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In this paper, a novel Virtual State-feedback Reference Feedback Tuning (VSFRT) and Approximate Iterative Value Iteration Reinforcement Learning (AI-VIRL) are applied for learning linear reference model output (LRMO) tracking control of observable systems with unknown dynamics. For the observable system, a new state representation in terms of input/output (IO) data is derived. Consequently, the Virtual State Feedback Tuning (VRFT)-based solution is redefined to accommodate virtual state feedback control, leading to an original stability-certified Virtual State-Feedback Reference Tuning (VSFRT) concept. Both VSFRT and AI-VIRL use neural networks controllers. We find that AI-VIRL is significantly more computationally demanding and more sensitive to the exploration settings, while leading to inferior LRMO tracking performance when compared to VSFRT. It is not helped either by transfer learning the VSFRT control as initialization for AI-VIRL. State dimensionality reduction using machine learning techniques such as principal component analysis and autoencoders does not improve on the best learned tracking performance however it trades off the learning complexity. Surprisingly, unlike AI-VIRL, the VSFRT control is one-shot (non-iterative) and learns stabilizing controllers even in poorly, open-loop explored environments, proving to be superior in learning LRMO tracking control. Validation on two nonlinear coupled multivariable complex systems serves as a comprehensive case study.

This paper proposes a neural network (NN)-based control scheme in an Adaptive Actor-Critic (AAC) learning framework designed for output reference model tracking, as a representative deep-learning application. The control learning scheme is model-free with respect to the process model. AAC designs usually require an initial controller to start the learning process; however, systematic guidelines for choosing the initial controller are not offered in the literature, especially in a model-free manner. Virtual Reference Feedback Tuning (VRFT) is proposed for obtaining an initially stabilizing NN nonlinear state-feedback controller, designed from input-state-output data collected from the process in open-loop setting. The solution offers systematic design guidelines for initial controller design. The resulting suboptimal state-feedback controller is next improved under the AAC learning framework by online adaptation of a critic NN and a controller NN. The mixed VRFT-AAC approach is validated on a multi-input multi-output nonlinear constrained coupled vertical two-tank system. Discussions on the control system behavior are offered together with comparisons with similar approaches.

•Adaptive binary controller via output feedback for exact tracking of multivariable uncertain plants with nonuniform arbitrary relative degrees.•Multivariable generalization of the global finite-time differentiators with dynamic gains and higher-order sliding modes.•Global asymptotic stability of the closed-loop system and ultimate exponential convergence to small residual sets are guaranteed.•Fast transient responses, improved tracking precision and chattering-free control signals.•Engineering application to inertially stabilized platforms with numerical results based on data acquired from experiments in real-world conditions. This paper presents an extension of the Binary Model Reference Adaptive Control (BMRAC) for uncertain multivariable (square) systems with non-uniform arbitrary relative degrees using only output feedback and its application to inertially stabilized platforms using a two degree of freedom gimbal as actuator. The BMRAC is a robust adaptive strategy with good transient performance, thus useful for uncertain systems, and the multivariable framework is suitable to deal with mechanical unbalances. Using a newly proposed differentiator with dynamic gains based on higher-order sliding mode, the proposed controller achieves global and exact tracking. To illustrate the effective of the proposed solution, simulations are presented using real-word data obtained from an instrumented vehicle in an irregular ground.

This study presents a control design of an angular position for the exoskeleton knee assistance system based on a model reference adaptive control (MRAC) strategy. Three schemes of the MRAC design have been proposed: the classical MRAC, MRAC with an adaptive disturbance observer, and MRAC with a nonlinear observer. The stability analysis for each scheme has been conducted and developed based on the Lyapunov theorem to prove the uniform ultimate bound of tracking and estimation errors. In addition, the adaptive laws have been developed for the proposed schemes according to the stability analysis. The effectiveness of the proposed state and output feedback controllers has been verified via computer simulation. The results based on numerical simulation have shown that the MRAC with a nonlinear observer could give better robustness characteristics and better performance in terms of tracking and estimation errors as compared to the other controllers.

This paper puts forward a novel output feedback model reference adaptive control (MRAC) scheme for solving an adaptive output tracking problem. The proposed control scheme only needs a scalar function to be updated online, which decreases the system adaptation complexity, compared to the existing MRAC schemes. Furthermore, the closed-loop signal boundedness and asymptotic output tracking are guaranteed with the proposed MRAC scheme. A simulation study is carried out to verify the effectiveness of the established approach.

This paper addresses the angular tracking control for an electromagnetic MEMS micromirror. The problem is formulated in the output regulation framework for output feedback systems. An extended internal model-based output feedback controller is developed, which can achieve excellent angular tracking and allow large parameter uncertainties ranging within any compact set. In addition, the simplified robust controller features itself in the aspect of having a very simple tuning procedure with only one adjusting parameter. The control scheme is based on sensing the angular position and incorporates only tracking error to adjust the desired output angular of micromirror. As a result, it is independent of the angular velocity and removes the noise amplification problem of the first-order backward difference method in state-feedback design. It also relaxes the extra measurement hardware, which reduces the complexity of tracking controller. In general, the proposed control scheme is beneficial for the integrated packaging of micromirror devices. Experimental validation is provided to verify the effectiveness of the proposed controller, which is programmed by LabVIEW and implemented with a field programmable gate array platform. The sinusoidal waves with different frequencies are utilized as the reference signals. It is shown that the controller offers improved steady-state performance over the existing schemes.

The application of deoiling hydrocyclone systems as the downstream of three-phase gravity separator (TPGS) systems is one of the most commonly deployed produced water treatment processes in offshore oil and gas production. Due to the compact system’s complexity and tailor-made features, it is always challenging to develop some optimally coordinated control solution for the coupled hydrocyclone and TPGS systems. It is obvious that coordinated control can better fulfill legislative discharge regulation by robustly maintaining high separation efficiencies. This paper presents a new control solution for a set of integrated hydrocyclone and TPGS systems by applying an improved multi-variable model reference adaptive control (MV-MRAC) approach with the aim of achieving both asymptotic output tracking and unknown disturbance rejection. A robust MV-MRAC controller design is proposed based on a control parameterization derived from a factorization of a high-frequency gain matrix Kp=LDS as a product of three matrices, where L represents unity lower triangular, D=sign(D) represents diagonal, and S represents positive definite, and a teaching–learning-based optimization (TLBO) algorithm for optimizing the adaption rates. The developed solution is analyzed and compared with a commonly deployed PI control solution on a model that is derived from a lab-scale produced water treatment process. This simulation study demonstrates the promising potential of the proposed control solution compared with the currently deployed PI control solution.

This work suggests a solution for the output reference model (ORM) tracking control problem, based on approximate dynamic programming. General nonlinear systems are included in a control system (CS) and subjected to state feedback. By linear ORM selection, indirect CS feedback linearization is obtained, leading to favorable linear behavior of the CS. The Value Iteration (VI) algorithm ensures model-free nonlinear state feedback controller learning, without relying on the process dynamics. From linear to nonlinear parameterizations, a reliable approximate VI implementation in continuous state-action spaces depends on several key parameters such as problem dimension, exploration of the state-action space, the state-transitions dataset size, and a suitable selection of the function approximators. Herein, we find that, given a transition sample dataset and a general linear parameterization of the Q-function, the ORM tracking performance obtained with an approximate VI scheme can reach the performance level of a more general implementation using neural networks (NNs). Although the NN-based implementation takes more time to learn due to its higher complexity (more parameters), it is less sensitive to exploration settings, number of transition samples, and to the selected hyper-parameters, hence it is recommending as the de facto practical implementation. Contributions of this work include the following: VI convergence is guaranteed under general function approximators; a case study for a low-order linear system in order to generalize the more complex ORM tracking validation on a real-world nonlinear multivariable aerodynamic process; comparisons with an offline deep deterministic policy gradient solution; implementation details and further discussions on the obtained results.

In DC microgrids, optimizing the hybrid energy storage system (HESS) current control to meet the power requirements of the load is generally a difficult and challenging task. This is because the HESS always operates under various load conditions, which are influenced by measurement disturbances and parameter uncertainties. Therefore, in this paper, we propose the H∞ state feedback control based on the reference modulation to improve the current tracking errors of the battery (Bat) and supercapacitor (SC) in the HESS for power tracking performance. Without altering the system control signal, the reference modulation technique combines the feedforward channel and output feedback signal directly to modulate the required currents of the Bat and SC derived from the required load power. The H∞ state feedback control based on the required Bat and SC currents modulated by the reference modulation technique is proposed to improve the current tracking errors under the influence of measurement disturbances and parameter uncertainties without a disturbance observer. The ability of the reference modulation technique to attenuate the disturbance without the use of a disturbance observer is one advantage for improving transient performance. The improvement of the HESS’s power tracking performance in DC microgrids is confirmed by study results presented under the influence of measurement disturbances for nominal parameters and parameter uncertainties.

In this paper we present the design and implementation of autopilot tracking control law for Micro Aerial Vehicle using the second order sliding mode approach. The inner loop attitude tracking control design is carried out using output feedback based second order sliding mode technique, to ensure finite time convergence of the tracking error dynamics. While addressing tracking control of a time varying reference signal, it is important to investigate the stability characteristics of the internal dynamics to ensure perfect tracking. This paper mainly addresses the output tracking control problem for a MAV and investigate the stability characteristics of the longitudinal zero dynamics during tracking. We have proposed a stability proof based on Lyapunov theory to analyze the stability of the MAV longitudinal zero dynamics during tracking. A nonlinear aircraft model obtained using aerodynamic derivatives of The Blackkite 300 mm wingspan fixed MAV is used for both control design and as well as to verify its performance against the classical control methods. Extensive hardware in-loop simulation results of the proposed control algorithm implemented on the commercially available PX4 based Pixhawk autopilot board are also presented here.