Unstructured Uncertainties Research Articles

Decentralized robust control of a vehicle’s interior sound field

In this article, a decentralized strategy is applied for active control of sound inside the automobile cabin by considering the uncertainty caused by the movement of the occupants. A coupled structural-acoustic analysis is done on the simplified geometry of the automobile cabin with passengers by the finite element method. The uncertainty caused by changes in the occupants’ head positions and angles is also considered. Then, a decentralized robust feedback control strategy is proposed to reduce the sound pressure level in the ears of all the occupants using the H∞ method for each individual control unit by considering the unstructured uncertainty of the plant. The efficiency of the proposed control strategy in minimizing the sound pressure level caused by the structural disturbance on the firewall panel is investigated.

Optimal Robust Navigation Control of an Endovascular Microrobot

Microrobots are considered to be a promising solution for complex sequence of operations in biomedical applications. The paper aims to present an algorithm for path planning and control of an endovascular microrobot navigated by a conventional magnetic resonance imaging (MRI) device. The microrobot is a ferromagnetic spherical bead moving through human vascular system; accordingly, the dynamic model is well developed considering the most dominant resistive force, i.e., hydrodynamic drag. An optimal approach considering a power index is used for path planning of the robot from an initial position to the desired destination avoiding collision with boundaries within a generated robust domain based on the unmodeled dynamic of surface forces. Due to the presence of parameter uncertainty and unmodeled dynamics, an adaptive backstepping controller is designed to navigate the bead through the optimal robust path such that the control signal can be implemented via a conventional MRI device. The control method is proved to have stability and convergence as it manages to maintain the estimated parameters and control inputs bounded in the presence of disturbance and uncertainty. The method of path planning explicitly deals with unstructured uncertainties of the surface forces based on developed comparison and reasoning as it maintains minimum effort optimality. The adaptive backstepping method is designed to handle structural uncertainties and disturbances due to pulsatile flow and non-measured velocity. Several simulations were performed to examine navigation of the ferromagnetic microrobot in the circulatory system of human body, and the tracking errors, estimated parameters, and control signal inputs are represented, accordingly. Also, it is shown how wall effect correlation term in the model could increase the control accuracy.

Finite-time optimized robust control with adaptive state estimation algorithm for autonomous heavy vehicle

Structured and unstructured uncertainties coupled with external disturbances usually pose considerable challenges in realizing heavy vehicle automatic lane guidance control. This paper presents a novel solution paradigm for the lateral control of autonomous heavy vehicle. The proposed control framework consists of an adaptive state estimator and a finite-time optimized ℋ∞ state feedback regulator. Firstly, based on the dual extended Kalman filter (DEKF) technique, which can estimate model time-varying parameters and unknown states at the same time, the adaptive state estimation algorithm is proposed and investigated to improve state estimation adaptivity against uncertain time-varying general front/rear axle cornering stiffnesses. Then, using zero-sum dynamic game theory as the general framework, we analyze the minimax controller design problem and introduce the finite-time optimized robust regulator design method. The autonomous heavy vehicle automatic lane guidance controller is designed in the framework of robust optimal state feedback control to make it robust against external disturbance. Finally, results of various driving scenarios from both simulation and hardware-in-loop (HIL) implementation show that the proposed finite-time optimized robust and adaptive state estimation control paradigm can robustly obtain satisfactory automated lateral control performance for heavy vehicle when the control system is characterized by both time-varying model parameters and uncertain external energy-bounded disturbance. A comparative study is also conducted to investigate cases to show its effectiveness when the adaptive state estimation is used and when it is not.

Unstructured Uncertainty Based Modeling and Robust Stability Analysis of Textile-Reinforced Composites with Embedded Shape Memory Alloys

This paper develops the mathematical modeling and deflection control of a textile-reinforced composite integrated with shape memory actuators. The mathematical model of the system is derived using the identification method and an unstructured uncertainty approach. Based on this model and a robust stability analysis, a robust proportional–integral controller is designed for controlling the deflection of the composite. We showed that the robust controller depends significantly on the modeling of the uncertainty. The performance of the proposed controller is compared with a classical one through experimental analysis. Experimental results show that the proposed controller has a better performance as it reduces the overshoot and provide robustness to uncertainty. Due to the robust design, the controller also has a wide operating range, which is advantageous for practical applications.

An efficient hybrid approach for trajectory tracking control of autonomous underwater vehicles

In this manuscript, an efficient hybrid trajectory tracking control scheme has been proposed for an autonomous underwater vehicle in the presence of structured and unstructured uncertainties. To cope with these uncertainties, the model-dependent control scheme is successfully combined with the model-free control scheme. Due to the effects of the uncertainties, the full knowledge of the dynamic model of the vehicle cannot be accurately obtained in real applications. Therefore whatever the partial information is available about the dynamics of the system has been utilized in the controller design. A radial basis function neural network is utilized for the approximation of the unknown dynamics without requiring offline learning. To compensate for the unknown effects like the external disturbances and the reconstruction error of the neural network, an adaptive compensator is also added to the part of the controller. For the stability analysis, the online learning of the parameters and the neural network weights are used in the Lyapunov approach. Based on the Lyapunov stability criteria and Barbalat lemma, the tracking errors converge to zero asymptotically. Finally, comparative numerical simulations are performed over a four-degree of freedom autonomous underwater vehicle and efficiency and applicability of the proposed control framework is validated in a comparative manner with the existing controllers.

Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

Generally, structural uncertainty of the robot dynamics system refers to model error caused by parameter identification, unstructured uncertainty is the unmodeled dynamic characteristic. No matter how elaborate modeling methods are used, there always be uncertainty. Therefore, this article applies deep learning for the first time to aid robot dynamic parameter identification of 6 degrees of freedom robot manipulator for compensation of uncertain factors. Firstly, the relatively accurate prediction of torque is obtained by the physical dynamic model using the parameters identification method (errors are less than 10% of the maximum torques). Secondly, we propose a novel deep neural network based approach called Uncertainty Compensation Model (UCM) to compensate the torque error introduced by the uncertainty. The UCM mainly composed by proposed Input Control Module (ICM) and Error Learning Models (ELMs) based on Long-Short-Term Memory and attention mechanism. The proposed ICM, which effectively avoids the unnecessary interference, is used to control valid input for ELMs. The ELMs, consisted by ELM units, concern extracting salient features from sequence data to predict the joint error. Also, this article summarizes the effects of valid input, timestep and attention mechanism on the performance of the UCM. Finally, the verification of parameter identification and torques compensation is carried out by a Universal Robot 5 manipulator. Compared with the prediction torques of physical dynamic model, the proposed UCM has a good ability to capture the friction characteristics and compensate for the error of local maximum torques, which effectively solves the deficiency of the physical dynamics model and improves prediction accuracy (errors are less than 6% of the maximum torques).

Fault Tolerant Control Scheme for a Class of Interconnected Nonlinear Time Delay Systems Using Event-Triggered Approach

In this study, the event-triggered decentralized fault tolerant control (FTC) problem is investigated for a class of interconnected nonlinear time delay systems in strict feedback form with input quantization and actuator faults. The model of interconnected nonlinear time delay systems includes unstructured uncertainties and unmeasurable states. Firstly, the radial basis function neural networks (RBFNNs) are introduced to identify the unstructured uncertainties and time delay functions. An adaptive RBFNNs state observer is constructed to identify the unmeasurable states, which will be used to the design of decentralized fault tolerant controller. By utilizing the adaptive backstepping technique with low pass filter, an event-based decentralized FTC strategy is developed for the considered interconnected nonlinear systems, such that the output signals could track the reference signals, and the stability of the closed loop systems is guaranteed. Finally, the effectiveness of the designed control strategy is illustrated by a practical simulation example.

Hybrid Sliding Mode/H-Infinity Control Approach for Uncertain Flexible Manipulators

In this article, a hybrid control approach combining sliding mode and H-infinity is proposed for an uncertain single-link flexible manipulator. The sliding mode controller stabilizes the nonlinear manipulator system, while the H-infinity controller enhances the noise rejection capability of the system by reducing the total system nonlinearity. The proposed hybrid controller is designed with the goal of rejecting external noises, hence providing a higher system performance, compared to a pure sliding mode controller. To avoid unintentional consequences of switching between the sliding mode and H-infinity controller, a fuzzy neural network weighting method is designed providing a smooth synthesis of both controller outputs. The neuro-fuzzy method applies a weighted combination of the two controller outputs to the manipulator system. In addition, a novel fuzzy estimation method is used to characterize the unstructured nonlinear disturbances in manipulator systems. The proposed hybrid control approach along with the fuzzy estimator is capable of providing a versatile means to stabilize flexible manipulator systems while maintaining a precise reference trajectory tracking in presence of unstructured uncertainty and nonlinear dynamics, as demonstrated by simulation results.

Improved Event-Triggering Scheme for Uncertain Systems

This paper develops a new event-triggering approach for a class of nonlinear systems. This approach goes beyond the control of certain systems by developing sporadic feedback control laws to solve unstructured uncertainty problem. It guarantees asymptotic stability of a broad range of uncertain systems under sample and hold implementation while it efficiently reduces the communication samples. Under the proposed scheme, in order to handle the uncertain dynamics, the sampling time might become smaller than that in the approach of the certain systems. Using linear matrix inequalities, the controller gains and the event triggering parameters are obtained simultaneously. In addition to addressing new scheme for uncertain systems, we propose another triggering mechanism for affine nonlinear systems. Simulation results illustrate that the increase of triggered messages due to the nonlinearity and uncertainty is kept small enough that can be neglected when compared with conventional event-triggering policy.

Internal model control of induction motors based on extended state observer

The conventional internal model control (IMC) has been used widely due to its advantages of less computational burden and simple implementation. Since the internal model controller has a fixed filter, disturbances created by mismatched models, parameter variations and other unstructured dynamic uncertainties in induction motors (IMs) cannot be eliminated by a fixed IMC. To solve these problems, the control strategy of an induction motor using internal model control with an extended state observer (IMC-ESO) was proposed. IM parameter variations and other unstructured dynamic uncertainties are considered in IM drives. Based on this model, an ESO is defined as a hypothetical equivocal function. Then the estimated disturbance is applied as a feed-forward compensation to accurately control the current loop. Since the designed ESO works concurrently with IMC, the fast dynamic response of the IMC is maintained. The feasibility and validity of the proposed method are validated by experimental results.

Robust constrained model predictive control design for piecewise non-linear systems with multiple operating points

In this paper, a robust model predictive control (MPC) scheme is developed for non-linear systems. We propose a new modeling approach, entitled piecewise non-linear, for plants with multiple operating points and with unstructured uncertainties. The systems, in each subregion, are composed of an affine model perturbed by an additive non-linear term which is locally Lipschitz. Considering a non-linear term in the model changes the control problem from a convex program to a non-convex one, which is much more challenging to solve. A standard dual-mode control strategy is introduced by parameterizing the infinite horizon control moves into a number of free control moves followed by a single state feedback law. The designed controller is robust against model uncertainty and guarantees system stability under switching between subregions. Numerical examples on a highly non-linear chemical process and another non-linear system are used to evaluate the applicability of the proposed method. Simulation results show a better performance in terms of speed of convergence and feasibility compared with the conventional robust MPC designs.

A Novel Terramechanics-Based Path-Tracking Control of Terrain-Based Wheeled Robot Vehicle With Matched-Mismatched Uncertainties

Path-tracking control of a wheeled robot is a complex dynamical problem due to the system nonlinearities, external disturbances, and modeled and unstructured uncertainties. The problem is profoundly exacerbated for the robot traversing deformable terrains. This paper mainly addresses this problem by the following contributions: i) path-tracking control of terrain-based robots considering deformable soil dynamics for deriving tractive forces and moments arising from wheel-terrain interactions; ii ) a novel modified moving integral sliding surface, where the dynamics of the sliding surface is regulated by an adaptive inverse neural network; and iii ) path-tracking controller synthesis considering the terrain-induced uncertainties. For this purpose, a novel adaptive indirect controller is proposed to address the path-tracking control of a wheeled robot in the presence of external disturbances and wheel slippage using an integrated modified Integral Sliding Mode Control (ISMC) and adaptive neural networks (NNs). A compensating controller term is introduced to minimize abrupt variations in control demand that may arise from uncertainties related to terrain properties and the resulting wheel slippage and motion resistance. The adaptive rules are formulated considering the ISMC based control dynamics, while the stability of the closed-loop system is ensured via Lyapunov stability theorem. The effectiveness of the proposed controller is demonstrated considering a typical sandy loam soil for linear and curved trajectories. It is inferred that the proposed adaptive indirect ISMC-NN robust controller has the capacity to reach a satisfactory path-tracking control performance in the presence of disturbance and uncertainties arising from robots interactions with the deformable terrain.

Adaptive Control of a 3-DOF Helicopter Under Structured and Unstructured Uncertainties

In this paper, an adaptive control strategy is presented for a three-degree-of-freedom helicopter under both structured and unstructured uncertainties. The adaptive control scheme learns online the helicopter’s inverse model with a Lyapunov-based adaptation law to estimate the system’s parameters. Moreover, a reference model is introduced to stabilize the helicopter at start-up and cope with unstructured uncertainties such as external disturbance. Therefore, the controller achieves accurate motion tracking in the presence of both structured uncertainties (parameters variation) and unstructured uncertainties (unknown disturbance). Unlike many controllers, the proposed adaptive control scheme’s stability is guaranteed by Lyapunov direct method. The proposed controller’s performance in coping with various uncertainties is highlighted in different operating conditions.

Quasi-Polynomial-Based Robust Stability of Time-Delay Systems Can be Less Conservative Than Lyapunov–Krasovskii Approaches

In this article, stability of uncertain time-delay unforced systems is analyzed through a generalized characteristic quasi-polynomial, which is exactly rewritten as a polytope whose interpolating functions exhibit mutual dependence. It is shown that an adequate combination of Kharitonov-like results and Polya-like relaxations leads to progressively better results at the price of more demanding computational burden. Stability methodologies are developed and proved advantageous when compared with traditional Lyapunov-based approaches, both for structured and unstructured uncertainty.

Adaptive Fuzzy Control Scheme for Variable-Speed Wind Turbines Based on a Doubly-Fed Induction Generator

The purpose of this paper is to present a new adaptive fuzzy control scheme for grid-connected variable-speed wind turbines (WT) based on a doubly-fed induction generator (DFIG). The proposed controller simultaneously guarantees two independent control objectives: (1) DFIG torque control allowing the extraction of maximum available power from the wind, and (2) control of the stator reactive power to maintain a desirable power factor according to the grid requirements. Unlike many existing control designs developed for DFIG-based WT, the design of the proposed controller is based on nonlinear coupled models of WT, without attempting approximate linearization. To improve performance in operating conditions, the model uncertainties and the nonlinear functions appearing in the tracking errors dynamics are reasonably approximated by adaptive fuzzy systems. It is mathematically proven that the proposed adaptive fuzzy control scheme can guarantee that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results show that the proposed control scheme has strong robustness against the system parameter variations and unstructured uncertainties.

Stability analysis of piecewise affine systems with multi-model predictive control

We propose an input–output stability analysis for closed-loop systems of piece-wise affine models under unstructured uncertainty and controlled by multi-model linear MPC with input constraints. Integral quadratic constraints (IQCs) are employed to assess the robustness of MPC under uncertainty. We efficiently create a model pool, by performing linearization on selected transient points. All the possible uncertainties and nonlinearities (including the controller) can be introduced in the framework, assuming that they admit the appropriate IQCs, whilst the dissipation inequality can provide sufficient conditions for stability through the incorporation of IQCs. We demonstrate the existence of static multipliers, which can reduce the conservatism of the stability analysis significantly. The proposed methodology is demonstrated through two illustrative case studies.

FEA based discrete-time sliding mode control of uncertain continuum mechanics MIMO vibrational systems

In this paper, a novel method for control of uncertain MIMO flexible systems is proposed. The control system is constructed such that it is capable of stabilizing a flexible dynamical system based on rigid approximation of continuum dynamics, resulting in facilitation of modeling and control procedures for complicated vibrational systems. In order to ensure system stability in real-time applications, the control system is obtained in discrete-time basis. To these ends, a sliding hyper-plane based sliding mode control (SMC) algorithm is constructed featuring sliding functions for individual subsystems. An approximated dummy model of the real system is used alongside the dynamical system in order to estimate parametric and unstructured uncertainties which are evaluated through comparisons between the dummy model, data obtained from the real system and corresponding mathematical model. Conventional SMC techniques introduce undesired effects of chattering which result in amplifying vibrations in flexible systems. To address this issue, an alternate algorithm based on selection of control signals within appropriate bounds is used in order to eliminate chattering and provide superior performance in comparison with conventional SMC techniques. The accuracy of proposed method has been verified through ANSYS® mechanical APDL-based simulations and its effectiveness has been verified in multiple operating conditions.

Adaptive sliding mode control of $n$ flexible-joint robot manipulators in the presence of structured and unstructured uncertainties

This study investigates a voltage-based adaptive sliding mode control (VB-ASMC) to tracking the position of an $n$ rigid-link flexible-joint (RLFJ) robot manipulator under the presence of uncertainties and external disturbances. First, the dynamic equations of the $n$-RLFJ robot manipulator have been divided into $n$ subsystems, and for each of them a voltage-based sliding mode control (VB-SMC) is designed simultaneously. The mathematical proof shows that the closed-loop system under VB-SMC has global asymptotic stability. Second, due to the use of the sign function in the VB-SMC structure, the occurrence of chattering is inevitable. Therefore, to overcome this problem, an adaptive estimator is designed to estimate the boundary of uncertainties. Since the adaptive estimator part in the VB-ASMC has only one law, the proposed control has a very low computational volume. The Lyapunov stability theorem shows that the controlled closed-loop system under the VB-ASMC has global asymptotic stability. Finally, extensive simulations on the single and 2-RLFJ robot manipulator and practical implementation on the single-RLFJ robot manipulator are presented to demonstrate the effectiveness and improved performance of the proposed control scheme.

Fixed order non-smooth robust H∞ wide area damping controller considering load uncertainties

This paper presents the design of a fixed order non-smooth robust H∞ wide area damping controller for the multi machine power system considering load uncertainties. Dynamic response of loads has a significant effect on system stability and directly determines the stability margin of the operating point. The effect of load uncertainties is considered by different percentage and size of induction motors as dynamic load which was considered by unstructured uncertainty. The proposed method is based on shaping of the open loop transfer function in the frequency domain. This proposed method is robust with respect to multi-model and load type (dynamic, static) and parametric time-delay uncertainties. Non-smooth optimization techniques are developed to H∞ synthesis problem under additional structural constraints on the controller. This approach avoids using Lyapunov variables and therefore leads to moderate size optimization programs even for very large systems.The performance of the controller has been tested on two-area four-machine test system model and New England 39 bus system under various operating scenarios and faults. The effectiveness of the developed robust centralized wide area controller, designed based on H∞ fixed order loop shaping synthesis, is compared with a conventional power system stabilizer.

Fast Terminal Sliding-Mode Control with an Integral Filter Applied to a Longitudinal Axis of Flying Vehicles

The automatic pilot flight control system is undoubtedly one of the most important parts of the flying vehicle that provide stability and to operate appropriately in the guidance section. Considering to nonlinear, dynamic and time-varying system, structural and parametric uncertainties of the flying vehicles, in flight control, varietal control approach have to achieve stability, proper operation as well as decreasing effect of uncertainties and modeling errors. In this paper, designing of the longitudinal flying vehicles autopilot a Fast Terminal Sliding Mode Control (FTSMC). Variable structure systems because of the robustness effect on uncertainty and the effects on disturbances which a contributor to widespread efficiency. One of the methods for controlling the variable structure is a sliding mode, which is one of the nonlinear controllers that can control the system in the structured uncertainties and unstructured uncertainties. Additionally, in the method of classic sliding Mode Control is got convergence of states equilibrium point by an asymptotic curve. While proportional Integral Sliding Mode Control has the convergence of states to the equilibrium point in finite time. One of the issues is that finite time cannot determine the time of convergence when the state turn initial position to a final position. The proposed method is based on the Lyapunov stability theory and has guaranteed stability of the control system. The controller is robust to external disturbances and unmodified dynamics. Three types of controllers which are multi-input-multi-output (MIMO) system with random uncertainty are designed. Furthermore, the classic sliding mode controller, the proportional-integral sliding mode controller as well as the integral terminal sliding mode controller are reviewed. A glance at the results simulates shows an improved in the proposed method. Simulations are done using MATLAB software.