Presence Of Model Uncertainties Research Articles

Robust trajectory tracking of an uncertain quadrotor with almost global finite-time convergence

This paper addresses the three-dimensional trajectory tracking control problem of quadrotor unmanned aerial vehicles (UAVs) in the presence of modeling uncertainties and external disturbances. We propose two control strategies that steer the quadrotor onto its desired path within a finite time and maintain it thereafter for all future times. The proposed strategies use a cascaded control architecture, where the inner loop consists of the attitude dynamics and the outer loop consists of the translational dynamics of the quadrotor. The proposed strategies guarantee the global finite-time convergence of the tracking errors. Consequently, the quadrotor tracks its desired trajectory globally regardless of its initial engagement geometry with respect to the path. The first strategy draws motivation from the terminal sliding mode control. Meanwhile, in the second strategy, we modify the reaching law using an exponential function, which dynamically adapts to the variation of system states and guarantees a shorter reaching time with improved tracking performance. We demonstrate the robust performance of the proposed strategy in the presence of system lag and bounded external disturbances, which may emulate the effect of wind waves and other environmental imperfections neglected during the controller design.

Robust Adaptive Control of a Coaxial-Ducted-Fan Aircraft with Uncertainty Model

This paper focuses on the robust adaptive control of a coaxial-ducted-fan aircraft under the context of significant system uncertainty. To ensure accurate tracking even in the presence of model uncertainties and disturbances for a ducted-fan flight platform, a model reference adaptive control architecture is proposed in this paper on the basis of control augmentation. With the nominal closed-loop system as the reference system, this architecture overlays adaptive control inputs on top of the linear time-invariant control inputs to compensate for uncertainties in real time. This allows the reference system to be tracked rapidly and accurately by the output of the uncertain system, which ensures that the performance of the closed-loop system meets design requirements and that the flight envelope of the prototype is further expanded. The superiority of the proposed method is demonstrated by numerical simulation of the ducted-fan aircraft. The simulation results show that the proposed robust adaptive control algorithm is advantageous over the baseline control in rapidly compensating for the closed-loop performance of the system, even in the presence of a more severe disturbance. At the same time, the performance of control algorithms are verified by conducting flight experiments. The results show that when the prototype undergoes low-speed slow-change maneuvering, there is little difference in the tracking performance of the four channels of the two control strategies. However, when the prototype undergoes high-speed fast-change maneuvering, the root mean square (RMS) values of the tracking errors of the robust adaptive control and the baseline control are 0.749 m/s and 1.039 m/s, respectively, and the robust adaptive algorithm reduces the tracking error by about 38% on the basis of the baseline control.

Cooperative Robust Output Regulation for Networks of Hyperbolic Systems With Unknown Signal Models

ABSTRACTThis Paper considers the cooperative robust output regulation problem for networks of heterodirectional hyperbolic systems, where the leader and disturbance dynamics are unknown to the followers. For this, a diffusively driven internal model is used, whose parameters are updated using an adaptive cooperative observer. The latter only communicates the coefficients of the characteristic polynomials related to the signal models. Hence, a minimal communication load is ensured, as, in addition, only the control inputs of the regulator have to be exchanged through the network. The adaptive internal model also ensures cooperative output regulation in the presence of model uncertainties, that do not destabilize the closed‐loop system. A systematic backstepping approach is presented for stabilizing the uncertain closed‐loop system. For this, solvability conditions are derived in terms of the agents transfer behaviour and the network topology. The presented adaptive regulator is validated in simulations for a network of three uncertain hyperbolic agents.

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.

Neuromorphic robust estimation of nonlinear dynamical systems applied to satellite rendezvous

Neuromorphic robust estimation of nonlinear dynamical systems applied to satellite rendezvous

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.

Output feedback control of anti‐linear systems using adaptive dynamic programming

AbstractThis paper introduces an adaptive optimal feedback control approach for discrete‐time anti‐linear systems (ALSs). The method utilizes sampling and measurable input–output data. By employing the Adaptive Dynamic Programming (ADP) technique, this study iteratively solves the discrete‐time algebraic Anti‐Riccati equation (AARE). Initially, an output feedback model is established for ALSs, and a model‐based algorithm is developed based on this model. The feasibility of this algorithm is based on the premise that the system dynamic information is completely known. Subsequently, for the scenario where the model is unknown, we further developed a model‐free ADP algorithm specifically designed to address optimal control problems in the presence of model uncertainty. With this algorithm, we achieve effective control optimization even in cases where detailed system dynamics information is lacking. Finally, through simulation experiments, we validated the feasibility and effectiveness of this algorithm.

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

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

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

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

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

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

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

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

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

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

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.