Conventional Adaptive Control Research Articles

Adaptive robust motion control for hydraulic load sensitive systems considering displacement dynamic compensation

Hydraulic load-sensitive systems (HLSS) are widely used for high power density and energy efficiency. This study introduces an adaptive, energy-efficient HLSS with a valve-controlled variable motor. The system faces challenges from non-linearities, including internal higher-order dynamics due to displacement changes and external unknown disturbances, which hinder precision applications. To address this issue, this study explores HLSS principles to develop an accurate system model. Subsequently, an adaptive robust motion control that considers displacement compensation (DCARC) is proposed using the established model. DCARC can learn unknown parameters online and compensate the model more accurately to improve control accuracy. Experiments show that considering the higher order dynamic effects caused by displacement in the system can improve model accuracy and effectively reduce the burden of parameter adaptation and robust feedback terms. High-precision and energy-efficient HLSS motion is verified and realized in the study. The control accuracy of DCARC is 19.4% higher than that of conventional adaptive robust control (ARC). Under experimental conditions, the proposed system can improve energy efficiency by up to five times compared to valve-controlled fixed displacement motor systems (VFDS).

Adaptive Fixed-time Control for Multiple Switched Coupled Neural Networks

Article Adaptive Fixed-time Control for Multiple Switched Coupled Neural Networks Linhao Zhao, and Boqian Li * Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia * Correspondence: BoqianLi@student.uts.edu.au (Boqian Li) Received: 15 July 2024 Accepted: 24 August 2024 Published: 26 September 2024 Abstract: Adaptive control is an effective approach for mitigating undesirable deviations in prescribed closed-loop plant behavior. However, conventional adaptive control methods often exhibit slow responses in various control tasks. This paper introduces a novel adaptive control method to achieve fixed-time synchronization in a class of coupled neural networks. We present coupled neural networks with multiple switching topologies and design a fixed-time adaptive control strategy for this system. Furthermore, we establish a criterion to ensure the fixed-time stability of the closed-loop system. Two numerical examples are provided to demonstrate the effectiveness and accuracy of the theoretical results.

Sensorless control method of induction motors with new feedback gain matrix and speed adaptive law for low speed range

Abstract The conventional adaptive full-order observer-based speed sensorless control method for induction motor is prone to instability at low-speed region as improper feedback gain matrix and imprecise speed adaptive law are often used. To address these problems, a new feedback gain matrix design approach as well as a new speed adaptive law design technique are proposed in this paper. Firstly, considering that the d-axis current estimation error in the traditional feedback gain matrix design method has a great influence on the stability of the speed estimation algorithm, especially at low speeds, a new feedback gain matrix design method is proposed to minimize the d-axis current estimation error. Secondly, a new speed adaptive law design technique is studied based on the conventional method, which only requires the d-axis and q-axis current estimation error with a weight coefficient to be designed, simplifying the conventional speed adaptive law. Thirdly, the transfer function from the speed observation error to the proposed adaptive error is analyzed by Routh stability criterion theory, and the weight coefficient suitable for full range stable operation is determined by MATLAB software. Fourthly, the stability of the proposed method in this paper is analyzed using the poles distribution maps. Finally, the proposed method is experimentally verified based on a 2.2 kW induction motor experimental platform. The experimental results show that the proposed method can make the induction motor operate steadily at low-speed and zero-speed region with rated load. In addition, the proposed method has better anti-disturbance performance than the existing method.

A Joint Logarithmic based nonlinear Adaptive filter for load balancing in electric power distribution system

When nonlinear loads are introduced to a distribution system, various power quality challenges emerge, such as load unbalance, harmonic distortion, and reactive power mismatch. This study presents a novel solution utilizing a joint logarithmic hyperbolic cosine adaptive filter-operated distribution static compensator to mitigate these issues. The cost function of the adaptive filter is designed to be smooth for minor errors and resilient to significant errors. The proposed control technique computes fundamental active and reactive currents, generating reference signals for the distribution static compensator. A comparative analysis with conventional adaptive control techniques is also conducted. The distribution system undergoes comprehensive testing under various dynamics to ensure compliance with IEEE-519 standards. Real-time simulation-based experiments are employed to showcase the algorithm's performance.

Generalization of Direct Adaptive Control Using Fractional Calculus Applied to Nonlinear Systems

This paper presents a new direct adaptive control (DAC) technique using Caputo’s definition of the fractional-order derivative. This is the first time a fractional-order adaptive law is introduced to work together with an integer-order stable manifold for approximating the uncertainty of a class of nonlinear systems. The DAC approach uses universal function approximators such as multi-layer perceptrons with one hidden layer or fuzzy systems to approximate the controller. This paper presents a new lemma, which elucidates and clarifies the link between the Caputo and the Riemann–Liouville definitions. The introduced lemma is useful in developing a Lyapunov candidate to prove the stability of using the proposed fractional-order adaptive law. This is further explained by a numerical example, which is provided to elucidate the practicality of using the fractional-order derivative for updating the approximator parameters. The main novelty of the results in this paper is a rigorous stability proof of the fractional DAC approach for a class of nonlinear systems that is subjected to unstructured uncertainty and deals with the adaptation mechanism using a traditional integer-order stable manifold. This makes the control scheme easier to implement in practice. The fractional-order adaptation law provides greater degrees of freedom and a potentially larger functional control structure than the conventional adaptive control. Finally, the paper demonstrates that traditional integer-order DAC is a special case of the more general fractional-order DAC scheme introduced here.

Coordinated Decision Control of Lane-Change and Car-Following for Intelligent Vehicle Based on Time Series Prediction and Deep Reinforcement Learning.

Adaptive cruise control and autonomous lane-change systems represent pivotal advancements in intelligent vehicle technology. To enhance the operational efficiency of intelligent vehicles in combined lane-change and car-following scenarios, we propose a coordinated decision control model based on hierarchical time series prediction and deep reinforcement learning under the influence of multiple surrounding vehicles. Firstly, we analyze the lane-change behavior and establish boundary conditions for safe lane-change, and divide the lane-change trajectory planning problem into longitudinal velocity planning and lateral trajectory planning. LSTM network is introduced to predict the driving states of surrounding vehicles in multi-step time series, combining D3QN algorithm to make decisions on lane-change behavior. Secondly, based on the following state between the ego vehicle and the leader vehicle in the initial lane, as well as the relationship between the initial distance and the expected distance with the leader vehicle in the target lane, with the primary objective of maximizing driving efficiency, longitudinal velocity is planned based on driving conditions recognition. The lateral trajectory and conditions recognition are then planned using the GA-LSTM-BP algorithm. In contrast to conventional adaptive cruise control systems, the DDPG algorithm serves as the lower-level control model for car-following, enabling continuous velocity control. The proposed model is subsequently simulated and validated using the NGSIM dataset and a lane-change scenarios dataset. The results demonstrate that the algorithm facilitates intelligent vehicle lane-change and car-following coordinated control while ensuring safety and stability during lane-changes. Comparative analysis with other decision control models reveals a notable 17.58% increase in driving velocity, underscoring the algorithm's effectiveness in improving driving efficiency.

Composite iterative learning adaptive fuzzy control of fractional-order chaotic systems using robust differentiators

In conventional adaptive fuzzy control, to improve the approximation ability of fuzzy logic systems (FLSs), more fuzzy rules should be employed, which will greatly increase the computational burden. This paper investigates the adaptive fuzzy backstepping control of a specific category of incommensurate fractional-order chaotic systems afflicted by functional uncertainties and actuator faults. To address the challenging “explosion of complexity” issue, a novel modified fractional-order robust differentiator is proposed, capable of effectively suppressing noise. Importantly, an iterative learning adaptation law including parameter errors between adjacent periods and prediction errors derived from a series–parallel model is developed to improve the approximation accuracy of FLSs without using abundant fuzzy rules. Utilizing the frequency distribution model and the Lyapunov stability criterion, this approach guarantees the semi-global uniform boundedness of the closed-loop system and facilitates the convergence of tracking errors to a small region. Finally, the effectiveness of theoretical results is demonstrated through numerical simulation examples.

A Novel Narendra‐based adaptive anti‐disturbance control strategy for the microgrid inverters: A novel MG VSI adaptive anti‐disturbance control strategy based on Narendra theory

AbstractThe control performance of the voltage source inverter will be reduced when it operates under complex microgrid (MG) conditions. Traditionally, problems such as high controller order and complex design may occur when robust control and secondary regulation are adopted for the inverters. Meanwhile, when the conventional adaptive control is adopted, the adaptive gain may be too high, resulting in increased sensitivity of the system to external disturbances. To address the above problems, a novel Narendra‐based adaptive anti‐disturbance control strategy for microgrid voltage source inverter is proposed in this paper. Firstly, a Narendra adaptive‐based controller is constructed, which enables the controller to adaptively adjust the system parameters to change the inverter output voltage quality. Secondly, the extended state observer (ESO) is introduced to observe and suppress the total disturbance of the system, which can make the system less sensitive and ensure the stability of the system operation. Thirdly, according to the Lyapunov stability theory, the Lyapunov function is chosen to prove the global asymptotic stability of the system, and an adaptive law is designed. Finally, the comparative experiment shows that the proposed control strategy based on Narendra adaptive anti‐disturbance can maintain excellent voltage control performance in complex working conditions, and then ensure that the inverter's output voltage always meets power quality standard in complex working conditions.

Development of resistance spot welding technology applying multi-stage adaptive control for narrow pitch spot welding of high strength steel sheets

In order to expand the application of high strength steel sheets to automotive bodies, stabilizing nugget diameter of resistance spot welds is required in the presence of several industrial disturbances. This study aims to ensure the nugget diameter of high strength steel sheets regardless weld spacing by utilizing adaptive control technology based on real-time feedback of heat quantity. Short weld spacing leads to expulsion from the sheet surface applying conventional adaptive control welding for mild steel sheets, whereas decrease in nugget diameter due to reduction of effective welding current for high strength steel sheets. On the other hand, two-stage adaptive control method enables to ensure nugget diameter regardless of steel strength even with 10 mm weld spacing, in which heat quantity was controlled independently in each stage of welding. The mechanism of these phenomena was revealed by verifying the influence of physical properties of steel and welding current pattern on the shunting phenomena using numerical simulation.

Adaptive Control of a Virtual Synchronous Generator with Multiparameter Coordination

This paper proposes an adaptive strategy of co-regulating the three parameters—P/ω droop coefficient, virtual inertia, and damping coefficient—for the virtual synchronous generator (VSG). This approach is able to solve the uncoordinated performance between the virtual inertia and the damping using the conventional adaptive control in which the system may experience serious frequency fluctuations. Through the mathematical modeling of the VSG grid-connected system, the segmental analysis of the VSG transient process is carried out, and the parameter adjustment law of each stage is obtained. The VSG angular velocity change and the angular velocity instantaneous change rate are associated with the inertia to realize the adaptive adjustment of the inertia, and the adaptive adjustment of the P/ω droop coefficient is carried out in real time according to the VSG angular velocity change. A functional relationship is established between the P/ω droop coefficient, virtual inertia, and damping coefficient so that the P/ω droop coefficient, virtual inertia, and damping coefficient are coordinated to keep the system in the best damping ratio state all the time. Finally, the superiority of the proposed strategy is proved by simulation comparison.

Robust Adaptive Resonant Controller for PMSM Speed Regulation Considering Uncertain Periodic and Aperiodic Disturbances

This paper studies a robust adaptive resonant controller (RARC) for the speed loop of permanent magnet synchronous motor (PMSM) drives to attenuate periodic speed fluctuation of uncertain frequency. The conventional adaptive resonant controller is susceptible to the aperiodic disturbances (e.g., load torque, parameter variation, and friction torque), so it has poor robustness to the aperiodic disturbance. To solve this problem, a linear extended state observer is applied to estimate the aperiodic disturbances, and then the estimated disturbances are adapted to the adaptive resonant controller to enhance its robustness. Thus, the proposed scheme can suppress the uncertain periodic and aperiodic disturbances simultaneously. Also, the stability of the closed-loop system with the proposed scheme is analyzed by the singular perturbation theory and the averaging theorem, which can guide the tuning of control parameters. Therefore, even if there exist aperiodic disturbances, the studied RARC can also stably operate to suppress the periodic disturbance of uncertain frequency. The effectiveness of the studied RARC is validated on a 2.2-kW PMSM drive platform.

Model-Based Control of a 4-DOF Rehabilitation Parallel Robot with Online Identification of the Gravitational Term.

Parallel robots are being increasingly used as a fundamental component of lower-limb rehabilitation systems. During rehabilitation therapies, the parallel robot must interact with the patient, which raises several challenges to the control system: (1) The weight supported by the robot can vary from patient to patient, and even for the same patient, making standard model-based controllers unsuitable for those tasks since they rely on constant dynamic models and parameters. (2) The identification techniques usually consider the estimation of all dynamic parameters, bringing about challenges concerning robustness and complexity. This paper proposes the design and experimental validation of a model-based controller comprising a proportional-derivative controller with gravity compensation applied to a 4-DOF parallel robot for knee rehabilitation, where the gravitational forces are expressed in terms of relevant dynamic parameters. The identification of such parameters is possible by means of least squares methods. The proposed controller has been experimentally validated, holding the error stable following significant payload changes in terms of the weight of the patient's leg. This novel controller allows us to perform both identification and control simultaneously and is easy to tune. Moreover, its parameters have an intuitive interpretation, contrary to a conventional adaptive controller. The performance of a conventional adaptive controller and the proposed one are compared experimentally.

Tuning functions based adaptive backstepping control for uncertain strict-feedback nonlinear systems using barrier Lyapunov functions with full state constraints

This paper investigates the tuning functions based adaptive backstepping control for a class of uncertain strict-feedback nonlinear systems using barrier Lyapunov functions with full state constraints. The proposed adaptive control scheme is constructed by combining the barrier Lyapunov functions (BLFs) with the tuning functions based adaptive backstepping control technique. Compared with the relevant results, the proposed adaptive control scheme is constructed to guarantee that the overparametrization problem inherent to the conventional adaptive backstepping control method is avoided and the full state constraints are not violated by introducing BLFs. Moreover, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded (UUB) and the tracking errors converge to a small neighborhood around zero by appropriately choosing design parameters. Simulation results of an electromechanical system are provided to verify the feasibility of the proposed adaptive control method. The simulation results clearly prove the effectiveness of our approach compared with the tuning functions based adaptive backstepping control (TFABC) and the traditional backstepping control design (BC) schemes that system state without constraints.

Neural Adaptive Impedance Control for Force Tracking in Uncertain Environment

Torque-based impedance control, a kind of classical active compliant control, is widely required in human–robot interaction, medical rehabilitation, and other fields. Adaptive impedance control effectively tracks the force when the robot comes in contact with an unknown environment. Conventional adaptive impedance control (AIC) introduces the force tracking error of the last moment to adjust the controller parameters online, which is an indirect method. In this paper, joint friction in the robot system is first identified and compensated for to enable the excellent performance of torque-based impedance control. Second, neural networks are inserted into the torque-based impedance controller, and a neural adaptive impedance control (NAIC) scheme with directly online optimized parameters is proposed. In addition, NAIC can be deployed directly without the need for data collection and training. Simulation studies and real-world experiments with a six link rotary robot manipulator demonstrate the excellent performance of NAIC.

Indirect adaptive fuzzy finite time synergetic control for power systems

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds

Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach.

Novel Robust Control Using a Fractional Adaptive PID Regulator for an unstable system

Recent advances in fractional order calculus led to the improvement of control theory and resulted in potential use of fractional adaptive PID controller in advanced academic and industrial applications as compared to the conventional adaptive PID controller. Basically, a fractional order adaptive PID controller is an improved version of classical integer order adaptive PID controller that outperformed its classical counterpart. In case of a closed loop system, a minute change would result in instability of the overall system. An efficient PID controller can be used to control the response of such system. Among various parameters of an instable system, speed of the system is an important parameter to be controlled efficiently. The current research work presents the speed controlling mechanism for an uncertain instable system by using fractional order adaptive PID controller.To validate the arguments, effectiveness and robustness of the proposed fractional order adaptive PID controller have been studied in comparison to the classical adaptive PID controller using The Criterion of quadratic error. Simulation findings and comparisons demonstrated that the proposed controller has superior control performance and outstanding robustness in terms of percentage overshoot, settling time, rising time, and disturbance rejection.

A Backstepping Controller Based on a Model-Assisted Extended State Observer for a Slice Rotor Supported by Active Magnetic Bearings

To improve the robustness of a slice rotor system supported by active magnetic bearings (AMBs), here, we propose a backstepping controller based on a model-assisted extended state observer (MESO-BC). Based on a generalized extended state observer (GESO), a model-assisted extended state observer is studied with consideration of the linear model of AMBs. The model-assisted extended state observer can estimate the unknown disturbances of the active magnetic bearing system, such as model inaccuracy and external disturbance, and is superior to the generalized extended state observer with respect to observation errors and the speed of convergence errors. In addition, it is compared with the backstepping controller based on a generalized extended state observer (GESO-BC) and conventional adaptive backstepping controller (ABC), and the simulation and experimental results verify the effectiveness of the proposed method. The experimental results demonstrate that the overshoot of the MESO-BC decrease by 5.94% and 13.2% as compared with the GESO-BC and ABC under the effect of pulse disturbance, respectively, and the rotor displacement of the MESO-BC reduce by 40.3% and 54.6% as compared with the GESO-BC and ABC under the effect of the sinusoidal disturbance, respectively.

Robust adaptive dynamic surface control scheme for a class of single‐input and single‐output uncertain nonlinear systems in strict‐feedback form

SummaryThis article presents a robust adaptive dynamic surface control using σ‐modification adaptation laws for a class of single‐input and single‐output (SISO) uncertain nonlinear systems in strict‐feedback form with parametric uncertainties and external disturbances. The proposed scheme is developed by employing dynamic surface control based robust adaptive control technique. The key features of the approach are that, first, the problem of explosion of complexity inherent in the conventional adaptive backstepping control method is avoided, second, the proposed approach of σ‐modification adaptation laws gives fast and accurate parameter estimation performance, and, third, the closed‐loop signals of the system are proven to be uniformly ultimately bounded (UUB) by using the Lyapunov stability theory. We show the effectiveness of our approach by simulating an electromechanical system.

A unified adaptive control approach of nonlinear continuous non‐parameterized systems with asymmetric control gains for trajectory tracking in different domains

AbstractIn this article, for nonlinear continuous non‐parameterized (NCNP) systems, an adaptive iterative learning control (ILC) algorithm, which can adjust the adaptive parameters in both iteration‐domain and time‐domain, is first proposed to track different reference trajectories repetitively over a finite time interval. As the NCNP system is required to asymptotically track reference trajectory in infinite time‐domain, by virtue of partitioning the reference trajectory and system signals with a fixed time interval, the proposed adaptive ILC controller is then extended to handle the asymptotic tracking issue in infinite time‐domain. Therefore, a unified adaptive control approach is practically presented for NCNP systems to track reference trajectories in different domains (the infinite iteration‐domain and the infinite time‐domain). A prominent feature of the unified adaptive control approach is that the unknown control gain matrices in the NCNP systems are assumed to be invertible only. As a result, the general requirement in conventional adaptive control and adaptive ILC that the control gain matrices of plants are real symmetric and positive‐definite (or negative‐definite) is greatly relaxed. In addition, only three adaptive variables are designed for adjusting or updating in the proposed unified adaptive control approach such that the structure of controller is very simple and the memory space for computing is saved.