Presence Of Model Uncertainties Research Articles

Robust and Adaptive Control of a Soft Continuum Manipulator for Minimally Invasive Surgery

This article investigates the model-based control in configurations space of a soft continuum manipulator for minimally invasive surgery. The main control challenges for these types of systems are the presence of model uncertainties and nonlinearities. To this end, a sliding-mode controller, a Lyapunov redesign controller, and an adaptive controller have been designed and compared by means of simulations and experiments on a prototype. The results indicate that the adaptive controller yields better accuracy but a slower transient. Conversely, the sliding-mode controller and Lyapunov redesign yield a faster response but can result in chattering or steady-state errors.

Design and implementation of a nonlinear observer for a novel Quadrotor UAV: the HELIPLANE

This study presents the design and dynamic modeling of a new type of quadrotor UAV, referred to as the Heliplane, which integrates the vertical take-off and landing (VTOL) capabilities of a multirotor with the horizontal flight efficiency of a fixed-wing aircraft. The modeling process involved creating a dynamic representation of the Heliplane, with special attention to the determination of its aerodynamic coefficients, which were calculated using SolidWorks Flow Simulation. The aerodynamic forces, including lift and drag, were analyzed based on the flow characteristics around the Heliplane's structure. To further enhance the system's performance, a nonlinear observer based on a sliding mode approach was synthesized. This observer was designed in a triangular form to ensure accurate state estimation and robust control, even in the presence of model uncertainties. The observer's key advantage lies in its finite-time convergence, which guarantees quick and precise tracking of the Heliplane's state variables. Simulation results demonstrated the effectiveness of the proposed observer in estimating the rotational and translational velocities with minimal deviation. The sliding mode observer's performance suggests its suitability for control and fault detection tasks in UAV applications, particularly in challenging operational environments.

Integrating Factor Models Using Bayesian Model Averaging: Evidence from Korea

This study applies the Bayesian model averaging approach to the Korean stock market data to yield an integrated factor model that can address model uncertainty. During the period from 2004 to 2022, we choose 14 factors and 10 macro predictors and generate a total of 10,485,760 candidate factor models by considering all possible combinations of each variable and whether to allow for model mispricing. The integrated model estimates the expected return and covariance matrix by combining the predictions of all the candidate models using Bayesian posterior probabilities as weights. In the Korean stock market, there is no clear winner model with dominant posterior probabilities, implying that model uncertainty is substantial. The optimal portfolio of the integrated model has a higher out-of-sample Sharpe ratio and lower downside risk than benchmark factor models. Model uncertainty has a significant impact on the estimation of the covariance matrix of the integrated model, and model disagreement about expected returns is particularly acute during periods of sharp market declines. In the presence of model uncertainty, Bayesian investors perceive equities to be riskier than historical volatility.

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

Composite adaptive neural control for automatic carrier landing system with input saturation and output constraints

This paper investigates the automatic carrier landing control problem in the presence of model uncertainty, airwake disturbances, input saturation, and output constraints. Considering the performance requirements of the carrier-based aircraft, a composite adaptive neural controller is proposed based on the time-varying barrier Lyapunov function and backstepping control techniques. The radial basis function neural network is used to approximate the model uncertainty, where the neural network weight update law incorporating prediction and tracking errors further improves the convergence rate of the neural network and mitigates high-frequency oscillations. Furthermore, an adaptive disturbance compensation model is established to mitigate the adverse effects of airwake disturbances and estimation errors in the neural network. Based on the Lyapunov stability theory, it is proven that the proposed controller maintains the aircraft trajectory within the prescribed constraints and also ensures that all signals in the closed-loop control system are semiglobally uniformly ultimately bounded. Finally, comparative simulations are performed to demonstrate the effectiveness and superiority of the proposed composite adaptive neural control method.

Adaptive sliding mode control with nonlinear MPC-based obstacle avoidance using LiDAR for an autonomous surface vehicle under disturbances

This manuscript proposes a path-following and collision avoidance guidance and control system for an autonomous surface vehicle operating in cluttered environments under perturbations. A nonlinear model predictive control strategy handles the state and control constraints for obtaining a guidance law. The approach considers the vessel sideslip angle to provide the desired heading angle, satisfying path-following and obstacle avoidance. Additionally, to solve the nonlinear optimization problem for obtaining the guidance law in real-time, the acados software package is used. Furthermore, in order to guarantee that the vehicle tracks the guidance in presence of model uncertainties and external disturbances, a robust adaptive sliding mode low-level controller is adopted. This controller is finite time convergent, and dynamically adapts its control gain to use only the necessary control inputs. Furthermore, a low-memory and computationally-light obstacle detection strategy employing a LiDAR sensor is presented and proven to run in real-time. Simulation and experimental tests conducted using a customized prototype validate the advantages of the proposed strategy and demonstrate the effectiveness of the path following while evading multiple obstacles in the presence of external disturbances and model discrepancies.

Neural adaptive performance guaranteed formation control for USVs with event-triggered quantized inputs

ABSTRACT This study presents an adaptive fault-tolerant performance guaranteed formation control strategy with event-triggered quantised inputs (EDQI) for unmanned surface vessels (USVs) in the presence of model uncertainties and actuator faults. Firstly, a prescribed performance formation guidance control law is proposed to allow formation tracking errors to evolve within the performance envelope and convergence to prescribed steady regions at an appointed time. Then, minimal-parameter-learning-based neural networks (MLP-NN) are used to tackle model uncertainties. To reduce the frequency of actuator updates and the communication burden on the channel, a dynamic memory event-triggered quantised input strategy is investigated. In addition, adaptive estimators and auxiliary design signals are built to compensate for the effects caused by input quantisation and actuator faults. It is proved that all closed-loop signals are bounded and formation errors can converge to the prescribed region at an appointed time. Finally, the effectiveness of the proposed controllers is verified by numerical simulations.

Robustness of dynamic quantum control: Differential sensitivity bounds

Dynamic control via optimized, piecewise-constant pulses is a common paradigm for open-loop control to implement quantum gates. While numerous methods exist for the synthesis of such controls, there are many open questions regarding the robustness of the resulting control schemes in the presence of model uncertainty; unlike in classical control, there are generally no analytical guarantees on the control performance with respect to inexact modeling of the system. In this paper, a new robustness measure based on the differential sensitivity of the gate fidelity error to parametric (structured) uncertainties is introduced, and bounds on the differential sensitivity to parametric uncertainties are used to establish performance guarantees for optimal controllers for a variety of quantum gate types, system sizes, and control implementations. Specifically, it is shown how a maximum allowable perturbation over a set of Hamiltonian uncertainties that guarantees a given fidelity error can be reliably computed. This measure of robustness is inversely proportional to the upper bound on the differential sensitivity of the fidelity error evaluated under nominal operating conditions. Finally, the results show that the nominal fidelity error and differential sensitivity upper bound are positively correlated across a wide range of problems and control implementations, suggesting that in the high-fidelity control regime, rather than there being a trade-off between fidelity and robustness, higher nominal gate fidelities are positively correlated with increased robustness of the controls in the presence of parametric uncertainties.

Uncertainty-aware output feedback model predictive combustion control of RCCI engines

Accurate model development is essential for effective model-based control of Reactivity Controlled Compression Ignition (RCCI) engines. However, due to the intricate nature of engine combustion process, achieving a precise model that can capture the complex dynamic behavior and ensure high control performance poses a significant challenge. In this paper, we propose an uncertainty-aware output feedback model predictive control approach for efficient combustion management in RCCI engines. In contrast to the previously developed approaches, this method adopts a data-driven approach within the linear parameter-varying (LPV) framework for model development. To address the model mismatch between the LPV model and the real system/data, Bayesian Neural Networks (BNNs) are employed which provide the probability distribution of the uncertainties. The BNNs enable the formation of a scenario tree, effectively characterizing the range of potential uncertainties in the system. Through the implementation of scenario-based model predictive control, our approach ensures high tracking performance for the RCCI engine in the presence of modeling uncertainties and measurement noise. Extensive simulations and experimental validations demonstrate the superiority of our uncertainty-aware model predictive control over traditional control strategies.

Distributed coordinated speed control of flywheel energy storage matrix systems with model uncertainties and disturbances

SummaryThis paper studies a coordinated rotor speed control of flywheel energy storage matrix systems (FESMS) in the presence of model uncertainties and unknown disturbances. We consider the scenarios that the torque variation during the operation of flywheel energy storage system (FESS) cannot be accurately measured and the coordinated operation of FESMS is realized without the measurements of accurate rotor speeds. We design a distributed extended state observer (ESO) only using local rotor position measurements to estimate the rotor speeds and unknown disturbances. Based on the estimated states, a coordinated control strategy with the compensation of model uncertainties and unknown disturbances is proposed. The proposed control strategy has the advantage of speed‐free feature and active disturbance rejection. By using Lyapunov stability theory, sufficient conditions are given to ensure that the FESMS can achieve the reference tracking. The performance of the control strategy is verified by simulations.

A practical leader–follower hybrid control scheme for wheeled mobile robots

This paper addresses the problem of tracking control of wheeled mobile robots in leader–follower formation. By applying the backstepping technique, a hybrid control scheme consisting of continuous control and intermittent control is proposed, and leader–follower formation tracking is achieved in the presence of model uncertainty and external disturbance. A relationship between the intermittent control width and control gain and the convergence of the position errors is obtained for the tracking accuracy estimation. As a special case, the concerned intermittent control can be converted to impulsive control with a sufficiently small control width, and tracking of the trajectory can be realized by directly driving the state variables of the follower at well-designed discrete instants. The main novelty of the proposed hybrid control scheme is to drive the tracking errors into a preset region via intermittent control (or impulsive control) signals and then achieve the tracking goal in the practical sense via continuous control signals. An example with the corresponding numerical simulations and experimental tests is given to illustrate the effectiveness of the proposed method.

Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation.

This article presents a neuroadaptive fault-tolerant control method for path tracking of multiinput multioutput (MIMO) systems in the presence of modeling uncertainties and external disturbances. In dealing with modeling uncertainties, neural networks (NNs) with diversified activation/basis functions are considered, with which we establish a set of control algorithms that are robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults. This is the first work that explicitly takes into account the neural weights uncertainties and activating function uncertainties in multiple layered neural networks in control design. In addition, we apply the developed control algorithms to unmanned ground vehicles (UGVs) with actuator failures. With the aid of Lyapunov stability theory, it is shown that the proposed control is able to drive the vehicle along the desired path with high precision and all the internal signals are uniformly ultimately bounded (UUB) and continuous. Both theoretical analysis and numerical simulation confirm the effectiveness of the designed strategy.

Extended State Kalman Filter-Based Model Predictive Control for Electro-Optical Tracking Systems with Disturbances: Design and Experimental Verification

A modified Extended State Kalman Filter (ESKF)-based Model Predictive Control (MPC) algorithm is introduced to tailor the enhanced disturbance suppression in electro-optical tracking systems. Traditional control techniques, although robust, often struggle in scenarios with concurrent internal, external disturbances, and sensor noise. The proposed algorithm effectively overcomes these limitations by precisely estimating system states and actively mitigating disturbances, thus significantly boosting noise and perturbation control resilience. The primary contributions of this study include the integration of ESKF for accurate system state and disturbance estimation in noisy environments, the embedding of an ESKF estimation-compensation loop to simulate an improved disturbance-free system, and a simplified modeling approach for the controlled device. This designed structure minimizes the reliance on extensive system identification, easing the predictive control model-based constraints. Moreover, the approach incorporates total disturbance estimation into the optimization problem, safeguarding against actuator damage and ensuring high tracking accuracy. Through rigorous simulations and experiments, the ESKF-based MPC has demonstrated enhanced model error tolerance and superior disturbance suppression capabilities. Comparative analyses under varying model parameters and external disturbances highlight its exceptional trajectory tracking performance, even in the presence of model uncertainties and external noise.

Collision-free automatic berthing of maritime autonomous surface ships via safety-certified active disturbance rejection control

This paper addresses the automatic berthing of a maritime autonomous surface ship operating in a confined water environment subject to static obstacles, dynamic obstacles, thruster constraints, and space constraints due to shorelines. A safety-certified active disturbance rejection control (ADRC) method is proposed for achieving the automatic berthing task of an MASS in the presence of model uncertainties and ocean disturbances. An extended state observer (ESO) based on a second-order robust exact differentiator (RED) is employed to estimate an extended state vector consisting of internal model uncertainties and external ocean disturbances. With the aid of the RED-based ESO, a nominal ADRC law is designed to achieve the position and heading stabilization. To avoid collisions with static obstacles, dynamic obstacles, and shorelines, input-to-state safe high-order control barrier functions are used to guarantee safety. Optimized control signals are obtained based on a constrained quadratic programming (QP) problem within safety constraints. In order to translate the control signals into the individual thruster command, a constrained QP problem is further used to search for optimized commands in real time. It is proven that the closed-loop automatic berthing system is input-to-state stable. By using the proposed method, the MASS is able to reach the desired position and heading with collision avoidance. Simulation results verify the effectiveness of the proposed safety-certified ADRC method for automatic berthing.

A disturbance rejection control for urban air mobility using artificial sensor-based Gaussian process regression

This paper presents a learning-based disturbance rejection control strategy for Urban Air Mobility (UAM) with vertical take-off and landing capability, which is subject to uncertainties in system parameters. The two primary sources of uncertainty during UAM operation, specifically moment of inertia uncertainty and center of gravity variation, are thoroughly analyzed as they negatively impact control performance. Building upon the analysis outcomes, a novel adaptive scheme is proposed that employs the modeling capabilities of Gaussian process regression for online learning to estimate model uncertainties. To ensure the collection of high-quality training data without relying on state derivatives, a nonlinear disturbance observer is employed as an artificial sensor. The suggested control algorithm is formulated by integrating Gaussian process regression with a baseline control derived using the feedback linearization control technique. Theoretical analysis grounded in the Lyapunov theorem reveals that the tracking error of the closed-loop system is semi-globally uniformly and ultimately bounded. Numerical simulations are conducted to validate the effectiveness of the proposed approach. The results obtained confirm that the proposed method can achieve superior tracking performance, even in the presence of model uncertainties and time-varying disturbances, surpassing existing approaches.

Fuzzy controller system utilization to increase the hydrogen production bioreactor capacity: toward sustainability and low carbon technology

Abstract The utilization of bio-hydrogen as a fuel source holds immense promise as a renewable energy option, offering compelling economic and environmental advantages. This study investigates the economic and environmental advantages of bio-hydrogen as a renewable energy source compared to fossil fuels, focusing on the reduction of greenhouse gas emissions such as carbon dioxide and carbon monoxide. The enhancement of anaerobic hydrogen production reactor capacity is explored through the application of a fuzzy controller system. Numerical simulations demonstrate that the fuzzy controller outperforms other methods in augmenting biological hydrogen production, effectively addressing the inherent non-linear characteristics of the system. In contrast, limitations in robustness against system uncertainty are observed with the non-linear controller. Exceptional tracking of desired values by the fuzzy controller, even in the presence of model uncertainty, results in a lower integral of time multiplied by squared error (ITSE) performance index compared to non-linear and proportional–integral controllers. Emphasizing the viability of the fuzzy method for regulating hydrogen production processes, potential gains of up to 95% in biological hydrogen production are indicated compared to open-loop configurations. This clean-burning fuel holds promise for industrial applications, contributing to the reduction of harmful gas emissions. The findings underscore the transformative potential of the fuzzy controller system in advancing sustainable hydrogen production and its significant role in addressing environmental concerns.

Robust Fractional Order PID Controller Coupled with a Nonlinear Filtered Smith Predictor for Solar Collector Fields

This work introduces a Fractional Order PID (FOPID) controller associated with a nonlinear Filtered Smith Predictor (FSP) to control a Solar Collector Field (SCF) system. The combined approach enhances the control performance by decoupling model uncertainties. First, the FOPID addresses linear model uncertainties through optimal robust design across the operational conditions, while the FSP compensator adeptly manages the system’s varying time delays. Rigorous tuning in the frequency domain considering closed-loop, robustness, and disturbance rejection performances is developed using a bi-level optimization approach to obtain the FOPID control parameters. Experimental results of the FOPID + nonlinear FSP demonstrate low rise time for reference tracking and robust stability across several operating conditions, even in the face of high-frequency disturbances in the solar irradiance due to passing clouds. Moreover, the nonlinear FSP effectively handles variations in time delays undergone by the water flow variation during the experiment. The achieved outcomes of the overall control structure, comprising the FOPID + FSP, demonstrate a promising approach for SCF system control, obtaining robust behavior in the presence of model uncertainties and disturbances while offering satisfactory reference tracking capabilities across an extensive range of plant operations.

Safety-Critical Receding-Horizon Planning and Formation Control of Autonomous Surface Vehicles via Collaborative Neurodynamic Optimization.

This article addresses the safety-critical receding-horizon planning and formation control of autonomous surface vehicles (ASVs) in the presence of model uncertainties, environmental disturbances, as well as stationary and moving obstacles. A three-level formation control architecture is proposed with a safety-critical formation trajectory generation module at its high level, a collision-free guidance module at its middle level, and an anti-disturbance control module at its low level. Specifically, a safety-critical formation trajectory generator is designed by leveraging collaborative neurodynamic optimization to plan safe formation trajectories to track a given trajectory and avoid stationary obstacles in a receding-horizon manner. Based on control barrier functions, a collision-free line-of-sight guidance law is developed to generate safe guidance commands to avoid collision with moving obstacles and other vehicles. An anti-disturbance control law is customized with a finite-time convergent observer for a vehicle to follow the guidance command signals. Simulation and hardware-in-the-loop experimental results are elaborated to validate the efficacy of the proposed method for the receding-horizon planning and formation control of ASVs.

Interacting Multiple Model Estimators for Fault Detection in a Magnetorheological Damper.

This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is subject to a filtering process. This paper applies various model-based filters in combination with the IMM strategy. One such estimator employs the recently introduced extended sliding innovation filter (ESIF) known as the IMM-ESIF. The ESIF is an extension of the sliding innovation filter for nonlinear systems based on the sliding mode concept. In the presence of modeling uncertainties, the ESIF has been proven to be more robust compared to methods such as the extended Kalman filter (EKF). The novel IMM-ESIF strategy is also compared with the IMM strategy, which incorporates the unscented Kalman filter (UKF), referred to herein as IMM-UKF. While EKF uses Taylor series approximation to linearize the system model, the UKF uses sigma point to calculate the system's mean and covariance. The methods were applied to an experimental magnetorheological (MR) damper setup, which was designed for testing control and estimation theory. Magnetorheological dampers exhibit a diverse array of applications in the automotive and aerospace sectors, with particular relevance to attenuating vibrations through adaptive suspension systems. Applied to a magnetorheological (MR) damper with distinct operating modes determined by the damper's current, the results showcase the effectiveness of IMM-ESIF. In mixed operational conditions, IMM-ESIF demonstrates a notable 80% to 90% reduction in estimation error compared to its counterparts. Furthermore, it exhibits a 4% to 5% enhancement in correctly classifying operational modes, establishing IMM-ESIF as a promising and efficient alternative for adaptive estimation in electromechanical systems. The improved accuracy in estimating the system's behaviour, even amidst uncertainties and mixed operational scenarios, signifies the potential of IMM-ESIF to significantly enhance the overall robustness and efficiency of estimations.

Robust trajectory tracking of quadrotors using adaptive radial basis function network compensation control

Radial Basis Function Neural Networks (RBFNN) methods have gained incredible efficiency and applicability in control. This paper presents a nested control strategy for robust trajectory tracking of a quadrotor using adaptive RBF compensation and NN-supervised control embedded with Integrator BackStepping (IBS). The approach addresses the robustness in the presence of modeling uncertainties, sensing noise, and bounded disturbances. The control design is derived from the decentralized inverse dynamics, using adaptive RBFNN for outer-loop disturbance approximation and compensation. In conjunction with an Inner-loop supervised control that stabilizes the quadrotor attitude, preventing initial instability during NN convergence. In addition, an adaptive Extended Kalman Filter (EKF) attenuates noisy signals. Simulation results demonstrate strong adaptability to changes in model parameters, and superior performance when compared to Proportional Integral Derivative (PID), Integrator BackStepping (IBS), and offline decentralized Multi-Layer Perceptron (MLP) algorithms, in terms of parameter convergence, disturbance compensation control, and noise attenuation.