Online Adaptive Control Research Articles

An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation

In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits.

MPPT control for PMSG based wind turbine: Adaptive dynamic programming approach

This paper proposes an online adaptive dynamic programming-based controller for the variable-speed wind turbine system, ensuring it tracks the maximum power point under uncertain conditions. This control scheme is composed of two components: the adaptive optimal control component and the high-order disturbance observer-based adaptive sliding mode control component. The adaptive optimal control part is designed using the online adaptive dynamic programming technique, which is one of the algorithms of the reinforcement learning method. This control component has the role of achieving the optimal characteristics for the nonlinear system. Likewise, the high-order disturbance observer is established to estimate system uncertainties and external disturbances. These approximated disturbances are then fed to the adaptive sliding mode control component for system disturbance compensation. As a result, the system, which is affected by the unknown disturbances, achieves robust optimal performance. In addition, the convergence of the adaptive laws and the stability of the overall system are ensured through the Lyapunov theory. Finally, comparative simulations are conducted to validate the advantages of the proposed control scheme in comparison to the existing methods.

Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

ABSTRACT Additive manufacturing (AM) are an emerging technique to generate complex structures of composite. However, the instability of the AM process leads to adverse manufacturing effects, including dimensional inaccuracies and poor mechanical performance in CFRP composites. Online monitoring and adaptive control based on autonomous cognition are suggested to ensure the accuracy of as-fabricated parts and enhance their quality upon manufacturing. In this study, a method of online monitoring and self-adaptive control based on multi-sensor fusion is proposed to predict and adjust the process parameters during AM of CFRP. The force sensor, visual camera, and thermal camera are employed to obtain the multiple signals upon manufacturing and then realize the autonomous perception, cognition, and decision features. Herein, the quantitative correlations between local defects and multi-sensing features are established to guide the decision-making of closed-loop adjustment. Besides, an empirical surrogate model between the misalignment of fiber bundles and input parameters is built to predict the proper parameters. Moreover, the temperature difference and contact force are selected as the controlled features, which are acquired using a thermal camera and a force sensor. The proposed system offers a novel framework for bolstering both the stability of the AM process and the quality of fabricated components.

Adaptive control for refrigeration via online identification

Vapor compression refrigeration systems (VCRS) occupy a crucial position in modern society and in the field of thermal sciences. However, the operation of VCRS is subjected to both external disturbances (dynamic-changing environment) and inherent system characteristics (coupling or nonlinear features), leading to issues like reduced refrigeration efficiency and significant fluctuations in cooling capacity. To address these challenges and enhance the adaptability of VCRS in dynamically changing environments, this study establishes a dynamic simulation model for VCRS based on the Switched Moving-Boundary method. The impact of external environmental disturbances on refrigeration performance is investigated, and continuous online identification methods are employed to elucidate its internal coupling characteristics and nonlinear features. The adaptive temperature control method is introduced, benefiting from the developed recursive least squares method with a forgetting factor for online identification, achieving precise model identification which facilities the real-time parameters tuning of adaptive controller. The results indicate that the hybrid paradigm of online identification and adaptive control algorithm not only effectively handles various disturbances but also reduces overshoot and IAE by 2–3 orders of magnitude compared to traditional PID controllers. Adaptive PID control maintains overshoot in the 10–4 order of magnitude and IAE in the 10–5 order of magnitude.

Robust optimal control for uncertain wheeled mobile robot based on reinforcement learning: ADP approach

This paper presents a robust optimal control approach for the wheel mobile robot system, which considers the effects of external disturbances, uncertainties, and wheel slipping. The proposed method utilizes an adaptive dynamic programming (ADP) technique in conjunction with a disturbance observer. Initially, the system's state space model is formulated through the utilization of kinematic and dynamic models. Subsequently, the ADP method is employed to establish an online adaptive optimal controller, which solely relies on a single neural network for the purpose of function approximation. The utilization of the disturbance observer in conjunction with the compensation controller serves to alleviate the effects of disturbances. The Lyapunov theorem establishes the stability of the complete closed-loop system and the convergence of the weights of the neural network. The proposed approach has been shown to be effective through simulation under the effect of the disturbances and the change of the desired trajectory.

Design and implementation of adaptive control for industrial robot servo trajectory tracking

Abstract To cope with the complex external environment of the robot, an online adaptive control method for industrial robots is implemented by using robot dynamic parameters as control variables. A Newton-Euler method is implemented to establish a rigid body dynamics model for the robot. Subsequently, we confirm the possibility of dynamic parameter adaptive control by employing the Lyapunov stability theory and derive the dynamic parameter estimation law. Three methods are proposed to realize the observation matrix containing reference information in the adaptive control algorithm. Through physical experiments, it is verified that the dynamic parameter adaptive control is superior to traditional PPI control, and its position tracking error and anti-interference ability are significantly improved.

Nonlinear dynamical system approximation and adaptive control based on hybrid‐feed‐forward recurrent neural network: Simulation and stability analysis

AbstractWe proposed an online identification and adaptive control framework for the nonlinear dynamical systems using a novel hybrid‐feed‐forward recurrent neural network (HFRNN) model. The HFRNN is a combination of a feed‐forward neural network (FFNN) and a local recurrent neural network (LRNN). We aim to leverage the simplicity of FFNN and the effectiveness of RNN to capture changing dynamics accurately and design an indirect adaptive control scheme. To derive the weights update equations, we have applied the gradient‐descent‐based Back‐Propagation (BP) technique, and the stability of the proposed learning strategy is proven using the Lyapunov stability principles. We also compared the proposed method's results with those of the Jordan network‐based controller (JNC) and the local recurrent network‐based controller (LRNC) in the simulation examples. The results demonstrate that our approach performs satisfactorily, even in the presence of disturbance signals.

Investigation of Legendre polynomials expanded adaptive controller with inverse compensation for high frequency tracking of electro-hydraulic force systems

Accurate force tracking of an electro-hydraulic force system (EHFS) is of great significance in various industrial applications. As the EHFS is inevitably confronted with hydraulic nonlinearities, dynamic variations and uncertain disturbances, the real-time force tracking performance is generally unsatisfactory, especially for high frequency command force signals. For reducing the force tracking error with relatively high frequency inputs, a Legendre polynomials expanded adaptive controller with inverse compensation is proposed in this paper. The proposed controller is constructed by introducing an offline designed inverse compensation (IC) controller and an online adaptive controller to the EHFS governed by traditional Proportional Integral (PI) controller, in which the former IC controller is acting as the inner loop controller and the latter adaptive controller is regarded as an outer controller. The inner loop IC controller with fixed control parameters is offline designed on the basis of traditional PI controller by means of the generalized projection identification algorithm and the zero magnitude error tracking technology, focusing on extending the real-time force tracking bandwidth of the EHFS. The outer loop online adaptive controller is developed by Legendre polynomials expanded adaptive filters with nonlinear mapping ability, whose weights are online updated by an adaptive tuning algorithm with the aim of handling system’s dynamic variations, nonlinearities and uncertain disturbances. The proposed controller is implemented on a real EHFS test rig by the xPC/Target rapid prototyping technique, and comparative experimental results demonstrate that the proposed controller can achieve much higher high frequency tracking performance than the traditional PI and IC controller commonly used in industry.

Adaptive Anti-Surge Control Strategy for PEM Fuel Cell Vehicle With Online Surge Detection

In this study, an online adaptive anti-surge control (OAASC) strategy is proposed for proton exchange membrane (PEM) fuel cell system. Insufficient attention has been given to the issue of the surge in the air supply system, which can damage the compressor and stack, reducing the fuel cell vehicle’s efficiency and lifespan. The OAASC approach employs a multiple time-domain sliding window technique and characteristic energy-based correction to detect and adaptively contain surges before they become serious. A dynamic model of the fuel cell air supply system with surge prediction is established using a two-stage parameter identification process and is verified through 60 kW fuel cell engine experiments. Simulation and experimental comparisons between OAASC and standard industrial methods are conducted to evaluate the proposed method’s performance. The experimental results demonstrate that the proposed strategy improves control performance, with surges being detected within 0.2 s and recovering within 0.5 s.

Online Adaptive Dynamic Programming for Optimal Self-Learning Control of VTOL Aircraft Systems With Disturbances

In this paper, a novel online adaptive dynamic programming control algorithm is developed to solve optimal control problems for Vertical Take Off and Landing (VTOL) aircraft systems. Considering the input disturbances of the VTOL systems and adding perturbation interference term to the value function, a robust policy iteration-based ADP method will be introduced to obtain the optimal controller with an approximate optimal control approach. The convergence property is developed to ensure the value function converges to a finite neighborhood of the optimal one. The uniform ultimate boundedness (UUB) of the iterative errors will be strictly proved by Lyapunov stability theory. Finally, we will give mathematical simulation results with the developed method. Compared with sliding mode control (SMC) and linear quadratic regulator (LQR) methods, the simulation results illustrate better overall performance of the novel method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As an effective tool to solve the optimal control problem of complex nonlinear systems, adaptive dynamic programming (ADP) can effectively overcome the computational problems caused by “curse of dimensionality”. VTOL aircraft system is a typical strongly coupled, underactuate, non-minimum phase system, and the sudden random disturbances will make the control task more challenging. Aiming at the particularity of VTOL system, a new online policy iteration-based ADP algorithm is developed to obtain optimal control with an approximate optimal control strategy in this paper. Decoupling and disturbances can be effectively avoided. convergence will be analyzed to make sure the value function converge to a finite neighborhood of the optimal value function. The UUB property of the system will be proved by the Lyapunov approach.

Dynamic Event-Triggered-Based Integral Reinforcement Learning Algorithm for Frequency Control of Microgrid With Stochastic Uncertainty

The intermittency of renewable energy and the uncertainty of load put forward higher robustness requirements for the frequency recovery of microgrid (MG). The energy storage equipment provides an idea for frequency control because of its fast response and strong controllability. In this paper, an on-line adaptive frequency control method is proposed to control the governor and energy storage to realize the frequency recovery of MG under stochastic uncertainty. First, the MG system with external disturbances is constructed as a zero-sum differential game model to obtain a robust optimal control scheme. Then, considering the system parameter uncertainty, an improved integral reinforcement learning (IRL) algorithm is designed, in which the reinforcement signal contains a non-quadratic function to solve the energy storage control constraints. Furthermore, a novel dynamic event-triggered control (DETC) is developed to reduce control update times of energy storage. The dynamic variable in DETC coupled with static trigger includes not only the past triggering information but also the disturbances. DETC has a larger trigger threshold than static event-triggered control (SETC). Meanwhile, the proposed algorithm is implemented by an action-critic network structure, in which the action network of energy storage is updated aperiodically. Finally, simulation results show that the proposed control algorithm can realize the frequency recovery of MG, and it has good robustness compared with other algorithms.

Online Adaptive Current Vector Adjustment for Deep Flux-Weakening Control of IPMSM

This article presents a novel online adaptive and accurate deep flux-weakening (FW) control for interior permanent magnet synchronous machine (IPMSM) drives. Based on the fundamental equations of IPMSM, the proposed online current vector adjustment in conjunction with the adaptive torque angle control loop contributes to improving model-based existing literature in two aspects. First, current vector control is constructed on the proposed flexible current vector recognition algorithm, which can be applied for motoring and generating modes. The optimum reference current vector for the FW region is analytically obtained using electrical speed as a feedback signal instead of using voltage feedback. Second, the proposed adaptive gain for the torque angle control loop is obtained using the small-signal approach to overcome the nonlinear effects in deep FW and improve dynamic performance. The effect of adaptive gain is compared using a bode diagram and an experimental test. Also, the stability of the proposed control scheme under parameter variations is maintained and shown by numerical and experimental analyses. The running of the IPMSM with the proposed control provides a smooth transition between regions, including maximum torque per ampere, FW, and deep FW applicable for EVs application. The experimental results confirm the performance of the proposed control strategy.

Learning-Based Neural Dynamic Surface Predictive Control for MMC

Reinforcement learning technique was developed recently as an interesting topic in designing adaptive optimal controllers. This technique explicitly provided a feasible solution to circumvent the “curse of dimensionality” and requiring a system model inherent in the classical dynamic programming algorithm. By virtue of this property, in our work, by introducing this technique into a predictor-based online adaptive neural dynamic surface predictive control architecture, we concentrate on a novel robust predictive control framework subject to system uncertainties. To be specific, in this presented framework, an adaptive dynamic programming control strategy utilizing a critic neural network point of view is developed to learn the optimal control policy. Our modification is able to facilitate the alleviation of performance deterioration caused by system uncertainties and enable the smooth and fast learning, while keeping the merits of the finite control-set model predictive control. Finally, the interest and applicability of the proposed control methodology are verified by performance evaluation.

An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control With Ensemble Koopman Modeling

Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlike typical data-driven methods that are data-hungry and lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotical stability under some mild conditions. The proposed AKOOC is developed based on an ensemble Koopman state space modeling with full basis functions that combines both linear and nonlinear bases without the need of event detection or switching. An iterative learning method is also developed to exploit model parameters, ensuring the effectiveness and the adaptiveness of the designed control. Simulation studies in the 4-bus (with detailed inner-loop control) MG system and the 34-bus MG system showed improved modeling accuracy and control, verifying the effectiveness of the proposed method subject to various changes of operating conditions even with time delay, measurement noise, and missing measurements.

Behavior Prediction Based Trust Evaluation for Adaptive Consensus of Quadrotors

Without proper treatment, a malfunctional quadrotor may bring severe consequences, e.g., becoming out of control, to the whole swarm. To tackle this problem, we develop a trust evaluations based consensus protocol. Specifically, each quadrotor in the swarm communicates with its connected neighbors, exchanging behavior predictions. By comparing the predicted and the actual behaviors of its neighbor regarding a pre-defined tolerance, each quadrotor assigns trust values to determine potentially legitimate or malfunctional companions. On this basis, an online adaptive controller adjusts each weight in the protocol corresponding to the trust evaluations designed before. We prove that, within proper tolerance, it is almost sure that the legitimate quadrotors can identify the malfunctional quadrotors through trust evaluations and ameliorate their effects on the whole system. Almost surely, the legitimate quadrotors can converge to their center in a finite time. We verify our method through MATLAB and GAZEBO. In particular, with our proposed method, the swarm system discussed in this paper is able to reach position and velocity consensus in the presence of malfunctional quadrotors.

Adaptive Control of Wire-Borne Underactuated Brachiating Robots Using Control Lyapunov and Barrier Functions

Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics. We present and experimentally implement an online adaptive controller for a wire-borne brachiating robot swinging on a vibrating cable. An adaptive function approximation approach is proposed to estimate the unknown dynamics of the flexible cable as an external force applied to the robot. Robust control Lyapunov and barrier functions are designed and incorporated into quadratic programs to synthesize a unified adaptive control framework, which formally guarantees the stability and safety of the brachiating robot in the presence of dynamic uncertainties, actuator constraints, and obstacles in the environment. The stability analysis and derivation of the adaptation law are carried out through a Lyapunov analysis. We demonstrate and validate the proposed control framework using extensive hardware experiments with an underactuated brachiating robot operating on a flexible cable. Simulation results, hardware experiments, and comparisons with a baseline controller show that the proposed quadratic programming-based controller achieves reliable tracking performance and disturbance estimation in the presence of model uncertainties, actuator limits, and safety constraints.

Simplified artificial neural network based online adaptive control scheme for nonlinear systems

Simplified artificial neural network based online adaptive control scheme for nonlinear systems

Application of combined optimization strategy and system of CNC machining parameter

This paper presents an efficient optimization method of combining off-line optimization with on-line adaptive control for CNC (Computerized Numerical Control) machining parameter, and develops the corresponding system, aims to multi-objective optimization and cutting force stability. Firstly, the multi-objective optimization solution of machining parameters is obtained by off-line optimization based on Pareto genetic algorithm and TRIZ theory. Then, on this basis, feed speed is adjusted online based on adaptive control system. Next, current is used as feedback to control cutting force, and cutting force can be kept at a set value by the on-line adjusting. We obtain the relationship between off-line optimization and on-line adaptive control. Finally, we build the combined optimization module and embed the self-developed CNC machining system. Based on the system, the experimental results verify that the optimized process parameters make the processing time reduced by 14 s, and no matter how cutting conditions change, cutting force can be stabilized as soon as possible by adjusting feed speed online for keeping the cutting process stability. From the perspective of building the parameter optimization function module, it provides a new idea for the self-development of CNC machining system.

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller.

The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller's superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure's characteristics change.

Reinforcement learning-based saturated adaptive robust neural-network control of underactuated autonomous underwater vehicles

This paper studies a high-performance intelligent online adaptive robust saturated dynamic surface control framework for underactuated autonomous underwater vehicles by engaging Actor–Critic neural networks in the presence of unmodeled dynamics, uncertainties, ocean disturbances and actuator saturation. The proposed controller is designed based on reinforcement learning method to compensate the effects of unmodeled dynamics and uncertainties more accurately that can lead to a better performance for the controller. The Actor–Critic neural networks are trained real-time by designing online training laws creatively and a new critic function is proposed to supervise the closed-loop performance with the aid of the Critic neural network. The proposed structure for the reinforcement learning method benefits from a model-free algorithm and only relies on the measurable variables of the closed-loop control system. This independency from system dynamics results in considerable low computational burden for the controller and, hence, the proposed control algorithm is efficient computationally. The hyperbolic tangent function is ingeniously used as a saturated stabilizer term to bound the control signals that results in low-amplitude control action and the probability of actuator saturation phenomenon is minimized by learning and compensating the actuator saturation nonlinearity as well. Finally, the stability of the proposed closed-loop system is investigated by the Lyapunov’s direct methodology and simulations along a comparative study with some quantitative assessments certify the contributions of this paper.