Presence Of Model Uncertainties Research Articles

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.

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.

A novel output redefinition method for nonlinear H∞ attitude stabilization of a flexible spacecraft

AbstractThis paper addresses a novel output redefinition method for the nonlinear attitude stabilization problem of a flexible spacecraft. As an idea, the flexible appendage tip‐point can be selected as attitude output which may improve attitude pointing accuracy. However, it makes the system non‐minimum phase. The output redefinition method is an effective method to make the system minimum phase. In this paper, a novel output redefinition is proposed to overcome the non‐minimum phase property such that it improves the attitude control system performance. Then, a nonlinear controller is designed based on back‐stepping approach and a modal observer. The simulation results show the effectiveness of the proposed method in the presence of model uncertainties and environmental disturbances.

Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model

In this paper we propose a robust Model Predictive Control where a Gated Recurrent Unit network model is used to learn the input–output dynamics of the system under control. Robust satisfaction of input and output constraints and recursive feasibility in presence of model uncertainties are achieved using a constraint tightening approach. Moreover, new terminal cost and terminal set are introduced in the Model Predictive Control formulation to guarantee Input-to-State Stability of the closed loop system with respect to the uncertainty term.

Robust high‐gain observer‐based sliding mode controller for pitch and yaw position control of an AUV

AbstractThis article addresses a trajectory tracking control problem concerning an autonomous underwater vehicle's pitch and yaw channel dynamics in the presence of model uncertainties, underwater disturbances, and input saturation. Three different observers are introduced to estimate unknown state variables: a Luenberger‐type cubic observer, a sliding mode observer, and a high‐gain observer (HGO). Initially, a backstepping controller is employed to tackle the tracking problem, extending it to incorporate backstepping sliding mode control (SMC). The mentioned observers are utilized in both aspects of the controller design. Our proposed control law assesses trajectory tracking performance by introducing virtual control inputs, with the sliding surface designed to guide the current state variables toward approximating the virtual state variables. By combining backstepping and SMC, ensure that the state variables of the closed‐loop system converge to the desired state using the HGO. A rigorous analysis is incorporated to validate the robust performance of our proposed control law under conditions of model uncertainties and underwater disturbances. Furthermore, the control law is extended for anti‐windup compensation, mitigating adverse effects on stern and rudder plane saturation levels. Lyapunov stability theory is adopted to establish the stability of the closed‐loop system. Our simulation results convincingly demonstrate the effectiveness of the HGO‐based backstepping SMC law compared to alternative control approaches.

A weather optimal heading-based incremental feedback control for dynamic positioning of ships

In this brief, the weather optimal heading-based incremental feedback control algorithm is built for dynamic positioning (DP) of ships in the presence of model uncertainties and unknown environmental disturbances induced by wind, waves and currents. The proposed control algorithm has two parts, i.e., the weather optimal heading portion and the control portion. An improved ZPC-W approach with the deviation of the lateral control force is designed in order to enhance the convergence rate of the desired weather optimal heading and avoid overshoot as well. In addition, based on the iterative sliding mode function, a concise incremental feedback control scheme is designed with the pitch ratio of the thruster as the control input. It is measurable in the practical plant and has nothing to do with ship models with good stability and strong robustness. Lastly, simulation studies are performed to illustrate the performance and the superiorities of the proposed control algorithm.

Aggregate Modeling of Thermostatically Controlled Loads for Microgrid Energy Management Systems

Second-to-second renewable power fluctuations can severely hinder the frequency regulation performance of modern isolated microgrids, as these typically have a low inertia and significant renewable energy integration. In this context, the present paper studies the coordinated control of Thermostatically Controlled Loads (TCLs) for managing short-term power imbalances, and their integration in microgrid operations through the use of aggregate TCL models. In particular, two computationally efficient and accurate aggregate TCL models are developed: a virtual battery model representing the aggregate flexibility of TCLs considering solar irradiance heat gains and wall/floor heat transfers, and a frequency transient model representing the aggregate dynamics of a TCL collection considering communication delays and the presence of model uncertainty and time-variability. The proposed aggregate TCL models are then used to design a practical Energy Management System (EMS) integrating TCL flexibility, and study the impact of TCL integration on microgrid operation and frequency control. Computational experiments using detailed frequency transient and thermal dynamic models are presented, demonstrating the accuracy of the proposed aggregate TCL models, as well as the economic and reliability benefits resulting from using these aggregate models to integrate TCLs in microgrid operations.

Nonlinear trajectory estimation using extended state-space recursive least squares.

In a variety of filtering applications including target tracking, global positioning systems, and autonomous robots, the nonlinear nature of the system model makes estimation tasks challenging. This study presents an extended state-space recursive least squares (ESSRLS) filter for nonlinear trajectory estimation. The paper focuses on ESSRLS filter derivation for non-autonomous systems and investigates its performance against the extended Kalman filter and unscented Kalman filter for maneuvering aircraft trajectory estimation applications. The major accomplishment of the proposed approach is nonlinear filtering, independent of a priori information about noise statistics and the provision of the tuning parameter (forgetting factor). This makes the ESSRLS a more suited candidate for practical nonlinear filtering applications. The simulation results show that in the presence of model uncertainties, data outages (occlusion), and large initial condition deviations, the proposed method gives superior estimation performance as compared to the state-of-the-art.

Extended Sliding Mode Observer-Based Output Feedback Control for Motion Tracking of Electro-Hydrostatic Actuators

This paper develops a novel output feedback control scheme for the motion-tracking problem of an electro-hydrostatic actuator (EHA) in the presence of model uncertainties and external disturbances. Firstly, a simplified third-order system model of the studied EHA is established using theoretical methods. For the first time, an extended sliding mode observer (ESMO) is introduced to simultaneously account for the shortage of unknown system states and modeling imperfections. Based on this, a robust nonlinear controller is developed using the backstepping control framework to stabilize the closed-loop system. This controller integrates estimates of immeasurable system states and lumped disturbances to deal with their adverse impacts. Moreover, the dynamic surface control (DSC) technique is employed to effectively mitigate the computational burden of the traditional backstepping framework. An ultimately uniformly bounded (UUB) performance is assured by using the recommended method. Furthermore, the stability of not only the observer but also the closed-loop system is concretely analyzed by using the Lyapunov theory. Finally, experiment results under various working scenarios are given to convincingly demonstrate the advantage of the suggested method in comparison with some reference control approaches.

Adaptive Threshold-based Fault Detection for Systems Exposed to Model Uncertainty and Deterministic Disturbance

The fault detection problem is investigated for discrete-time linear uncertain systems. Instead of designing a fault detection system from the viewpoint of observer design for robust residual generation, an adaptive threshold approach is proposed to attain robustness against disturbance and norm-bounded model uncertainty. The main goal of the research is to develop a threshold design method that could establish an appropriate trade-off between false alarms and missed fault detection in the presence of model uncertainty. For this purpose, the H∞ optimization technique is adopted in the linear matrix inequality framework to compute the unknown parameters of an adaptive threshold. It is shown that the proposed fault detection system based on an adaptive threshold depends only on the system parameters and the control input of the monitored system. It is independent of robust residual generator designs in traditional observer-based fault detection systems. The effectiveness of the proposed approach is verified on two well-known benchmark systems: a direct-current motor and three tank systems. Several types of faults are successfully detected in both applications.

Tac2Pose: Tactile object pose estimation from the first touch

In this paper, we present Tac2Pose, an object-specific approach to tactile pose estimation from the first touch for known objects. Given the object geometry, we learn a tailored perception model in simulation that estimates a probability distribution over possible object poses given a tactile observation. To do so, we simulate the contact shapes that a dense set of object poses would produce on the sensor. Then, given a new contact shape obtained from the sensor, we match it against the pre-computed set using an object-specific embedding learned using contrastive learning. We obtain contact shapes from the sensor with an object-agnostic calibration step that maps RGB (red, green, blue) tactile observations to binary contact shapes. This mapping, which can be reused across object and sensor instances, is the only step trained with real sensor data. This results in a perception model that localizes objects from the first real tactile observation. Importantly, it produces pose distributions and can incorporate additional pose constraints coming from other perception systems, multiple contacts, or priors. We provide quantitative results for 20 objects. Tac2Pose provides high accuracy pose estimations from distinctive tactile observations while regressing meaningful pose distributions to account for those contact shapes that could result from different object poses. We extend and test Tac2Pose in multi-contact scenarios where two tactile sensors are simultaneously in contact with the object, as during a grasp with a parallel jaw gripper. We further show that when the output pose distribution is filtered with a prior on the object pose, Tac2Pose is often able to improve significantly on the prior. This suggests synergistic use of Tac2Pose with additional sensing modalities (e.g., vision) even in cases where the tactile observation from a grasp is not sufficiently discriminative. Given a coarse estimate of an object’s pose, even ambiguous contacts can be used to determine an object’s pose precisely. We also test Tac2Pose on object models reconstructed from a 3D scanner, to evaluate the robustness to uncertainty in the object model. We show that even in the presence of model uncertainty, Tac2Pose is able to achieve fine accuracy comparable to when the object model is the manufacturer’s CAD (computer-aided design) model. Finally, we demonstrate the advantages of Tac2Pose compared with three baseline methods for tactile pose estimation: directly regressing the object pose with a neural network, matching an observed contact to a set of possible contacts using a standard classification neural network, and direct pixel comparison of an observed contact with a set of possible contacts. Website: mcube.mit.edu/research/tac2pose.html

A Deep Reinforcement Learning-Based Path-Following Control Scheme for an Uncertain Under-Actuated Autonomous Marine Vehicle

In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. By virtue of light-of-sight guidance, a surge-heading joint guidance method is developed within the kinematic level, thereby enabling the AMV to follow the desired path accurately. Within the dynamic level, model uncertainties and time-varying environment disturbances are taken into account, and the reinforcement learning control method using the twin-delay deep deterministic policy gradient (TD3) is developed for the under-actuated vehicle, where path-following actions are generated via the state space and hybrid rewards. Additionally, actor-critic networks are developed using the long-short time memory (LSTM) network, and the vehicle can successfully make a decision by the aid of historical states, thus enhancing the convergence rate of dynamic controllers. Simulation results and comprehensive comparisons on a prototype AMV demonstrate the remarkable effectiveness and superiority of the proposed LSTM-TD3-based path-following control scheme.

Disturbance observer-based adaptive neural control for underactuated surface vehicle with constraint of input saturation

In this paper, a robust adaptive neural control algorithm is provided to solve the problem of path-following control for underactuated surface vehicle (USV) subject to input saturation, in the presence of model uncertainties and unknown marine environmental disturbance.In the algorithm, through the application of neural network (NN), an adaptive disturbance observer (ADO) is proposed to observe the unknown disturbance, which does not need the precise information of the upper bound of the disturbance, and the model uncertainty can be appropriated simultaneously. Specially, since the ADO and control law share the same set of NN, the number of adaptive parameters of the controller can be greatly reduced. After that, dynamic surface control (DSC) method is used to avoid repeated derivative in the process of back-stepping, which solve the “calculation explosion” problem. An auxiliary system is designed to compensate errors caused by input saturation, which solve the input saturation problem of actuators. Through Lyapunov stability analysis, it is proved that all signals of the closed-loop system are semi-globally ultimately uniformly bounded (SGUUB). Finally, some experiments are conducted to demonstrate the effectiveness of the algorithm.

Combined Fault Diagnosis Scheme Based on Recurrent Neural Network and H− Observer for Satellite Attitude Control System

This study proposes a combined fault diagnosis scheme based on a recurrent neural network (RNN) and an H− observer for satellite attitude control systems (ACSs) in the presence of model uncertainties, external disturbances, and measurement noise. The ACS is decoupled into multiple independent channels, such that the residuals generated by the observers respond to the corresponding faults. A novel multilayer adaptive Gaussian recurrent neural network (MAGRNN) structure is developed as an approximator to estimate the lumped disturbance; a robust term is introduced to improve accuracy. Considering the actuator fault in the finite frequency domain and the fault-sensitive index, a set of H− unknown input observers (UIOs) is designed using the output of the MAGRNN-based approximator as compensation. The existence conditions of the approximator and observer are proposed and proved. The fault diagnosis results for three cases verify that the proposed method can be used for small fault detection and isolation.