Presence Of Model Uncertainties Research Articles

Decoupled inputs sliding mode controllers for a fuel cell-supercapacitor module in hybrid generation applications

The development of Multiple Input/Multiple Output (MIMO) sliding mode control setups for a fuel cell/supercapacitor module is presented in this paper. The main objective of the proposed controllers consists in simultaneously satisfying the demand and regulating the DC bus voltage, even in the presence of model uncertainties and strongly varying operating conditions. Two design approaches are utilized to synthetise different control setups, each one capable to robustly deal with such control challenges: on one hand, variable-gains first-order sliding mode and, on the other, supert-wisting second-order sliding mode control. The stability of the nonlinear controlled system is formally analysed. Extensive simulations are conducted, to comparatively assess the performance of the proposed MIMO sliding mode controllers. Both control setups exhibited highly satisfactory results, demonstrating robustness to external disturbances and parameter variations, proving to be more suitable than classic linear PID controllers.

Identification and control design for a class of non-minimum phase dead-time systems based on fractional-order Smith predictor and genetic algorithm technique

The design of Smith predictor (SP) controllers has been largely investigated in the literature to solve fractional-order dead-time systems control problems; this is due to its robustness in presence of modeling uncertainties and measurement noises. In this paper, a new SP control strategy is proposed for a special class of non-minimum phase dead-time systems. This controller can be designed only by imposing a new fractional-order model and new robust controller in the modeling and synthesis stages. A fractional multi-low-order dead-time (FMLODT) model is accordingly proposed in the SP modeling stage, where an actual process behavior is well ensured by high model accuracy. The proposed fractional model parameters are identified by an adequate optimization tool based genetic algorithm. Furthermore, a robust fractional order controller based FMLODT model is synthesized, its structure is analytically determined through a three terms fractional-order reference (TTFOR) model. The proposed fractional controller parameters are tuned according to some guidelines available in the literature. Thus, two main contributions are proposed in this work: the first one consists of introducing a general framework for the SP modeling scheme. This is ensured by the proposed FMLODT model, which enhances significantly the modeling accuracy compared to the existing models in the literature. The second innovation consists of proposing a general framework for the controller design step in the SP scheme. This is guaranteed by the proposed TTFOR model, which improves significantly the trade-off between the two contradictory goals: performance and robustness. To highlight the proposed strategy of good performances, both the new fractional order model and the classical controller are applied on the minimum phase dead-time systems. The obtained simulation results illustrate the performance and improvement of the proposed model comparatively to preceding works.

Robust Feedback-Linearization Technique for Grid-Tied LCL Filter Systems Using Disturbance Estimation

In this paper, feedback linearization (FBL) technique together with disturbance observer (DO) approach is proposed to mitigate the effect of the resonant frequency of grid-tied LCL filter systems. The state-feedback control law is employed to achieve stabilization of the LCL filter system under a wide range of resonant frequency variation. The DO is designed to counteract the effect of model uncertainty and unknown disturbance aiming to achieve asymptotic stability under FBL control. Specifically, the observer is designed to estimate an additional input, representing uncertainty and unknown disturbance, from measurable variables. Then, the FBL control utilizes the disturbance estimate to compensate for its effect. An interesting feature of the composite controller is its ability to meet the transient response specifications even in the presence of model uncertainty and external disturbance. The composite controller was implemented for simulation and experimental evaluation, and performance testing. High performance with respect to disturbance rejection and parameter variation has been demonstrated.

Design an impedance control strategy for a teleoperation system to perform drilling process during spinal surgery

This work proposes a novel impedance control strategy for a delayed bilateral tele-surgery system to perform a drilling process during spinal surgery. In the new designed control scheme, regarding a desired impedance model for master and slave robot, an especial dynamic characteristic at the surgeon and master as well as slave and vertebra interface is designed. Two desired impedance models are proposed for the master and slave robots such that: (a) the salve robot that holds the drilling device should track the master path but complies with the reaction force of the vertebra, and (b) the surgeon should receive feedback from the slave-vertebra interaction force via the master robot. These main objectives are attained by proper adjustment in the proposed impedance model, which does not require any direct measurement of vertebra reflections. Then, the impedance model is put into a proper sliding mode controller to cope with the modeling uncertainties in the slave side. Consequently, the absolute stability concept is utilized to investigate closed-loop system stability and transparency. Finally, the control scheme is implemented on one degree of freedom robotic manipulators as master and slave robot. Experimental results demonstrate the efficiency of the designed impedance control scheme in the presence of modeling uncertainties.

Adaptive Sliding Mode Trajectory Tracking Control for Unmanned Surface Vehicle with Modeling Uncertainties and Input Saturation

In the presence of modeling uncertainties and input saturation, this paper proposes a practical adaptive sliding mode control scheme for an underactuated unmanned surface vehicle (USV) using neural network, auxiliary dynamic system, sliding mode control and backstepping technique. First, the radial basis function neural network with minimum learning parameter method (MLP) is constructed to online approximate the uncertain system dynamics, which uses single parameter instead of all weights online learning, leading to a reduction in the computational burdens. Then a hyperbolic tangent function is adopted to reduce the chattering phenomenon due to the sliding mode surface. Meanwhile, the auxiliary dynamic system and the adaptive technology are employed to handle input saturation and unknown disturbances, respectively. In addition, a neural shunting model is introduced to eliminate the “explosion of complexity” problem caused by the backstepping method for virtual control derivation. The stability of the closed-loop system is guaranteed by the Lyapunov stability theory. Finally, simulations are provided to validate the effectiveness of the proposed control scheme.

A coordinate descent approach to optimal tracking time allocation in point-to-point ILC

Iterative learning control (ILC) is designed for applications involving multiple executions of the same task. Existing work has applied ILC to point-to-point motion tasks, but has not fully exploited its design freedom to optimize performance criteria other than the tracking accuracy. This paper extends the task description of the point-to-point ILC framework for discrete-time systems by considering the tracking time instants of desired positions as changing variables (i.e. the temporal location of each position can vary). This extension allows the optimization of an additional performance index while maintaining the tracking accuracy. This optimization problem is solved using a two stage design framework, and an iterative algorithm consisting of a norm optimal ILC update and a coordinate descent approach is then derived to minimize an additional performance index, e.g. control effort, for the point-to-point motion tasks. This algorithm is tested on a gantry robot to verify its effectiveness in the presence of model uncertainty and disturbances.

Quadrotor Fault Tolerant Incremental Sliding Mode Control driven by Sliding Mode Disturbance Observers

This paper proposes an Incremental Sliding Mode Control driven by Sliding Mode Disturbance Observers (INDI-SMC/SMDO), with application to a quadrotor fault tolerant control problem. By designing the SMC/SMDO based on the control structure of the sensor-based Incremental Nonlinear Dynamic Inversion (INDI), instead of the model-based Nonlinear Dynamic Inversion (NDI) in the literature, the model dependency of the controller and the uncertainties in the closed-loop system are simultaneously reduced. This allows INDI-SMC/SMDO to passively resist a wider variety of faults and external disturbances using continuous control inputs with lower control and observer gains. When applied to a quadrotor, both numerical simulations and real-world flight tests demonstrate that INDI based SMC/SMDO has better performance and robustness over NDI based SMC/SMDO, in the presence of model uncertainties, wind disturbances, and sudden actuator faults. Moreover, the implementation process is simplified because of the reduced model dependency and smaller uncertainty variations of INDI-SMC/SMDO. Therefore, the proposed control method can be easily implemented to improve the performance and survivability of quadrotors in real life.

Distributed Detection of Weak Signals From One-Bit Measurements Under Observation Model Uncertainties

We consider the distributed detection of weak signals from one-bit measurements collected by a sensor network where observation model uncertainties exist at all the sensor nodes. To solve this problem, a one-bit locally most powerful test (LMPT) detector is proposed in this letter. Moreover, asymptotically optimal one-bit quantizers at all the sensor nodes are designed for the proposed one-bit LMPT detector. In this letter, model uncertainties are interpreted as multiplicative noise and its variance represents the strength of model uncertainties. Theoretical analysis indicates that, when the strength of model uncertainties is finite, the proposed detector using one-bit data with $\pi N/2$ sensors approximately achieves the same detection performance as the clairvoyant detector that directly uses analog measurements with $N$ sensors. Simulation results corroborate our theoretical analysis and show that, compared to the one-bit generalized likelihood ratio test detector, the proposed one-bit LMPT detector provides better detection performance in the presence of model uncertainties.

Sliding mode predictive tracking control for uncertain Steer-by-Wire system

The Steer-by-Wire (SbW) system is probably the most significant innovation among drive-by-Wire technologies in the automotive industry. Without the mechanical link, the most challenging issue is to control the wheels to closely follow the driver’s command. To improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances in the steering control processes, a sliding mode predictive tracking control (SMPC) strategy for a SbW system with uncertain dynamics is proposed. The simulation and experimental results demonstrate that the performance of the proposed SMPC tracking controller is superior to both SMC and MPC methods for the steering angle tracking task.

Optimization-based nonlinear control laws with increased robustness for trajectory tracking of non-holonomic wheeled mobile robots

This paper presents the integration of kinematic and dynamic trajectory tracking control problem for non-holonomic wheeled mobile robots (WMRs) in the presence of model uncertainties and disturbances. The non-holonomic constraints, inherent nonlinearities and model uncertainties of WMRs are the main difficulties arising in trajectory tracking control for these systems. To address this problem, after modeling of a WMR incorporating wheel actuators dynamics, optimization-based nonlinear control laws are analytically developed for the kinematic and dynamic based controllers using the prediction of WMR responses. The tracking precision is more increased, under disturbances and uncertainties by appending the integral feedback technique to the design method. The performed analyses as well as the simulation results show that the tracking errors are remarkably decreased by the proposed control system in the presence of model uncertainties and disturbances. Finally, the effectiveness of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulation results.

Improved decentralized finite-time formation control of underactuated USVs via a novel disturbance observer

This note investigates the decentralized finite-time formation control of underactuated unmanned surface vessels (USVs) in the presence of model uncertainty and environmental disturbance. In the algorithm, a novel adaptive finite-time disturbance observer (AFTDO) is incorporated into the proposed control strategy that enhances its robustness to the environmental disturbance. By fusion of neural network (NN) and minimal learning parameterization (MLP) techniques, the AFTDO is constructed without priori information about the ship model and the upper bound of disturbance. On the basis of undirected graph, the decentralized control law is developed with local information from neighboring USVs. Specially, since the AFTDO and the decentralized control law share the same set of NN, the whole algorithm is with merits of concise form and less adaptive parameters. Such design contributes to smaller computation load and facilitates the implementation of the algorithm in ocean engineering. The semi global finite-time uniformly boundedness (SGFTUB) of the closed-loop system is proved by the Lyapunov theory. Numerical simulations and comprehensive comparisons are conducted to demonstrate the remarkable performance and superiority of the proposed algorithm.

Backstepping Trajectory Tracking Based on Fuzzy Sliding Mode Control for Differential Mobile Robots

Differential wheeled mobile robot (DWMR) is a typical nonholonomic complex system with the practical importance and theoretically interesting properties. A novel backstepping & fuzzy sliding mode controller (BFSMC) is proposed for trajectory tracking of the DWMR in the presence of model uncertainties and external disturbances. Backstepping control technique is used to eliminate the pose deviations of the mobile robot based on the kinematic model. Sliding mode control is adopted at the dynamic level for velocity tracking of the driving wheels, in which the gain of switching control is adjusted adaptively by means of fuzzy logic inference, in order to mitigate the chattering problem. The tracking error convergence of the BFSMC is demonstrated by means of the Lyapunov stability criteria. Numerical simulation shows that the BFSMC has the better accuracy, rapidity, smoothness and robustness, when compared to the conventional SMC. A vision-guided mobile robot with an onboard camera is developed for the experiment of path tracking. The experimental results further validate the feasibility and effectiveness of the BFSMC.

Observer-based fractional-order adaptive type-2 fuzzy backstepping control of uncertain nonlinear MIMO systems with unknown dead-zone

A new problem of observer-based fractional adaptive type-2 fuzzy backstepping control for a class of fractional-order MIMO nonlinear dynamic systems with dead-zone input nonlinearity is considered in the presence of model uncertainties and external disturbances where the control scheme is constructed by combining the backstepping dynamic surface control (DSC) and fractional adaptive type-2 fuzzy technique. First, a linear state observer estimates immeasurable states. Second, the unknown nonlinear functions of the uncertain system are approximated with interval type-2 fuzzy logic systems. Third, to avoid the complication of backstepping design process, the DSC is used. Fourth, by using the fractional adaptive backstepping, fractional adaptive laws are constructed, the proposed method is applied to a class of uncertain fractional-order nonlinear MIMO system. In order to have a better control performance in reducing tracking error, the controller parameters are tuned by using the PSO algorithm. Stability of the system is proven by the Mittag-Leffler method. It is presented that the proposed design guarantees the boundedness property for the system and also the tracking error can converge to a small neighborhood of the zero. The simulation examples are given to show the efficiency of the proposed controller.

Station-keeping Control of an Underactuated Stratospheric Airship

This paper studies the station-keeping control of underactuated stratospheric airships in the presence of model uncertainties and wind field, and a T–S fuzzy model-based adaptive backstepping SMC (sliding mode controller) is proposed. Firstly, a fuzzy dynamics model is constructed to represent the local dynamic behaviors of the given 6-DOF nonlinear dynamics of an airship. And different from the traditional algorithm, the station-keeping control is resorted to path-following control. Then, the guidance-based path-following principle is adopted to obtain the guidance law, and the backstepping technique is used to obtain the desired velocities. In order to solve the problem of model uncertainties between T–S fuzzy model and nominal model, the sliding mode control approach is adopted. Adaptive terms are designed to estimate the upper bound of the uncertainties. Besides, a wind field observer is designed to estimate the speed and direction of the wind. The stabilization of the system is discussed using Lyapunov stability theory. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed method.

Consensus Based Strong Tracking Adaptive Cubature Kalman Filtering for Nonlinear System Distributed Estimation

The distributed cubature Kalman filter is widely used in the field of target tracking, however, the presence of model uncertainties will undermine its tracking stability and effectiveness for tracking maneuvering target. In order to eliminate this effect on maneuvering target tracking, this paper develops a distributed consensus based strong tracking cubature Kalman filter scheme. First, each node obtains its local estimation with the usage of local observations via strong tracking cubature Kalman filter, where the suboptimal fading factor and adaptive factor are introduced for adaptively modifying the filter gain. Then, the designed filter gain is used for updating the local state estimation. Second, after all, nodes have achieved its local estimation, each node exchanges its local estimation to its neighbors and updates its local estimation according to the consensus communication protocol. It can be further proved that the distributed interaction between neighbors will contribute to enhancing the tracking stability. The detailed proof for stochastic boundedness of the estimation error is analyzed by introducing a stochastic process. Simulation results demonstrate that the proposed algorithm can achieve higher tracking accuracy than the existing methods for tracking a maneuvering target.

Disturbance-Observer-Based Prescribed Performance Fault-Tolerant Trajectory Tracking Control for Ocean Bottom Flying Node

The development of marine petroleum exploitation becomes an important part of marine economy development gradually. Ocean bottom flying node (OBFN) is a kind of autonomous underwater vehicle which can explore marine petroleum and natural gas in large-scale deployment. This paper investigates the prescribed performance fault-tolerant trajectory tracking control method for an OBFN in presence of modeling uncertainties, ocean current disturbances, and thruster faults. By using a new performance function and an error transformation scheme, the proposed controller is able to achieve the desired tracking performance and the maximum convergence time is known as a priori. A disturbance observer is designed to deal with the influence of the modeling uncertainties, ocean disturbances, and thruster faults. The stability of the closed-loop OBFN system is proved by using Lyapunov theory. Finally, simulation results under different types of thruster faults illustrate the effectiveness of the proposed disturbance-observer-based prescribed performance fault-tolerant trajectory tracking controller.

Diving Adaptive Position Tracking Control for Underwater Vehicles

This paper presents a robust position tracking control scheme for underwater vehicles moving in a vertical plane. The idea comes from the demand of underwater position tracking control for the newly borne Trans-media Aerial Underwater Vehicle (TMAUV). Although position control of a TMAUV is still within the scope of autonomous underwater vehicles (AUVs) control, it has new features. An underwater reference path for the TMAUV could be characterized by a strong maneuver that many assumptions in the conventional AUV controller design could not be employed. In this paper, a Lyapunov-based backstepping controller is developed for a nonlinear coupled input system releasing all constraints on the pitch angle, heave velocity, and angular velocity. Furthermore, neural networks and parameter estimation are employed to develop a robust controller in the presence of model uncertainties, parameter uncertainties, and external disturbances. This paper also solves the problem of adaptive estimation for the system parameters under the coupled input condition. Simulations are presented to demonstrate the feasibility and effectiveness.

Combined Quaternion-Based Error State Kalman Filtering and Smooth Variable Structure Filtering for Robust Attitude Estimation

This paper presents a novel robust quaternion-based error state Kalman filter (ESKF) for coping with modeling uncertainty in inertial measurement unit (IMU)-based attitude estimation. The smooth variable structure filter (SVSF) has recently been proposed and proven to be robust to modeling uncertainty. In an effort to combine the accuracy of an ESKF with the robustness of the SVSF, the ESKF and SVSF algorithms have been merged to create the ESKF-SVSF algorithm. In particular, a comprehensive fault detection strategy has been proposed to combine the optimality of the ESKF and the robustness of the SVSF. The proposed ESKF-SVSF algorithm was validated on experimental data collected from a small unmanned aerial vehicle (UAV) in the presence of faulty gyroscope signals. In the experiment, four faulty test cases were consideblack, involving the injection of two types of faults into the raw gyroscope signals to simulate modeling uncertainty. Although the proposed ESKF-SVSF algorithm incurs a slightly increased computational load, the experimental results demonstrate that the proposed algorithm yields more accurate attitude estimates than the conventional approach does in the presence of modeling uncertainty.

Implicit Euler Implementation of Twisting Controller and Super-Twisting Observer without Numerical Chattering: Precise Quasi-Static MEMS Mirrors Control

The quasi-static operations of MEMS mirror are very sensitive to undesired oscillations due to its very low damping. It has been shown that closed-loop control can be superior to reduce those oscillations than open-loop control in the literature. For the closed-loop control, the conventional way of implementing sliding mode control (SMC) algorithm is forward Euler method, which results in numerical chattering in the control input and output. This paper proposes an implicit Euler implementation scheme of super twisting observer and twisting control for a commercial MEMS mirror actuated by an electrostatic staggered vertical comb (SVC) drive structure. The famous super-twisting algorithm is used as an observer and twisting SMC is used as a controller. Both are discretized by an implicit Euler integration method, and their implementation algorithms are provided. Simulations verify that, as compared to traditional sliding mode control implementation, the proposed scheme reduces the chattering both in trajectory tracking output and control input in presence of model uncertainties and external disturbances. The comparison demonstrates the potential applications of the proposed scheme in industrial applications in terms of feasibility and performance.

Combined kinematic and dynamic control of an underwater swimming manipulator

An underwater swimming manipulator (USM) is a long and slender underwater vehicle composed of multiple links that are connected by motorized joints and equipped with thrusters. The USM belongs to the class of underwater vehicle-manipulator systems (UVMS), and the main task is to carry out autonomous inspections and light intervention operations on subsea installations. To achieve this, it is important to develop a control system that maintains the stability of the USM, while at the same time utilizes the kinematic redundancy of this highly flexible underwater vehicle. In this paper, we propose a combined kinematic and dynamic control approach for the USM. The approach uses the singularity robust multiple task priority (SRMTP) framework to generate a velocity reference and combines this with a dynamic velocity controller based on sliding mode control (SMC). This novel approach allows us to analyze the stability properties of the kinematic and dynamic subsystems together, in the presence of model uncertainty, while still retaining the possibility to solve multiple tasks simultaneously. We show that the multiple set-point regulation tasks will converge asymptotically to zero without the strict requirement that the velocities are perfectly controlled. This novel approach then avoids the assumption of perfect dynamic control that is common in kinematic stability analyses for robot manipulators. We demonstrate the applicability of the proposed approach through a simulation study of a USM carrying out three simultaneous tasks. The results show that all the regulation tasks converge to their respective set-points. The proposed control approach is applicable to vehicle-manipulator systems in general, and for any combination of regulation tasks.