Reference Input Research Articles

Design of unmanned aerial vehicle formation keeping controller based on improved consistency algorithm design

This article conducts in-depth research on the problems of large gain convergence oscillation and simultaneous application of translational rotation in traditional consistency algorithms. First, based on the traditional consistent formation algorithm, a virtual long machine is introduced, and a new norm is defined to extend the original consistent algorithm to solve the problem of large gain convergence oscillation in the Linearizability algorithm. Secondly, in response to the issue of the inability to maintain the formation caused by the application of translational rotation in traditional consistent formation processes, the maximum distance is introduced as feedback to the reference controller to modify the reference input trajectory to ensure good tracking of the formation during translational rotation. Finally, a joint semi physical simulation was conducted using the open-source flight control Ardupilot and the open-source simulation platform Airsim to further validate the feasibility of the improved algorithm. The results of this study indicate that the proposed algorithm has significant improvements in solving the problems existing in traditional consistency protocols and has good feasibility in practical applications.

Improvement of tracking performance of SMA-actuated rotary actuator via thermocouple delay compensation using adaptive predictor

This paper investigates the impact of bounded and unknown delay of a J-type thermocouple sensor on controlling a Shape Memory Alloy (SMA) actuated rotary actuator. To compensate for the delay, a nonlinear adaptive predictor is utilized, which predicts the exact temperature of the present moment using the output of the sensor. A robust sliding mode control is then designed to track the reference input, which is the rotation angle of the pulley. In addition to temperature estimation, the inner system identification algorithm of the predictor identifies the heat transfer coefficients of the experimental setup. Experimental tests demonstrate the precise control of the proposed algorithm. The tracking error of the adaptive predictor with sliding mode control is compared to that of a regular sliding mode controller without temperature delay compensation consideration, showing the superior performance of the proposed control algorithm.

Improved direct-parallel active noise control systems for applications with narrowband noises

Rotary machinery generates noises at the fundamental frequency and its harmonics. The direct-parallel form active noise control (ANC) system, which is capable of reducing multiple narrowband noises, uses many harmonically related sinusoids as the reference input. The idea of direct-parallel form ANC is to split these sinusoids into several mutually exclusive sets, so each set has frequencies spaced out as far as possible. This paper proposes an improved direct-parallel form method to separate the reference sinusoidal signals and to enhance the noise reduction performance. Several experiments regarding to the ANC with a muffler model is made to verify the performance of the proposed method.

Advanced controller design based on interval type-2 fuzzy neural network for elementary super-lift Luo converter

The elementary super-lift Luo converter is a third-order DC/DC step-up converter developed using the super-lift method that increases input voltage with low current and voltage ripples and a high voltage gain. On account of the nonlinearity and uncertainty incorporated with voltage, load changes, and switching operation of elementary super-lift Luo (ESLL) converter, it is a challenging task to design an efficient controller. To overcome these problems, this study proposes a new control approach based on interval type-2 fuzzy neural network (IT2FNN) to regulate a DC output current and voltage of ESLL converter. The parameters of IT2FNN were trained offline using a gradient descent algorithm. Mean square error (MSE), one of the most used statistical performance indicators, was used to evaluate the convergence accuracy performance of gradient descent algorithm. The DC output voltage regulation performance of IT2FNN was evaluated via a comparative simulation with interval type-2 fuzzy controller (IT2FC) and proportional + integral (PI) controller in MATLAB/Simulink environment in terms of settling time, average computational time, and recovery time under three different operational conditions: various reference voltages, input voltage, and load. Moreover, the DC output current regulation performance of IT2FNN was evaluated via a comparative simulation with IT2FC and PI controller in MATLAB/Simulink environment in terms of settling time and steady-state error under various reference current changes. The detailed simulation results obtained from comparative simulation validated the effectiveness of IT2FNN.

A Novel Method for a Pursuit–Evasion Game Based on Fuzzy Q-Learning and Model-Predictive Control

This paper explores a pursuit–evasion game (PEG) based on quadrotors by combining fuzzy Q-learning (FQL) and model-predictive control (MPC) algorithms. Initially, the FQL algorithm is employed to perceive, make decisions, and predict the trajectory of the evader. Based on the position and velocity information of both players in the game, the pursuer quadrotor determines its action strategy using the FQL algorithm. Subsequently, a state feedback controller is designed using the MPC algorithm, with reference inputs derived from the FQL algorithm. Within each MPC cycle, the FQL algorithm dynamically provides reference inputs to the MPC, thereby enhancing its robust control and dynamic optimization for the quadrotor. Finally, simulation results verify the effectiveness of the proposed algorithm.

Next-Generation MPPT: neural network-driven optimization for superior solar performance

This paper presents a novel smart Maximum Power Point Tracking (MPPT) method based on a Neural Network Controller (NNC) to enhance solar power efficiency and mitigate significant oscillations. Traditional MPPT techniques, including perturbation and observation, fixed and variable incremental conductance methods, and fractional open circuit voltage, often suffer from slow response to sudden environmental changes (temperature and irradiation), inaccurate tracking, lack of prediction abilities, and complex structures. These drawbacks lead to power loss, reduced effectiveness of photovoltaic (PV) systems and raised broadband oscillations. To address these issues, we propose an intelligent MPPT strategy utilizing NNC. The NNC inputs are the error and derived error (E&ΔE) between the reference and actual inputs, with the duty cycle (D) as the target output. The proposed algorithm demonstrates an efficiency of approximately 0.75% compared to 0.63% for the P&O method. Simulation and experimental validation results confirm the NNC strategy's effectiveness, robustness, and efficiency, providing a thorough system analysis.

Immersion and Invariance-Based Linear Tracking and Regulation Controller for Depth Position of an AUV

This paper addresses the tracking control challenge in the diving motion system of a specific class of autonomous underwater vehicles (AUVs) characterized by a torpedo-like shape. A decoupled and reduced-order three degrees-of-freedom linearized diving motion model is employed for depth position control. A control law is synthesized using the immersion and invariance (I&I) technique to achieve the control objectives. The primary aim is to attain tracking by immersing a stable, lower-order target (second-order) dynamic system into a three-dimensional manifold, upon which the closed-loop system evolves. We address the regulation problem as a specialized instance of the tracking problem, with the reference input set as a predetermined known depth that requires regulation. The efficacy of the proposed control law is evaluated through simulation studies involving various scenarios. Robustness tests are conducted to assess the control law’s performance under modeling uncertainties and underwater disturbances. The computer simulation employs an AUV named MAYA, utilizing experimentally validated diving motion parameters. A comparative analysis is performed between the proposed control law and other benchmark controllers to gauge its performance. Additionally, the effectiveness of the proposed control law is confirmed by validating its application to the nonlinear model of the diving motion system.

An improved two‐degree‐of‐freedom ADRC for asynchronous motor vector system

AbstractThis paper proposes an improved two‐degree‐of‐freedom active disturbance rejection controller for the coupling problem of asynchronous motor vector system. To simplify the analysis process and accommodate observers of different types, a unified expression based on different controllers for the system output is developed. The closed‐loop transfer function generated by the reference input and load disturbance is given. For the coupling problem of motor output speed and immunity, the structure of a higher‐order extended state observer is reconstructed. The extended state observer estimates both the output speed and the total system disturbance, which serve as feedback and feed‐forward compensation quantities. Compared to the PI controller and traditional active disturbance rejection controller, the proposed controller achieves decoupling of output response speed and immunity, simplifies the process of parameter tuning. Finally, simulation and experiment results verify the feasibility and effectiveness of the algorithm in this paper.

A deterministic design approach of tilt integral derivative controller for integer and fractional-order system with time delay

A tilt integral derivative (TID) controller modifies the proportional integral derivative (PID) controller in the fractional domain. It converts the proportional gain as a function of frequency and is thereby capable of achieving optimal system response. The usual practice for the parameter estimation of the TID controller is by minimization of the error-based objective functions using optimization techniques. Although precise results can be achieved, these nature-inspired algorithms are stochastic and hence produce different solutions during different iterations. Therefore, a comparative statistical study is usually necessary to validate the best possible result. This study shows a deterministic analytical procedure for the paramssseter estimation of TID controllers. The magnitude and phase angle criteria, along with the frequency-domain loop shaping specifications, are used for the explicit evaluation of the TID parameters. Because of its model-independent nature, this tuning strategy can be used for a variety of integral and nonintegral order systems with different plant structures. In this article, the authenticity of the applied procedure is demonstrated through suitable numerical examples. The complexity of the design problem is enhanced by using it for both integer and non-integer (fractional) order plus time-delay systems. Further, the robustness of the control system in the presence of a TID controller was examined under the influence of external parameters and input reference changes. Simulation studies validate the supremacy of TID controllers over PID controllers in terms of reference tracking and disturbance rejection capabilities.

Improving stability and performance in IoT-Driven networked control systems

With the widespread adoption and benefits of the Internet of Things (IoT), this technology has expanded its reach into the realm of automation and control, giving rise to a branch known as industrial networks. The use of IoT technology is on the rise due to its daily increasing advantages and ease of use. However, it has also introduced new challenges in control systems, prompting researchers to conduct numerous studies to address these challenges and propose innovative solutions. One of these challenges, for example, is the limited bandwidth of the network, which leads to challenging issues such as variable random time delays and packet loss. In light of these challenges, using networked control systems instead of many traditional control methods can significantly improve the performance and stability of closed-loop systems. In this context, this paper aims to enhance system tolerance and reliability, along with permanent state error correction, by presenting methods for designing networked controllers that are influenced by random delays. To achieve this goal, we first propose a predictive control model with reduced prediction horizons, assuming constant delays in the input. This controller not only ensures stability but also tracks the reference input. Simulation results demonstrate that this controller can tolerate some variations in delay despite being designed for constant delays. However, it is not robust against significant variations in delay inherent to network environments. Therefore, in the second approach, a controller based on the Lyapunov function and linear matrix inequalities is designed for a system with time-varying input delays. This controller, in addition to stabilizing the system, tracks the reference input by minimizing the state error.

An improved kT/C noise cancellation technique with presampling for high‐speed SAR ADCs

AbstractThe kT/C noise cancellation technique can effectively reduce the digital‐to‐analog converter (DAC) size for successive approximation register ADCs and thus relax the burden for input drivers and reference buffers. However, the prior kT/C noise cancellation technique suffers from a hard trade‐off between the noise, amplifier bandwidth and linearity. Here, an improved kT/C noise cancellation technique is proposed to break the trade‐off. It uses presampling to hold the input signal unchanged during the noise cancelling phase, leading to significantly relaxed requirements on the bandwidth and linearity of the amplifier. The proposed technique is verified in a 14‐bit 200 MS/s SAR analog‐to‐digital converter (ADC) with a DAC size of 128 fF. Simulation results show that this work achieves 75.6 dB signal to noise and distortion ratio and consumes 1.56 mW power.

Exponential output tracking via a saturated RLC for a class of nonlinear systems under PSD of periodic references

This paper considers the same class of zero-relative-degree nonlinear systems for which a saturated Repetitive Learning Control (RLC) has been recently shown to ensure exponential – and not just asymptotical – convergence to zero of both the output and the input tracking errors in the case of periodic output reference signals with a known single period. The explicit role of the nonlinear unstructured uncertainties is here investigated within the more general scenario in which the output reference signal is multi-periodic. Special emphasis is provided to the generation of the input reference whose effect has to be exponentially nullified by the RLC. In particular, the following question is answered. Restrict the design to the sub-class of periodic output reference signals that admit a Periodic Signal Decomposition (PSD) (namely, the ones that can be written as a finite sum of periodic functions) with pairwise-commensurable periods (so that the knowledge of the multiple periods characterizing the input reference is preserved once the common multiple among the single periods is additionally included). Then, which robustness and convergence properties can be still achieved by the same output-feedback (definitely saturated) RLC, once it is intuitively generalized to transiently include an additional bank of learning estimation schemes corresponding to the single periods? Interestingly, the theoretical tools of this paper can be also used to successfully address identifiability and convergence issues regarding the identification of periodic components of general multi-periodic signals.

Dual-reference adaptive feedforward control of a magnetostrictive device for synchronous micropositioning and microvibration isolation

In this paper, we propose a novel dual-reference adaptive feedforward controller to realize synchronous micropositioning and microvibration isolation on a magnetostrictive device. The scheme of the proposed adaptive feedforward controller and its differences from the traditional single-reference controller are briefly introduced. The desired trajectory and available external disturbance as two input references are utilized to develop the proposed controller. The dynamics compensator is constructed based on the modified filtered-x normalized least mean square (MFxNLMS) algorithm with the discrete cosine transform (DCT) technique. The asymmetric hysteresis compensator is modeled via the arctangent-polynomial modified Prandtl–Ishlinskii (APMPI) model. The experimental setup is built, and the closed-loop control experiment is carried out according to the designed experimental process. Comparison of experimental results show that the proposed dual-reference DCT-MFxNLMS controller behaves better than the single-reference DCT-MFxNLMS and proportional-derivative-derivative (PID) integrated controller for the synchronous micropositioning and vibration isolation cases. Moreover, by the dual-reference DCT-MFxNLMS controller, the vibration isolation ratio enhances and the tracking bandwidth increases within the interest of frequency bandwidth, compared with those of open-loop system, respectively.

Optimized Discrete Nonlinear Control of Alternating Current Three-Phase Motors via an Industrial Variable Frequency Drive

This article presents a suboptimal nonlinear control strategy to improve the dynamics of a three-phase alternating current (AC) motor. Using dynamic programming, the calculation of the Bellman function is avoided by determining a suboptimal control sequence that locally minimizes a quadratic performance index at each step. The motor’s fixed-frame nonlinear mathematical model controls the stator currents, rotor magnetic fluxes, and rotor angular speed by applying voltages to the stator. Experimental tests are conducted using a Delta VFD007EL11A variable frequency drive (VFD), demonstrating improved motor state behavior and performance compared to an optimal proportional integral (PI) control and a fixed reference input in the VFD. The experiments include set point changes and a comparative analysis of the energy consumption between both controllers considering two cases: free and with load on the motor shaft.

Adaptive dynamic‐surface repetitive control for uncertain nonlinear systems with mismatched disturbances and input delay

AbstractBased on additive state decomposition technique, this article presents an adaptive dynamic‐surface repetitive control method for a class of uncertain nonlinear systems with mismatched disturbances and input delay. First, the original uncertain nonlinear system is decomposed into a linear time invariant (LTI) primary system responsible for the periodic signal tracing and rejection task, and a nonlinear secondary system with input time delay responsible for the robust stabilization task. A modified repetitive controller with a small correction to the delay constant is then independently designed for the LTI primary system, while an adaptive dynamic surface controller is designed for the nonlinear secondary system. Stability analysis is performed for each subsystem and the design procedure of the whole system is presented in detail. Finally, comparative simulations with other repetitive control and dynamic surface control methods show that the presented method not only relaxes the constraints on the reference inputs and exogenous disturbances, but also ensures that the system has better disturbance rejection and periodic‐signal tracking performance in both transient and steady states. As a result, this method broadens the application of both repetitive control and dynamic surface control.

Practical one-shot data-driven design of fractional-order PID controller: Fictitious reference signal approach

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.

Magic Furniture: Design Paradigm of Multi-Function Assembly.

Assembly-based furniture with movable parts enables shape and structure reconfiguration, thus supporting multiple functions. Although a few attempts have been made for facilitating the creation of multi-function objects, designing such a multi-function assembly with the existing solutions often requires high imagination of designers. We develop the Magic Furniture system for users to easily create such designs simply given multiple cross-category objects. Our system automatically leverages the given objects as references to generate a 3D model with movable boards driven by back-and-forth movement mechanisms. By controlling the states of these mechanisms, a designed multi-function furniture object can be reconfigured to approximate the shapes and functions of the given objects. To ensure the designed furniture easy to transform between different functions, we perform an optimization algorithm to choose a proper number of movable boards and determine their shapes and sizes, following a set of design guidelines. We demonstrate the effectiveness of our system through various multi-function furniture designed with different sets of reference inputs and various movement constraints. We also evaluate the design results through several experiments including comparative and user studies.

A Class-AB LDO regulator with high stability suitable for multiple input reference voltages

A Class-AB LDO regulator with high stability suitable for multiple input reference voltages

Precision motion control of rotary flexible link manipulators using polynomial input trajectories and feedback control

This paper aims to develop a high-precision controller for a flexible link manipulator using polynomial reference, feedforward input, and feedback control. The control objective is to move the tip of the flexible link to a target position as quickly as possible without exciting its resonant modes. Upon a frequency response-based system identification, a frequency-domain feedback controller is designed for reference tracking. The controller includes a proportional-integral controller, a lead filter, and a set of notch filters to control the rigid body and the flexible link dynamics. A set of polynomial reference and feedforward input trajectories are then designed to improve the tracking performance and minimize residual vibrations. The conventional polynomial reference is generated based on the rigid body motion of the system. A revised polynomial reference generation method is then developed by adding flexible motions to the conventional reference. Simulations and experimental tests indicate significant improvements in the performance of the control system using the revised polynomial trajectories.

3D Hybrid modeling for the ultrasonic phased array inspection of porosity in heavy plates: Simulation and experimental validation

Quantitative NDT modeling and simulation tools are important in design and fabrication of ultrasonic phased array probes with optimum characteristics and reduce the number of experimental efforts and development time. In order to achieve high accuracy (Amplitude deviation < 1 dB) with a low computational time in the simulation, the modeling tools require not only the high-frequency approximation of the ultrasonic wave propagation but also the high-precision electro-acoustic coupling model. This paper describes a computationally efficient 3D time-dependent hybrid model for the simulation of ultrasonic phased array inspection of porosity in heavy plates. This method combines the results of both 3D finite element analysis (PZFLex-FEA) and the semi-analytical ultrasonic simulation “Ray-Tracing”. As a first step, we demonstrate the FEA modeling of an ultrasonic phased array probe, which includes the full 1-3 piezocomposite design with the arrangement of array elements, conducting electrodes, multi-phase backing material, acoustical matching layer and electrical circuit of a 50 Ohm coaxial cable. In the current FEA simulation, we considered the acoustical and electrical cross-talk between the adjacent elements of an ultrasonic array sensor. In the second step, the FEA-simulated ultrasonic pulse-echo signal and its frequency spectrum of an array element in the farfield region are further used as the reference input signal in the CIVA-UT software to simulate the imaging of porosity in heavy plates, quantitatively. The design of the 3D wedge-geometry of the application specific array sensor is modeled and optimized using the CIVA raytracing and multi-phase backing models. The real excitation pulse of the industrial ultrasonic testing equipment “ROMIS (ROSEN Modular Inspection System)” is also included in the simulation to achieve high accuracy in the current hybrid modeling. The dependency of the detectability of the porosity on the frequency of the array sensor and the active element group so-called “Virtual Probe” are quantitatively analyzed. Finally, the 3D hybrid model simulated Time Corrected Gain (TCG) curves of the porosity like defects in the reference steel plates are compared with the real experiments and a good quantitative agreement is achieved.