncertain nonlinear systems with unknown constant input time delay. A new control method is introduced which merges an adaptive fuzzy sliding mode predictor with an adaptive fuzzy sliding mode controller to circumvent the effects of the unknown time delay, nonlinearities, and unknown plant dynamics. The proposed predictor and controller can be implemented in the real time without recursive and tedious calculations and only by using output-feedback. The uniform ultimate boundedness of the closed-loop system is also guaranteed. Simulation and experiment studies on a nonlinear benchmark problem reveal the effectiveness of the proposed method.

DESIGN AN ADAPTIVE WAVELET NEURAL NETWORK CONTROLLER FOR A TWO-WHEELED MOBILE ROBOT LIKE VEHICLE

This paper presents an analytical approach to design an adaptive backstepping wavelet neural network (WNN)-based controller for global asymptotic stabilization of a two-wheeled mobile robot (TWMR). It is assumed that the dynamics model is unknown, and also system exposed to an external disturbance. The design method is based on the concept of backstepping method. At the first level, adaptive backstepping controller is employed. This controller is taking advantage of WNN identifier for estimating of unknown plant dynamics. Moreover, since the adaption laws of controller are extracted in sense of Lyapunov function, the stability of closed loop is guaranteed. At the second level, robust controller is combined with primary controller, which results L2 tracking performance and comforts lumped uncertainties exit in control system due to approximation error and external disturbance. Finally, a numerical example for the proposed control scheme is presented.

Adaptive iterative learning control for nonlinear pure-feedback systems with initial state error based on fuzzy approximation

Abstract In this paper, an iterative learning control strategy is presented for a class of nonlinear pure-feedback systems with initial state error using fuzzy logic system. The proposed control scheme utilizes fuzzy logic systems to learn the behavior of the unknown plant dynamics. Filtered signals are employed to circumvent algebraic loop problems encountered in the implementation of the existing controllers. Backstepping design technique is applied to deal with system dynamics. Based on the Lyapunov-like synthesis, we show that all signals in the closed-loop system remain bounded over a pre-specified time interval [0,T]. There even exist initial state errors, the norm of tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity and the learning speed can be easily improved if the learning gain is large enough. A time-varying boundary layer is introduced to solve the problem of initial state error. A typical series is introduced in order to deal with the unknown bound of the approximation errors. Finally, two simulation examples show the feasibility and effectiveness of the approach.

Improvement in DTC-SVM of AC drives using a new robust adaptive control algorithm

In this paper a new robust adaptive speed controller algorithm for AC motor drives is presented. The main feature of this algorithm is that minimum synthesis is required to implement the strategy. MCS algorithm is a significant development of MRAC. The stability of the proposed system is achieved through Popov’s Hyperstability criteria. The new algorithm appeared to be robust in the face of totally unknown plant dynamics, external disturbances and parameter variations with the plant. Finally, a new approach has been successfully implemented on DTC-SVM. Extensive simulation results are presented to validate the proposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.

Robust Passivity-Based Adaptive Control Of A Nonholonomic Mobile Robot Using Fuzzy Logic

This paper considers the position control problem of nonholonomic mobile robots with plant uncertainties and external disturbances. A robust passivity-based controller using fuzzy logic is proposed based on the reduced-form dynamic model of nonholonomic systems. The fuzzy logic system, whose parameters are tuned on-line, is introduced to learn the unknown (uncertain) plant dynamics due to the universal approximation property of fuzzy logic systems, and the adaptive compensator is employed to suppress external disturbances and approximation errors. The proposed control scheme can guarantee that all signals in the closed-loop system are uniformly ultimately bounded. Simulation results show the efficiency of the proposed approach.

Feedback based adaptive compensation of control system sensor uncertainties

In this paper, the problem of adaptively compensating sensor uncertainties is addressed in a feedback based framework. In this study, sensor characteristics are modeled as parametrizable uncertain functions and a compensator is constructed to adaptively cancel the effects of sensor uncertainties, to generate an adaptive estimate of the plant output. Such an estimated output is used for the feedback control law. Adaptive control schemes using a model reference approach with sensor uncertainty compensation are developed for LTI plants with either known or unknown plant dynamics. A new feedback controller structure is developed for the case when the plant dynamics is unknown, to handle the plant and sensor uncertainties. Simulation results are presented to show that the proposed adaptive sensor uncertainty compensation designs significantly improve system tracking performance.

Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A controller is proposed for the robust backstepping control of a class of nonlinear pure-feedback systems using fuzzy logic. The proposed control scheme utilizes fuzzy logic systems to learn the behavior of the unknown plant dynamics. Filtered signals are employed to circumvent algebraic loop problems encountered in the implementation of the usual controllers, and the approximation errors can be efficiently counteracted by employing smooth robust compensators. Most importantly, the uniform ultimate boundedness of all signals in the closed-loop system can be guaranteed, and <emphasis emphasistype="italic">a priori</emphasis> knowledge of the plant dynamics is no longer required. Furthermore, the proposed method can be used for adaptive control of a large class of single-input--single-output nonlinear systems in both strict-feedback and pure-feedback forms, and has great potential in many diverse applications. The performance of the proposed approach is demonstrated through three simulation examples, including one nonlinear pure-feedback and two nonlinear strict-feedback systems. </para>

Control of Multivariable Systems Using Modified Local Optimal Controller

In this paper, a modified local optimal control approach is proposed for multivariable systems. As for unknown plant dynamics, system identification must be performed to obtain a model based on which the local optimal controller is designed and implemented. The proposed method guarantee closed loop stability characteristic when dealing with non-minimum phase plant which is a considerable advantage over the original local optimal controller. In addition to computational efficiency and structure simplicity, experimental results on a lab-based test rig confirm the effectiveness and robustness of the proposed local optimal controller over a conventional genetically tuned PID.

Discrete-Time Backstepping Synchronous Generator Stabilization using a Neural Observer

This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of bounded disturbances, based on a neural observer. A high order neural network structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF); besides the observer is based on a recurrent high-order neural network (RHONN), which estimates the state vectors of the unknown plant dynamics. The learning algorithm for both neural networks is based on an Extended Kalman Filter (EKF). The applicability of the proposed approach is tested, via simulations, by its application to synchronous generators control.

Stable indirect adaptive control based on discrete-time T–S fuzzy model

This paper presents an indirect adaptive fuzzy control scheme for uncertain nonlinear asymptotically stable plants. A discrete-time T-S fuzzy input-output model is employed to approximate the unknown plant dynamics. The T-S fuzzy model is fed with its own states, which are indeed its past outputs, rather than the measurements from the plants. Entirely based on this model, a feedback linearization control law is designed by using the model structure, model parameters and model states and then applied to control the model and the plant. Premise and consequent parameters of the model are updated on-line by gradient descent algorithm and recursive least square estimation method using plant output measurements. Stability analysis shows that if there the model structure is accurate the adaptive controller achieves the bounded tracking error and boundedness of all the closed-loop signals. In the ideal case where the signals are persistently exciting during the model parameter adaptation a perfect asymptotic tracking is ensured. The effectiveness of the method is verified by simulation application to SISO and MIMO example plants.

FCAPS: Fuzzy Controller with Approximated Policy Search Approach

A fuzzy controller requires an engineer to tune its rules for controlling a given plant. To reduce the burden, we develop a gradient-based tuning method for the fuzzy controller. The developed method is closely related to a theory of reinforcement learning, but takes advantages of a practical assumption made for faster learning. In reinforcement learning, values of problem states need to be acquired through lots of trial-and-error interactions between the controller and the plant. And the plant dynamics should also be learned by the controller. In this research, we assume that an approximated value function of the problem states can be represented as a function of a Euclidean distance from a goal state and an action executed at the state. And, we propose to use it for the gradient search as an evaluation function. Our experimental results on a pole-balancing problem show that the proposed method can tune the fuzzy controller to have an optimal policy for reaching the goal state despite an unknown plant dynamics in not only a set-point problem but also a tracking problem.

STABLE ADAPTIVE CONTROL UNDER UNMEASURABLE PLANT STATES

Abstract A dynamic neural network based algorithm for learning control of an unknown nonlinear continuous-time multiple-input-multiple-output plant with unmeasurable states is proposed in this paper. A new dynamic neural network structure is utilised to model the unknown plant dynamics through modelling the input-output mapping. A feedback linearization is applied to design controller for the neural model and the neural states are used as a source of precious information about current plant dynamics. A gradient based update of the weights is performed at discrete time instants over a moving measurement window in order to reduce the model output – real output mismatch. The learning controller is applied to a double link robot arm. Stability of the system is analysed through ultimate boundedness of all signals.

A novel robust adaptive control algorithm for AC drives

Abstract In this paper a robust adaptive control algorithm for AC machine is presented. The main feature of this algorithm is that minimum synthesis is required to implement the strategy—hence the appellation minimum controller synthesis (MCS). Specifically, no plant model is required (apart from a knowledge of the state dimension) and no controller gains have to be calculated. The MCS algorithm appeared to be robust in the face of totally unknown plant dynamics, external disturbances and parameter variations with the plant. Finally a new approach has been successfully implemented on field-oriented controlled drive. Discussion on theoretical aspects, such as, selection of a reference model, stability analysis, gain adaptive and steady state error are included. Results are also presented.