Nonlinear Adaptive Control Scheme Research Articles

An Adaptive Extended Kalman Filter With Passivity-Based Control for DC-DC Converter in DC Microgrids Supplying Constant Power Loads

This paper proposes an adaptive nonlinear control strategy to eliminate the instability problem of buck converters in DC microgrids (DCMG) considering constant power load (CPL). The strategy includes an Adaptive Passivity-Based Controller (PBC) and an Adaptive Extended Kalman Filter (AEKF). The proposed AEKF performs faster and more accurately than the conventional EKF and adjusts the covariance matrices adaptively. Furthermore, this AEKF directly estimates the unknown CPL power supplied by the converter and prevents the number of measurement sensors from increasing. Hence, PBC ensures system stability alongside AEKF. The controller guarantees Lyapunov stability and fast dynamic response in the presence of uncertainties and perturbations compared to other nonlinear methods. MATLAB simulations and laboratory implementation are provided to confirm the performance and robustness of the proposed control strategy and validate the analysis.

Adaptive robust control of electromagnetic actuators with friction nonlinearity and uncertainty compensation

Friction nonlinearity and uncertainty are the main factors affecting the highly performance control of electromagnetic actuators. In this paper, a nonlinear adaptive robust control strategy is proposed of electromagnetic actuators with friction nonlinearity and uncertainty compensation. First, the dynamical model of the electromagnetic actuator is established considering nonlinearity and uncertainty. Then, an adaptive robust controller is designed based on the continuously differentiable friction model to ensure that the control input is continuously and bounded. In the design of the controller, the unfavorable effects of unknown parameters in the electromagnetic actuator are eliminated by constructing a parameter adaptive law. Meanwhile, in order to improve the tracking accuracy of the electromagnetic actuator, a nonlinear robust control law is designed to ensure the robustness of the controller. The stability analysis by Lyapunov function shows that the asymptotic tracking effect can be obtained when only parameter uncertainty exists in the closed-loop system of the electromagnetic actuator, and the consistent bounded stability can be ensured when the system also exists uncertain nonlinearity. Extensive comparative results verify the effectiveness of the proposed control method.

A passivity control and developed nonlinear disturbance observer for boost converter with constant power load in DC microgrid

SummaryThis paper presents an adaptive nonlinear control scheme to ensure the stability of a boost converter in a DC microgrid that supplies a constant power load (CPL) and a resistive load. The proposed controller comprises a passivity‐based control (PBC) and a Nonlinear Disturbance Observer‐Based Robust Control (NDOBRC). The PBC guarantees the overall stability of the proposed controller, whereas the NDOBRC compensates for various sources of disturbances and uncertainties in the system, such as CPL power. The large‐signal stability of the PBC is established using the Lyapunov function. Simulation and experimental results demonstrate the effectiveness of the proposed controller in terms of superior performance compared to the PI method.

Robust adaptive control of underwater glider for bottom sitting-oriented soft landing

This paper deals with the formalization, design, theorization, and implementation of a robust adaptive nonlinear control scheme to solve the bottom sitting-oriented soft landing control problem for an underwater glider under model uncertainties, actuator dynamics, and external disturbances. First, uniformized motion equations are derived based on Lie derivatives for describing pitch angle and vertical velocity tracking control. Then, the robust adaptive control framework is enhanced by containing an anti-windup auxiliary system and a disturbance observer. The stability of the closed-loop control system based on Lyapunov theory is proven to guarantee the boundedness of the entire internal signals. Finally, multifarious experimental results are also conducted by a self-developed underwater glider to demonstrate the effectiveness of the proposed controller.

Nonlinear control of neurodegenerative diseases. A case study on optical illusion networks disrupted by diabetic retinopathy

We present an efficient computational framework for the design of optimal drug delivery control strategies that can successfully treat a family of neurodegenerative diseases that originate from channelopathies and synaptopathies. To this end, we extend our previously introduced scalable and adaptable modelling framework that models heterogeneous Hodgkin–Huxley (HH) neuronal networks to account for the modular organisation of the neurons in the brain, e.g. interconnecting sub-networks of heterogeneous neurons. Based on this framework, we introduce a novel design of lateral inhibition networks to successfully reproduce 2D optical illusions that are known to occur in the human retina. We model the dynamic behaviour of Diabetic Retinopathy (DR), a neurodegenerative disease that progressively hinders the inherent ability of patients to distinguish optical illusions. We implement nonlinear control on accurate models of diabetic lateral inhibition neuronal networks to recover their functionality and investigate the effects of virtual drug administration. We utilise the healthy and diabetic optical illusions generated by these networks as a ‘computational’ phenotype to design therapies based on an adaptive terminal error iterative learning controller (TE-ILC). Therefore, we provide a comprehensive computational framework that is able to imitate the dynamics of healthy and diseased neuronal networks and we propose an adaptive nonlinear control strategy based on the error between output images that correspond to healthy and diseased conditions.

An adaptive safety control approach for virtual coupling system with model parametric uncertainties

Virtual coupling attracts extensive attention from the railway communities due to its promising capacity increase and flexibility improvement. Accurate control that guarantees system safety and performance remains a problem that needs to be further studied. This paper investigates the virtual coupling train system (VCTS) safety control problem with model parameter uncertainties using an adaptive nonlinear control strategy based on urban rail transit systems. A safe automatic train control model is introduced, and a novel control framework consisting of two levels is designed to give an overall virtual coupling train control architecture. For the leading train, we deploy an automatic train control system based on dynamic programming. Then, considering parameter uncertainties, we design an adaptive nonlinear controller that ensures safety and control efficiency for the following trains. The proposed control strategy can ensure that a virtually coupled train set tracks the given speed curve and keeps inner distance while guaranteeing the string stability and safety of the platoon, considering parameter uncertainties. The simulation results show that the proposed approach leads to great performance improvement in virtual coupling train operations.

Carrier Aircraft Flight Controller Design by Synthesizing Preview and Nonlinear Control Laws

This paper proposes an innovative automatic carrier landing control law for carrier-based aircraft considering complex ship motion and wind environment. Specifically, a strategy is proposed to synthesize preview control with an adaptive nonlinear control scheme. Firstly, incremental nonlinear backstepping control law is adopted in the attitude control loop to enhance the anti-disturbance capability of the aircraft. Secondly, to enhance the glide slope tracking performance under severe sea conditions, the carrier motion is predicted, and the forecasted motion is adopted in an optimal preview control guidance law to compensate influences induced by carrier motion. However, synthesizing the inner-loop and outer-loop control is not that straightforward since the preview control is naturally an optimal control law which requires a state-space model. Therefore, low-order equivalent fitting of the attitude-to-altitude high-order system model needs to be performed; furthermore, a state observer needs to be designed for the low-order equivalent system to supply required states to the landing controller. Finally, to validate the proposed methodology, an unmanned tailless aircraft model is used to perform the automatic landing tasks under variant sea conditions. Results show that the automatic carrier landing system can lead to satisfactory landing precision and success rate even under severe sea conditions.

Robust Nonlinear Adaptive Control for Power Quality Enhancement of PMSG Wind Turbine: Experimental Control Validation

Due to the intense penetration of wind energy into the power grid, grid quality and stability have become a crucial necessity in this type of power generation. It is in this context that this article has just designed an Adaptive Nonlinear Control strategy applied to the Permanent Magnet Synchronous Generator (PMSG) of 1.5 MW power, in order to generate good quality and cleanly usable energy. Interestingly, this robust control algorithm mainly uses the Lyapunov stability theory, which ensures the stability of the Wind Energy Conversion System (WECS), and therefore offers excellent results in the presence of system parametric uncertainties and changes in the elements of the external environment. To this end, the methodology followed in this in-depth study focuses on the application of the Adaptive Backstepping Control algorithm for WECS by exploiting the MATLAB/Simulink toolbox. The theoretical study and simulation of the WECS was supported by the Processor-in-the-Loop (PIL) implantation of the control in the dSPACE DS1104 embedded board to approve the effect of the control in terms of robustness against different wind profiles and parametric changes. ST-LINK communication is used to connect the embedded board and the host computer. The results obtained revealed a fast response of the different signals, a practically low ripple rate of the order of 0.1% and minor overshoots for the different electrical quantities. Operation with a unity power factor is well ensured via this control strategy. Therefore, the adaptive control applied to the WECS has verified the high performance offered and benefits from additional robustness properties.

Nonlinear Adaptive Disturbance Rejection Control Strategy Based on VSG for Photovoltaic-Storage Grid-Connected Inverter

Nonlinear Adaptive Disturbance Rejection Control Strategy Based on VSG for Photovoltaic-Storage Grid-Connected Inverter

A Nonlinear Adaptive Stabilizing Control Strategy to Enhance Dynamic Stability of Weak Grid-Tied VSC System

Large scale renewable energy systems interfaced with voltage source converters (VSCs) experience several challenging issues when integrated to weak grids. One of the specific issue i.e. control loop interaction of vector-controlled VSC under high grid impedance leads to several instability mechanism ultimately resulting in unwanted tripping of the VSC unit. As a countermeasure, a unique nonlinear adaptive stabilizing control (NASC) technique has been proposed in this paper to enhance the dynamic stability of the VSC during such scenarios. A small-signal model is developed first to examine the instability mechanism of VSC connected to weak grids under abnormal operating conditions and varying set points of the power controller. The dominant modes are identified from the analysis and a stabilizing controller is developed based on adaptive dynamic surface control philosophy for enhancing its stability. The proposed controller has been proved to be uniformly ultimately bounded using Lyapunov analysis. Besides, the stabilizing mechanism of VSC is analyzed with the proposed controller by performing small-signal stability analysis of the entire system. The efficacy of the proposed control scheme is demonstrated by performing numerical simulations in MATLAB 2017a and conducting experimental validations in controller-hardware-in-the Loop (CHIL) platform.

Nonlinear disturbance observer-based adaptive nonlinear model predictive control design for a class of nonlinear MIMO system

Model predictive control with less prior knowledge of system uncertainty and external disturbance is a long-standing theoretical and practical problem. In this paper, a solution is presented by proposing a disturbance observer-based adaptive nonlinear model predictive control scheme for a class of nonlinear MIMO systems. Our scheme requires the state and parameterdependent state-space model to linearise the nonlinear system along the prediction horizon. To cope with the unknown system uncertainty, the multiple estimation model and the concept of second-level adaptation [Pandey, V. K., Kar, I., & Mahanta, C. (2014). Multiple models and second level adaptation for a class of nonlinear systems with nonlinear parameterisation. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1–6). IEEE.] technique for the nonlinear system is used. To ensure the boundedness of the estimated parameter, a projection-based adaptive law [Hovakimyan, N., & Cao, C. (2010). L1 adaptive control theory: Guaranteed robustness with fast adaptation. SIAM.] is used. Using a twin-rotor MIMO system (TRMS), the effectiveness of the proposed control algorithm has been verified. Simulation and real-time results show that the proposed control algorithm performs better than the existing control algorithm in the presence of unknown external disturbance and parameter uncertainties.

A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.

Observer-based lateral stability adaptive control method for distributed drive electric vehicles

Aiming at the lateral instability of distributed drive electric vehicles (DDEV) under extreme conditions, a nonlinear adaptive lateral stability control strategy is designed based on Cubature Kalman filter (CKF) observer and adaptive fuzzy sliding mode control (FSMC) theory. To solve the problem that it is difficult to measure some parameters of road and vehicle state directly, a vehicle state observer based on a double CKF algorithm is designed. The joint sliding mode surface is designed by the deviation between the ideal and observed values of yaw rate and sideslip angle. The SMC algorithm is combined with the adaptive fuzzy algorithm to design an adaptive FSMC to accurately obtain the additional yaw moment of the vehicle. Simulation verification is carried out based on CarSim and MATLAB/Simulink simulation platforms. The results show that the proposed lateral stability adaptive method can effectively improve the robustness of the DYC system.

An Inverse Dynamics-Based Control Approach for Compliant Control of Pneumatic Artificial Muscles

Rehabilitation is an area of robotics in which human–robot collaboration occurs, requiring adaptation and compliance. Pneumatic artificial muscles (PAM) are soft actuators that have built-in compliance making them usable for rehabilitation robots. Conversely, compliance arises from nonlinear characteristics and generates obstructions in modeling and controlling actions. It is a critical issue limiting the use of PAM. In this work, multi-input single-output (MISO) inverse modeling and inverse dynamics model learning approaches are combined to obtain a novel nonlinear adaptive control scheme for single PAM-actuated 1-DoF rehabilitation devices, for instance, continuous passive motion (CPM) devices. The objective of the proposed system is to bring an alternative solution to the compliant operation of PAM while performing exercise trajectories, to satisfy requirements such as larger range of motion (ROM) and adaptability to external load impedance variations. The control system combines the operation of a nonlinear autoregressive network with exogenous inputs (NARX)-based inverse dynamics estimator used as a global range controller and cascade PIDs for local position and pressure loops. Implementation results demonstrated the efficacy of the introduced method in terms of compliant operation for dynamic external load variations as well as a stable operation in case of impulsive disturbances. To summarize, a simple but efficient method is illustrated to facilitate the common use of PAM.

Vertical Motion Control of an Underwater Glider with a Command Filtered Adaptive Algorithm

Underwater gliders are widely used in oceanic observation, which are driven by a hydraulic buoyancy regulating system and a movable mass. Better motion performance can help us to accomplish observation tasks better. Therefore, a command filtered adaptive algorithm with a detailed system dynamic model is proposed for underwater gliders in this paper. The dynamic model considers seawater density variation, temperature variation and hull deformation according to dive depth. The hydraulic pump model and the movable mass dynamic are also taken into account. An adaptive nonlinear control strategy based on backstepping technique is developed to compensate the uncertainties and disturbances in the control system. To deal with the command saturation and calculation of derivatives in the backstepping process, command filtered method is employed. The stability of the whole system is proved through the Lyapunov theory. Comparative simulations are conducted to verify the effectiveness of the proposed controller. The results demonstrate that the proposed algorithm can improve the motion control performance for underwater gliders under uncertainties and disturbances.

Perturbation estimation based nonlinear adaptive control of VSC flexible excitation system

A new type of flexible excitation system (FES) is proposed by using fully-controlled power electronic devices such as IGBTs to replace the half controlled devices in the conventional static excitation system, which has the merit of independent control of rotor angle and terminal voltage of the synchronous generator. This paper proposes nonlinear adaptive control (NAC) strategies for synchronous generator with FES in a single machine infinite bus system. External disturbances and the uncertainties of all parameters as well as modelling are defined as lumped perturbation terms and estimated by perturbation observers or state and perturbation observer. The estimated perturbation terms are used to compensate the real perturbations and achieve a model-independent and robust NAC. Merits of the FES against the conventional static excitation system and effectiveness of the proposed NAC scheme against the accurate model based multi-variable feedback linearisation control are verified via small-signal stability analysis and simulation studies. The simulation results have shown that the proposed NAC can achieve superior control performance with less states feedback during a three-phase short circuit and better robustness against parameter uncertainties, compared with linear control and multi-variable feedback linearisation control.

Adaptive nonlinear control scheme for containment of COVID-19 spread

An early understanding of effective policy and transmission dynamics can help us prevent further outbreaks of the COVID-19. This article proposes a nonlinear adaptive control strategy based on effective policy to contain this epidemic. A mathematical model of nonlinear dynamics of the COVID-19 transmission in India is considered. The model also takes uncertainty into account. The control input calculated by the proposed controller prescribes the contact rate of the disease so that the number of infectious population converges to a desired zero value. The controller utilizes a Lyapunov theorem-based adaption law to be able to deal with modeling uncertainties, instability, and divergence. The controller performance on the nonlinear and uncertain model of the COVID-19 transmission has been investigated through a simulation study.

Adaptive Model Predictive Control for Fire Incidents in Water Distribution Networks

The real-time control of water distribution networks for firefighting is an important but little-studied problem. Water utilities in England and Wales reduce hydraulic pressure to a minimum regulatory threshold in order to reduce leakage and avoid financial penalties. However, utilities are not legally bound to guarantee specific flow rates from fire hydrants, which poses a risk for firefighting. This study presents an adaptive nonlinear model predictive control scheme for water distribution networks integrating two modes of control: (1) normal control, when a network operates under normal conditions and the objective is to minimize energy costs and average zonal pressure, and (2) fire control, when fire flows need to be delivered from hydrants without exceeding maximum pressure and customer demand is maintained to the best extent possible. The proposed scheme is applied to an operational network. It demonstrates the combined benefits of reducing costs and leakage with increased fire flows.

Research on Anti-Interference Nonlinear Adaptive Control Method for Autonomous Underwater Gliders

In this paper, the characteristics of underactuation are analyzed according to the working principle of underwater glider, a complete 6DOF model of underwater glider is established, and the error equation of 3D path tracking is given. Based on the line-of-sight navigation method in three-dimensional space, the nonlinear adaptive control strategy is designed, and the roll angle control law, pitch angle control law, and depth control law of underwater glider are given. The tracking error of underwater glider is proved to be convergent by Lyapunov stability theorem. Finally, the results of marine experiments verify that the nonlinear controller designed in this paper can overcome the influence of constant current disturbance under certain conditions and has good robustness and good tracking effect under different ocean current sizes and course angles. Of course, one control method cannot be applicable to all sea conditions. When the angle between the size and direction of the ocean current exceeds a certain value, the tracking control of the underwater glider will show obvious divergence. Therefore, the stability range against the interference of the sea current is given in this paper.

Global Source Optimisation Based on Adaptive Nonlinear Particle Swarm Optimisation Algorithm for Inverse Lithography

Source optimisation (SO) is an approved approach to improve the imaging quality in inverse lithography techniques. It is critical to apply an optimisation approach with high convergence efficiency and minimum errors in pixel-based SO. To improve the convergence efficiency of the pixel-based SO, a route of particle swarm optimiser (PSO) combined with the adaptive nonlinear control strategy (ANCS) is proposed in this study. As a global optimisation algorithm, ANCS-PSO has the attributes of breaking away from the local optimum by adjusting the particle learning factor adaptively. In addition, the nonlinear control approach can broaden the search range and speed up the convergence of the iteration operation. The proposed approach also is compared with the linear decreasing inertia weight strategy and the simulated annealing strategy. The performance verification simulation displays the validity of PSO-ANCS and its potentials in SO with high convergence efficiency and optimisation capacity, by comparing the linear decreasing inertia weight strategy and the simulated annealing strategy.