Presence Of Model Uncertainties Research Articles

Adaptive neural-based finite-time attitude synchronization and tracking control of multiple rigid bodies under actuator faults and saturation

PurposeThe purpose of this paper, is to solve the problem of finite-time fault-tolerant attitude synchronization and tracking control of multiple rigid bodies in presence of model uncertainty, external disturbances, actuator faults and saturation. It is assumed that the rigid bodies in the formation may encounter loss of effectiveness and/or bias actuator faults.Design/methodology/approachFor the purpose, adaptive terminal sliding mode control and neural network structure are used, and a new sliding surface is proposed to guarantee known finite-time convergence not only at the reaching phase but also on the sliding surface. The sliding surface is then modified using a proposed auxiliary system to maintain stability under actuator saturation.FindingsAssuming that the communication topology between the rigid bodies is governed by an undirected connected graph and the upper bounds on the actuators’ faults, estimation error of model uncertainty and external disturbance are unknown, not only the attitudes of the rigid bodies in the formation are synchronized but also they track the time-varying attitude of a virtual leader. Using Lyapunov stability approach, finite-time stability of the proposed control algorithms demonstrated on the sliding phase as well as the reaching phase. The effectiveness of the proposed algorithm is also validated by simulation.Originality/valueThe proposed controller has the advantage that the need for any fault detection and diagnosis mechanism and the upper bounds information on estimation error and external disturbance is eliminated.

Trajectory Tracking Control for Underactuated USV with Prescribed Performance and Input Quantization

This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust control architectures are investigated for surge motion and yaw motion. To guarantee the prespecified performance requirements for position tracking control, the constrained error dynamics are transformed to unconstrained ones by virtue of a tangent-type nonlinear mapping function. On the other hand, the inaccurate model can be identified through radial basis neural networks (RBFNNs), where the minimum learning parameter (MLP) algorithm is employed with a low computational complexity. Furthermore, quantization errors can be effectively reduced even when the parameters of the quantizer remain unavailable to designers. Finally, the effectiveness of the proposed controllers is verified via theoretical analyses and numerical simulations.

Geometric-based prescribed performance control for unmanned aerial manipulator system under model uncertainties and external disturbances

In this paper, a robust control strategy with a cascade structure is designed for the underactuated unmanned aerial manipulator (UAM) to perform favorable trajectory tracking, in the presence of model uncertainties and time-varying external disturbances (e.g. wind drag). In the position loop, a geometric distance is adopted as the criterion for the thrust design. Besides, both the model uncertainties and external disturbances are compensated by employing a robust term. In the attitude loop, the prescribed performance guarantees are enforced in the controller design to standardize transient performance on attitude errors. In particular, just relying on the necessary feedback states, an auxiliary system, the adaptive control technique, and the disturbance observer are respectively designed to estimate the model uncertainties and external disturbances. Through the Lyapunov stability theory analysis, the proposed approach based on a geometric distance can effectively track the desired trajectory. The results of the comparative simulations study demonstrate the effectiveness and advantages of the proposed approach.

A note on “Fractional-order adaptive backstepping control of robotic manipulators in the presence of model uncertainties and external disturbances”

A note on “Fractional-order adaptive backstepping control of robotic manipulators in the presence of model uncertainties and external disturbances”

Capture and detumbling control for active debris removal by a dual-arm space robot

Active debris removal (ADR) technology is an effective approach to remediate the proliferation of space debris, which seriously threatens the operational safety of orbital spacecraft. This study aims to design a controller for a dual-arm space robot to capture tumbling debris, including capture control and detumbling control. Typical space debris is considered as a non-cooperative target, which has no specific capture points and unknown dynamic parameters. Compliant clamping control and the adaptive backstepping-based prescribed trajectory tracking control (PTTC) method are proposed in this paper. First, the differential geometry theory is utilized to establish the constraint equations, the dynamic model of the chaser-target system is obtained by applying the Hamilton variational principle, and the compliance clamping controller is further designed to capture the non-cooperative target without contact force feedback. Next, in the post-capture phase, an adaptive backstepping-based PTTC is proposed to detumble the combined spacecraft in the presence of model uncertainties. Finally, numerical simulations are carried out to validate the feasibility of the proposed capture and detumbling control method. Simulation results indicate that the target detumbling achieved by the PTTC method can reduce propellant consumption by up to 24.11%.

Adaptive variable impedance control for a modular soft robot manipulator in configuration space

Compliance is a strong requirement for human–robot interactions. Soft-robots provide an opportunity to cover the lack of compliance in conventional actuation mechanisms, however, the control of them is very challenging given their intrinsic complex motions. Therefore, soft-robots require new approaches to e.g., modeling, control, dynamics, and planning. One of the control strategies that ensures compliance is the impedance control. During the task execution in the presence of coupling force and position constraints, a dynamic behavior increases the flexibility of the impedance control. This imposes some additional constraints on the stability of the control system. To tackle them, we propose a variable impedance control in configuration space for a modular soft robot manipulator (MSRM) in the presence of model uncertainties and external forces. The external loads are estimated in configuration space using a momentum-based approach in order to reduce the calculation complexity, and the adaptive back-stepping sliding mode (ABSM) controller is designed to guard against uncertainties. Stability analysis is performed using Lyapunov theory which guarantees not only the exponential stability of each state under the designed control law, but also the global stability of the closed-loop system. The system performance is benchmarked against other conventional control methods, such as the sliding mode (SM) and inverse dynamics PD controllers. The results show the effectiveness of the proposed variable impedance control in stabilizing the position error and diminishing the impact of the external load compared to SM and PD controllers.

A Nonlinear Model Predictive Control Design for Autonomous Multivehicle Merging into Platoons

Integrated control for automated vehicles in platoons with nonlinear coupled dynamics is developed in this article. A nonlinear MPC approach is used to address the multi-input multi-output (MIMO) nature of the problem, the nonlinear vehicle dynamics, and the platoon constraints. The control actions are determined by using model-based prediction in conjunction with constrained optimization. Two distinct scenarios are then simulated. The first scenario consists of the multivehicle merging into an existing platoon in a controlled environment in the absence of noise, whereas the effects of external disturbances, modeling errors, and measurement noise are simulated in the second scenario. An extended Kalman filter (EKF) is utilized to estimate the system states under the sensor and process noise effectively. The simulation results show that the proposed approach is a suitable tool to handle the nonlinearities in the vehicle dynamics, the complication of the multivehicle merging scenario, and the presence of modeling uncertainties and measurement noise.

Learning-based collision-free coordination for a team of uncertain quadrotor UAVs

This paper investigates the safe coordination problem for a team of quadrotor unmanned air vehicles (UAVs) in the presence of model uncertainties and polygonal obstacles. Firstly, collision avoidance potential functions (PFs) related with both positions and velocities are utilized to construct the basic and bounded controller for achieving collision-free coordination. Then, in order to enhance the tracking performance under model uncertainties, an complementary control is developed via approximate dynamic programming (ADP), where the unknown dynamics of UAVs are accurately estimated in finite-time. Through using stored data in learning-based ADP, the approximated error of the weights in critic neural network converges with finitely excited signal. The sufficient conditions of the closed-loop system stability and collision avoidance are derived. The performance of the learning-based coordination methodology is demonstrated via simulation results.

Robust adaptive fault‐tolerant proportional‐derivative tracking control for six‐degrees of freedom unmanned aerial vehicles

AbstractThis article presents a robust adaptive control scheme for six‐degrees of freedom (DOF) unmanned aerial vehicles (UAVs) in the presence of modeling uncertainties and actuation faults. The proposed control is in proportional‐derivative (PD) form, and is able to tolerate actuation faults yet ensure stability and transient performance without the need for the detail model information of UAV. Furthermore, the PD control scheme exploited adaptively self‐tuning PD gains, which avoids the ad‐hoc and time‐consuming trial and error process for gain determination as commonly required in traditional PD control, thus is design‐friendly and low‐cost, rendering the control algorithms easy and straightforward for programming and implementation. Both theoretical analysis and numerical simulation validate the effectiveness of the proposed control method.

Distributed leaderless formation control for multiple autonomous underwater vehicles based on adaptive nonsingular terminal sliding mode

In this study, we address the formation control problem of multiple autonomous underwater vehicles (AUVs) in the presence of model uncertainties and ocean-current disturbances. Specifically, we propose a robust distributed leaderless formation control strategy by cross-coupling a synchronous control technique, a nonsingular terminal sliding-mode control approach, and an adaptive control method. We enhance the robustness against system uncertainties by redefining the cross-coupling errors and ensure that the finite-time tracking error converges to zero from any initial value via the designed adaptive nonsingular terminal sliding mode control, which also eliminates the chattering problem. A major advantage of our adaptive control law is neglecting the requirement of any prior knowledge about the upper bounds of the model uncertainties and ocean-current disturbances. Our comparative simulation results demonstrate the effectiveness of the proposed distributed leaderless formation control scheme.

Ergodic robust maximization of asymptotic growth

We consider the problem of robustly maximizing the growth rate of investor wealth in the presence of model uncertainty. Possible models are all those under which the assets’ region E and instantaneous covariation c are known, and where the assets are stable with an exogenously given limiting density p, in that their occupancy time measures converge to a law governed by p. This latter assumption is motivated by the observed stability of ranked relative market capitalizations for equity markets. We seek to identify the robust optimal growth rate, as well as a trading strategy which achieves this rate in all models. Under minimal assumptions upon (E,c,p), which in particular allow for an arbitrary number of assets, we identify the robust growth rate with the Donsker–Varadhan rate function from occupancy time large deviations theory. We also explicitly obtain the optimal trading strategy. We apply our results to the case of drift uncertainty for ranked relative market capitalizations. Here, assuming regularity under symmetrization for the covariance and limiting density of the ranked capitalizations, we explicitly identify the robust optimal trading strategy.

Robust adaptive model predictive control for guaranteed fast and accurate stabilization in the presence of model errors

AbstractNumerous control applications, including robotic systems such as unmanned aerial vehicles or assistive robots, are expected to guarantee high performance despite being deployed in unknown and dynamic environments where they are subject to disturbances, unmodeled dynamics, and parametric uncertainties. The fast feedback of adaptive controllers makes them an effective approach for compensating for disturbances and unmodeled dynamics, but adaptive controllers seldom achieve high performance, nor do they guarantee state and input constraint satisfaction. In this article we propose a robust adaptive model predictive controller for guaranteed fast and accurate stabilization in the presence of model uncertainties. The proposed approach combines robust model predictive control (RMPC) with an underlying discrete‐time adaptive controller. We refer to this combined controller as an RMPC‐ controller. The adaptive controller forces the system to behave close to a linear reference model despite the presence of parametric uncertainties. However, the true dynamics of the adaptive controlled system may deviate from the linear reference model. In this work we prove that this deviation is bounded and use it as the modeling error of the linear reference model. We combine adaptive control with an RMPC that leverages the linear reference model and the modeling error. We prove stability and recursive feasibility of the proposed RMPC‐. Furthermore, we validate the feasibility, performance, and accuracy of the proposed RMPC‐ on a stabilization task in a numerical experiment. We demonstrate that the proposed RMPC‐ outperforms adaptive control, robust MPC, and other baseline controllers in all metrics.

Fuzzy logic control of a hybrid renewable energy system: A comparative study

In this paper the authors have developed a hybrid renewable energy system. The studied structure comprises a wind energy generator, a shaded photovoltaic generator associated to an inertial storage system. The modeling of the global structure is developed and the control strategy is discussed. Conventional methods have been used to control the renewable sources. However, they may not have a suitable performance if the controlled system contains nonlinearity, as the wind turbine system or the shaded photovoltaic generator. In the presence of modeling uncertainties, the necessity of methods presenting controllers with appropriate performance as the advanced control strategies is inevitable. Fuzzy logic is used to control both the wind generator’s pitch angle and the maximum power point tracking (MPPT) of a shaded photovoltaic generator. The control of the storage flywheel, associated with the studied structure, to regulate the power supplied by renewable sources is also discussed. The design of the proposed strategy and its comparison with conventional controllers are carried out. The proposed method effectiveness is verified by MATLAB simulation results.

Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators

In this study a novel cooperative controller design is developed to tackle the consensus tracking problem of Multi-Agent systems (MAS) based on multilayer perceptron neural network (MLPNN), applied on a distributed synchronous generator (SG) Multi-Agent system in presence of model uncertainties and external disturbances. Application of MLPNN in controller design can lead to smoother system response and can neutralize the impacts of model uncertainties. Furthermore, the proposed method benefits from a novel algorithm formerly known as error backpropagation (BP) algorithm to update and to regulate the weights of MLPNN adaptively based on the principles of consensus error. The proposed strategy can be very effective in control of the distributed SG Multi-Agent system due to its ability for system identification, parameter estimation, and disturbance approximation. Moreover, the utilization of neural networks can meet the criterion to make the consensus error uniformly ultimately bounded. Ultimately, simulation results illustrate the applicability and effectiveness of the novel MLPNN controller to model the system uncertainties and to deal with external disturbances of the distributed SG Multi-Agent system.

A Robust Hybrid Control for Autonomous Flying Robots in an Uncertain and Disturbed Environment

With the aim of efficiently achieving complex trajectory tracking missions in the presence of model uncertainties and exogenous disturbances, this paper proposes a robust hybrid control for the orientation and position of flying robots by adopting insights from sliding mode, geometric tracking, and nonlinear feedback control strategies. Various retrofits are implemented to the composite control scheme in order to tackle the system uncertainties, eliminate the chattering effects, and enhance the trajectory tracking performance. The convergence and stability analysis demonstrated the asymptotic stability of the proposed control algorithm. To reveal the promising performance of the developed control schemes, a qualitative comparative analysis of different proposed control approaches is performed. The comparative analysis examines highly maneuverable trajectories for various tracking scenarios in the presence of uncertain disturbances. The simulation results demonstrated the versatility, robustness, and convergence of the developed control laws that allow autonomous flying robots to effectively perform agile maneuvers.

Model‐based multi‐objective particle swarm production optimization for efficient injection/production planning to improve reservoir recovery

AbstractThis study employs an adjusted version of the multi‐objective particle swarm optimization (MOPSO) algorithm to plan an optimized reservoir's injection/production strategy. Three case studies, including two water‐flooding benchmark models and one gas‐condensate problem, are exercised as subjected problems to validate the MOPSO approach. The contradicting values of objectives, long‐term net present value (LNPV) versus short‐term net present value (SNPV), are obtained so that relying on a Pareto front improves decision‐making. In one water‐flooded case, inflow control valves of smart wells are considered to be adjusted within the optimization, while in the second case, the optimized well injection rates are control variables. The obtained results for water‐flooded reservoirs are shown to optimize the competitive objective functions more than the previous efforts in the literature. Moreover, 10 different permeability maps of the second case are implemented to obtain the optimum injection rates to perform optimization under uncertainty. The MOPSO robustly optimized the production/injection strategy in the presence of model uncertainty. In the gas‐condensate problem, the optimal gas injection rate in the SPE‐3 benchmark model is determined. The gas‐condensate case's outputs yield a decreased oil saturation result in the reservoir compared to non‐optimized production scenarios. Results illustrate that for all cases, MOPSO can provide optimal injection/production scenarios. Therefore, the proposed scheme gives the advantage of deciding between the set of results into a decision‐maker to optimize the production program by trading‐off within different strategies. Besides the Pareto fronts with adequate variety and steadiness, a great converging rate is the main advantage of this method.

Seamless Active Morphing Wing Simultaneous Gust and Maneuver Load Alleviation

This paper deals with the simultaneous gust and maneuver load alleviation problem of a seamless active morphing wing. The incremental nonlinear dynamic inversion with quadratic programming control allocation and virtual shape functions (denoted as INDI-QP-V) is proposed to fulfill this goal. The designed control allocator provides an optimal solution while satisfying actuator position constraints, rate constraints, and relative position constraints. Virtual shape functions ensure the smoothness of the morphing wing at every moment. In the presence of model uncertainties, external disturbances, and control allocation errors, the closed-loop stability is guaranteed in the Lyapunov sense. Wind tunnel tests demonstrate that INDI-QP-V can make the seamless wing morph actively to resist “1-cos” gusts and modify the spanwise lift distribution at the same time. The wing root shear force and bending moment have been alleviated by more than 44% despite unexpected actuator fault and nonlinear backlash. Moreover, during the experiment, all the input constraints were satisfied, the wing shape was smooth all the time, and the control law was executed in real time. Furthermore, as compared with the linear quadratic Gaussian control, the hardware implementation of INDI-QP-V is easier; the robust performance of INDI-QP-V is also superior.

Incremental Twisting Fault Tolerant Control for Hypersonic Vehicles With Partial Model Knowledge

A passive fault tolerant control scheme is proposed for the full reentry trajectory tracking of a hypersonic vehicle in the presence of modeling uncertainties, external disturbances, and actuator faults. To achieve this goal, the attitude error dynamics with relative degree two is formulated first by ignoring the nonlinearities induced by the translational motions. Then, a multivariable twisting controller is developed as a benchmark to ensure the precise tracking task. Theoretical analysis with the Lyapunov method proves that the attitude tracking error and its first-order derivative can simultaneously converge to the origin exponentially. To depend less on the model knowledge and reduce the system uncertainties, an incremental twisting fault tolerant controller is derived based on the incremental nonlinear dynamic inversion control and the predesigned twisting controller. In this article, it is shown that not only the benefits of both incremental control and twisting control are inherited, but also their side effects are reduced. Notably, the proposed controller is user friendly in that only fixed gains and partial model knowledge are required. Numerical simulations in various cases and comparison studies are conducted to verify the effectiveness of the proposed method.

Prescribed time tracking control of constrained Euler–Lagrange systems: An adaptive proportional–integral solution

AbstractMost autonomous systems can be described by Euler–Lagrange (EL) model. This article investigates the problem of prescribed time tracking control for EL systems in the presence of modeling uncertainties, prescribed performance requirements and time‐varying state constraints. Two proportional–integral (PI)‐like control schemes with time‐varying gains are developed with several favorable features: (1) achieving prescribed tracking precision within finite time in the presence of modeling uncertainties; (2) state constraints being obeyed all the time; (3) both the final tracking accuracy and the setting time being preassigned irrespective of initial condition or any other constraining parameter; and (4) bearing simple PI structure with analytical formula for robust‐adaptive tuning gains, and demanding inexpensive online computation. The benefits and effectiveness of the proposed control are also validated via numerical simulation.

Optimal risk exposure and dividend payout policies under model uncertainty

This paper investigates a combined optimal risk exposure and dividend distribution decision making problem in presence of model uncertainty. In the context of model uncertainty, the decision maker regards the reference model (fitted by observed information) as an approximation to the true model and believes that the true model exists in a family of alternative models surrounding the reference model. The aim is to find a robust strategy of risk exposure and dividend payments, which maximizes the expected cumulative discounted dividends until ruin plus a “penalty” on the distortion between the reference and alternative models in the worst-case scenario. We provide explicit expressions for the value functions and derive the respective optimal strategies explicitly. We show that the optimal dividend strategy is always of a barrier type and that it is optimal to retain full risk when the surplus is large. We also find that when the insurer is more averse to ambiguity, her optimal strategy on risk retention and dividend payout is more conservative.