Hybrid Adaptive Control Research Articles

Adaptive hybrid control for the formed morphology in powder-based laser metal deposition

Adaptive hybrid control for the formed morphology in powder-based laser metal deposition

Hybrid Adaptive Event-Triggered Consensus Control with Intermittent Communication and Control Updating

Hybrid Adaptive Event-Triggered Consensus Control with Intermittent Communication and Control Updating

Impedance Learning-Based Hybrid Adaptive Control of Upper Limb Rehabilitation Robots

This paper presents a hybrid adaptive control strategy for upper limb rehabilitation robots using impedance learning. The hybrid adaptation consists of a differential updating mechanism for the estimation of robotic modeling uncertainties and periodic adaptations for the online learning of time-varying impedance. The proposed hybrid adaptive controller guarantees asymptotical control stability and achieves variable impedance regulation for robots without interaction force measurements. According to Lyapunov’s theory, we proved that the proposed impedance learning controller guarantees the convergence of tracking errors and ensures the boundedness of the estimation errors of robotic uncertainties and impedance profiles. Simulations and experiments conducted on a parallel robot validated the effectiveness and the superiority of the proposed impedance learning controller in robot-assisted rehabilitation. The proposed hybrid adaptive control has potential applications in rehabilitation, exoskeletons, and some other repetitive interactive tasks.

Adaptive Formation Tracking Control of Multiple Vertical Takeoff and Landing UAVs With Bearing-Only Measurements.

This article studies the leader-following formation tracking control problem of multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) subject to uncertain parameters, in which the target formation configuration is defined by using the interneighbors' bearing vectors. An adaptive formation control algorithm with bearing-only measurements is proposed under a hierarchical control framework. More specifically, a saturated adaptive distributed control force is developed in the position loop with bearing-only measurements. Since the bearing vectors with respect to the neighbors can be directly measured by low-cost onboard cameras, the proposed formation algorithm can be implemented without interagent communication. Subsequently, in the attitude loop, an adaptive hybrid control scheme via two modified Rodrigues parameters (MRPs) sets is proposed, which achieves the command attitude tracking globally and also avoids the unwinding problem of MRPs. Based on the Lyapunov stability analysis and hybrid theory, we prove that the overall closed-loop error system is globally asymptotically stable. Finally, we provide a numerical example to demonstrate the effectiveness of the proposed control algorithm.

Improved control of permanent magnet synchronous motors using adaptive nonlinear control with Deadbeat Observer

Permanent Magnet Synchronous Motor (PMSM) control optimization is crucial for a variety of applications, including electric vehicles, industrial automation, and renewable energy systems (RESs). However, traditional control methods often struggle to adapt to the dynamic and non-linear nature of PMSMs, leading to sub-optimal performance. To address these challenges, this paper proposes a hybrid adaptive nonlinear control strategy with a deadbeat observer (DO) designed to improve the performance of PMSMs. This approach aims to improve the accuracy and robustness of the control while taking into account the system parameters variations and disturbances. Simulation tests comparing the proposed method with an adaptive nonlinear control (ANLC) are presented, highlighting its superior effectiveness in controlling PMSMs under varying load conditions and speed fluctuations. The proposed strategy achieves a maximum relative speed error around of 6% at 0.4 s under gradually varying load torque disturbances, which is better than the ANLC with 8%. Furthermore, under large speed variations, the suggested method maintains a maximum relative speed error of 0.82% at 0.85 s. Furthermore, the robustness assessment under system parameters variations, stator resistance, and inductance, shows extraordinary performances of the proposed scheme. These results highlight the effectiveness and robustness of the proposed strategy in achieving precise PMSM control while dynamically adapting to changing conditions. This research underscores the potential of our approach to advance key technologies, including electric vehicles, industrial automation, and renewable energy systems. By optimizing PMSM control, this strategy contributes to increased efficiency, reliability, and adaptability, facilitating broader adoption of electric propulsion systems and sustainable energy solutions.

Active Equalization of Lithium Battery Pack with Adaptive Control based on DC Energy Conversion Circuit

Background:: How to solve the inconsistency of battery pack is a key point to ensure reliable operation of electric vehicles. Battery equalization is an effective measure to address the inconsistency. Passive equalization method has poor efficiency and thermal management problems. Average voltage equalization method is only suitable for situations where there is a significant voltage difference between batteries. The SOC-based equalization method is relatively difficult and may inevitably lead to the accumulation of errors during the process. Objective:: In order to avoid the disadvantages of traditional control methods, a new control method is proposed to improve the accuracy and self-adaptation of active equalization, which is easy to be realized without online calculation. Methods:: Cascaded bidirectional Buck-boost circuit is adopted as the novel equalization topology. Based on fuzzy PID theory, an adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed in this paper. Parameter design of the fuzzy PID controller is carried out. A battery equalization system based on cascaded bidirectional Buck-boost circuit is designed and developed. Experimental verification is conducted on relevant hardware platforms. Results:: An adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed. Compared to passive equalization, this proposed method provides high efficiency. Regarding traditional voltage control, the method improves control reliability and flexibility. Compared to the average voltage equalization method, the approach needs less convergence time. Moreover, the control method is much easier to realize than the SOC-based equalization method. Conclusion:: By using the presented adaptive control based on DC energy conversion circuit, the degree of self-adaptation of the equalization process has been obtained as higher and the inconsistency as smaller.

Proposed Fault Detection Algorithm with Optimized Hybrid Speed Control

The Brushless DC (BLDC) motor is a common choice for industrial applications, particularly in the automotive sector, owing to its high efficiency and robust capabilities. To detect the position of the motor rotor, hall-effect sensors can be used, but these sensors may prevent the system from operating if they fail. Consequently, fault-tolerant control (FTC) has been proposed in several studies to ensure continuity of operation in the event of sensor failure. This paper proposes an innovative method of fault detection in the hall effect sensor for a BLDC motor using combinatorial functions. This paper proposes an innovative method of hall-effect sensor fault detection for a BLDC motor using combinatorial functions. For the speed control of the BLDC under study, a hybrid adaptive neuro-fuzzy inference control (ANFIS) is implemented. In addition, the FTC signal reconstruction technique adopted has been improved to achieve motor start-up despite a fault in one of the sensors, thanks to well-defined fault detection algorithms. Simulation results are presented for each sensor failure case to test the effectiveness of the method used.

A Self-Adaptive Inertia Hybrid Control of a Fully Constrained Cable-Driven Parallel Robot

A Cable-Driven Parallel Robot (CDPR) is a special type of parallel robot actuated by elastic cables instead of rigid links, which brings uncertainties of model nonlinearities and structural friction depending on the payload variation. A precise motion with minimum control effort against an unknown payload has been a challenging task. To conquer it, we propose an adaptive hybrid control approach for the fully-constrained CDPR with an unknown payload in this paper. A Cartesian space position control and a joint space cable force control are designed through inertia estimation to encounter the effect of the uncertain payload. And then, an artificial neural network (ANN) is employed to estimate the end-effector force in Cartesian space for precise force control and consecutive uncertainties compensation. The stability of the proposed controller is proven based on the Lyapunov method and the efficacy is verified by the experiments. As for feasibility results, the proposed adaptive hybrid control-based cable force estimation was evaluated on the Mini CDPR. Compared to the traditional control scheme, the tracking error was decreased approximately 56% and the utilized tension was significantly reduced for the given trajectory tracking test. Furthermore, the investigation of free movements of the helix trajectory and pick-and-place tasks showed that the proposed hybrid adaptive control can control the position and cable force altogether while being able to estimate the inertia of the end-effector. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The study was motivated by the control problem of adjusting cable tensions of a cable-driven parallel robot (CDPR) against varying payloads at the end-effector. Several advanced control approaches were proposed for but limited to CDPR model parameter estimation and did not consider varying payload. This paper suggests a practical methodology capable of both self-adaptation to unknown payload inertia and consequently improving tracking control performance for a CDPR. In this paper, we derived an adaptive control model and proved stability to ensure safety. In order to encounter unmodeled uncertainties such as pulley friction and cable characteristics, we utilized an artificial neural network (ANN) trained by experiments. The experimental results could confirm that the proposed scheme can estimate the payload inertia and generate minimized cable tension while maintaining good trajectory tracking performance. In future research, we will show more practical validation for a scale-up CDPR with heavy objects toward logistics or pick-and-place application in practice.

Cluster Synchronization of Fractional-Order Complex Community Networks in Quaternion-Valued Field

Without separating the fractional-order quaternionvalued complex networks (FOQVCNs) into four real-valued systems, cluster synchronization is investigated in this paper. First, the FOQVCNs are divided into several clusters, and the dynamic behaviors of the clusters are different. For the case of different dynamics behaviors of the network nodes, hybrid adaptive control is proposed, which means that coupling strength and feedback control gain are simultaneously regulated with the evolution of states. Particularly, some new analytical approaches for fractional-order adaptive schemes, including the method of contradiction, the Mean Value Theorem are developed to establish synchronization criteria of the network system. Furthermore, asymptotical synchronization of FOQVCNs with identical nodes is also studied by adaptive feedback controller and adjusting coupling strength as a special case. At last, two fractional-order quaternion-valued complex community networks are divided to synchronize to the desired trajectory, respectively and numerical simulations are provided to illustrate the validity of theoretical results.

Adaptive neural dynamic-based hybrid control strategy for stable retrieval of tethered satellite systems

Adaptive neural dynamic-based hybrid control strategy for stable retrieval of tethered satellite systems

Mittag-Leffler projective synchronization of uncertain fractional-order fuzzy complex valued neural networks with distributed and time-varying delays

&lt;p&gt;To study the Mittag-Leffler projective synchronization (MLPS) problem of fractional-order fuzzy neural networks (FOFNNs), in this work we introduced the FOFNNs model. On this basis, we discussed the MLPS of uncertain fractional-order fuzzy complex valued neural networks (FOFCVNNs) with distributed and time-varying delays. Utilizing Banach contraction mapping principle, we proved the existence and uniqueness of the model solution. Moreover, employing the construction of a new hybrid controller, an adaptive hybrid controller, and the fractional-order Razumikhin theorem, algebraic criteria was obtained for implementing MLPS. The algebraic inequality criterion obtained in this article improves and extends the previously published results on MLPS, making it easy to prove and greatly reducing the computational complexity. Finally, different Caputo derivatives of different orders were given, and four numerical examples were provided to fully verify the accuracy of the modified criterion.&lt;/p&gt;

A COMPARATIVE ANALYSIS OF LOWER LIMB EXOSKELETON GAIT CONTROL SYSTEMS

The complexities associated with lower limb exoskeleton control systems stem from the dynamic and non-linear nature of human movement. Gait control, in particular, re-quires intricate coordination of joints and muscles, demanding precise adjustments to ac-commodate variations in walking speed, terrain, and user preferences. Additionally, the diverse physical characteristics among users, such as weight, height, and muscle develop-ment, introduce parametric uncertainties that amplify the challenge of designing control systems that can adapt seamlessly to individual differences. Addressing these complexities involves an advanced and multi-level control approach. In the pursuit of effective control strategies, a non-linear mathematical model for a single-leg lower limb exoskeleton is de-rived, considering mechanical and electromechanical components. Тhe gait control sys-tems of a lower limb exoskeleton were investigated, emphasizing the development of vari-ous control strategies, including proportional derivative (PD), PD with gain scheduling, hy-brid adaptive, and adaptive controllers with gain scheduling. The article ends with a com-prehensive comparative analysis of the developed controllers, presenting the integral abso-lute errors (IAE) during exoskeleton walking in both load and no-load conditions to evalu-ate their productivity, efficiency, and adaptability. Extensive testing was conducted using a physical test model representing the exoskeleton of the lower limbs. During a series of ex-periments, the functionality and operability of each control system were evaluated in vari-ous scenarios. The results of these tests give valuable information about the strengths and weaknesses of various control strategies, contributing to the development of lower limb exoskeleton technology.

A Hybrid Adaptive Controller for Soft Robot Interchangeability

A Hybrid Adaptive Controller for Soft Robot Interchangeability

An Improved Power Quality in a Renewable Energy-based Microgrid System Using Adaptive Hybrid UPQC Control Strategy

Due to its ability to integrate renewable energy, improve energy efficiency, and fortify the power system's resilience, microgrids are widely used as regional energy systems. But these advantages do have certain drawbacks in terms of control and Power Quality (PQ). In order to guarantee the proper operation of equipment that is connected and the system's general health, PQ is crucial in microgrids. For microgrids to operate successfully, governing stratagems in concurrence with front-line power electronics devices bid a firm context to knob PQ challenges like Voltage Sag and Swell, Source/Grid current harmonics, Voltage imbalances, Active and Reactive power compensation etc. Multifunctional systems that can integrate clean energy generation and as well as enhance PQ are necessary to meet the demands of complex loads that have the capability of operating in the situation of an unavailable network grid and power electronics devices, which necessitates the need for clean energy. Hence, this paper provides enactment of an adaptive hybrid control strategy based on an Adaptive Leaky Least Mean Square (AL_LMS) algorithm combined with Fuzzy Logic (FL) to the Unified Power Quality Conditioner (UPQC) in a Solar-PV energy based Microgrid system in improving microgrid power quality. The concert of UPQC is assessed by the conventional PI controller and Fuzzy Logic control with MATLAB/SIMULINK software platform, simulation results are conversed with proportionate studies in improving the PQ by minimizing Total Harmonic Distortion (THD), the Voltage Sag &amp; Swell and the result comparison shows the effectiveness of the Fuzzy Logic in coordination with the AL_LMS algorithm resulting improved power quality within the IEEE Power Quality-519 Standards.

Design of a Hybrid Adaptive Controller for Series Elastic Actuators of Robots

Design of a Hybrid Adaptive Controller for Series Elastic Actuators of Robots

Attitude and Vibration Control of a Flexible Spacecraft using Hybrid Adaptive Super-Twisting Non-singular Terminal Sliding Mode Control

Attitude and Vibration Control of a Flexible Spacecraft using Hybrid Adaptive Super-Twisting Non-singular Terminal Sliding Mode Control

Adaptive hybrid control design for comparative clinical trials with historical control data

Abstract We propose an adaptive hybrid control causal (AHCC) design to leverage historical control data to reduce the sample size demanded by standard randomised controlled trials (RCT). Under the causal inference framework, we define the causal estimand of the average treatment effect and derive the corresponding estimator based on the trial data and historical control data. The AHCC design takes a multistage or group sequential approach. The number of patients randomised to the concurrent control is adaptively adjusted based on the amount of information borrowed from the historical control data. At each stage, based on the interim data, the contribution of the historical control data, quantified by the effective sample size, is updated and used to determine the randomisation ratio between the treatment and control arms for the next stage, with the goal to resemble a standard RCT upon the completion of the trial. Simulation studies show that the AHCC design has desirable operating characteristics. For example, it saves on sample size when substantial information can be borrowed from the historical control, and it maintains power when little information can be borrowed from the historical control.

Neuro-Fuzzy-Based Adaptive Control for Autonomous Drone Flight

Adaptive control is the capability of a control system to modify its operation and achieve the best possible operation mode. A quadcopter is a nonlinear, unstable and under-actuated dynamic system, thus providing a challenge to control engineers in controlling and stabilising it during flight. This paper proposes the design, development, and application of an intelligent adaptive hybrid controller to control and stabilise the drone. The training data for adaptive neuro-fuzzy inference systems (ANFIS) are generated by the Linear Quadratic Regulator (LQR) under white-noise disturbance. The trained ANFIS is subsequently used to estimate the parameters of the control distribution matrix for the actual fault condition and the reconfiguration is carried out by computing new feedback gain using the pseudo-inverse technique. For the simple adaptive controller, LQR is also used to generate the desired trajectories of the reference model. In both experiments, the extended Kalman filter is implemented due to its non-linearity benefit. We demonstrate the performance of the proposed approach as a representative case study. The preliminary numerical simulation results further indicate that the proposed method is promising compared to conventional control techniques to control and stabilise a quadcopter drone.

Adaptive Output Containment Tracking Control for Heterogeneous Wide-Area Networks with Aperiodic Intermittent Communication and Uncertain Leaders.

This paper proposes an adaptive distributed hybrid control approach to investigate the output containment tracking problem of heterogeneous wide-area networks with intermittent communication. First, a clustered network is modeled for a wide-area scenario. An aperiodic intermittent communication mechanism is exerted on the clusters such that clusters only communicate through leaders. Second, in order to remove the assumption that each follower must know the system matrix of the leaders and achieve output containment, a distributed adaptive hybrid control strategy is proposed for each agent under the internal model and adaptive estimation mechanism. Third, sufficient conditions based on average dwell-time are provided for the output containment achievement using a Lyapunov function method, from which the exponential stability of the closed-loop system is analyzed. Finally, simulation results are presented to demonstrate the effectiveness of the proposed adaptive distributed intermittent control strategy.

Hybrid adaptive control of CNC drilling for enhancement of tool life and surface quality

Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.