Robust Adaptive Control Law Research Articles

Adaptive Robust Control for a Spatial Flexible Timoshenko Manipulator Subject to Input Dead-Zone

This article investigates the adaptive robust spatial vibration control for a flexible Timoshenko manipulator subject to input dead-zone nonlinearity characteristic. The “disturbance-like” terms and dead-zone nonlinearity are first incorporated into the context of control design, and the new boundary robust adaptive control laws are constructed to reduce the shear deformation and elastic oscillation, ensure the expected angle orientation, handle the input dead-zone, and estimate the upper bound of compound disturbances. The convergence of states and the stability of the system are analyzed and proven without simplifying the infinite dimensional dynamics. In the end, the effectiveness of the presented scheme is demonstrated by the result of simulation research.

Coordinated Saturated Output-Feedback Control of an Autonomous Tractor-Trailer and a Combine Harvester in Crop-Harvesting Operation

This paper proposes a coordinated tracking controller for a tractor-trailer to keep a desired distance and heading angle relative to a combine harvester for the autonomous unloading of harvested crops in automatic agriculture operations. A coordinate transformation and the prescribed performance control (PPC) technique are employed to develop a second-order Euler-Lagrange formulation of the tracking errors. Then, a neural adaptive saturated velocity observer-based controller is proposed to force that the trailer center is exactly placed under the end point of the harvester unloading tube to collect cereal grains. By an effective application of the PPC technique and computing a safe motion boundary, controller non-singularity, collision avoidance and connectivity between two vehicles are preserved continuously. The model uncertainties including unknown vehicle parameters, variable trailer mass and moment of inertia during the crop collection, surface friction, climate and crop conditions and external disturbances are compensated by an effective combination of a neural network and an adaptive robust control law. Lyapunov’s direct method is employed to prove that the tracking errors are semi-globally uniformly ultimately bounded and converge to a neighborhood of the origin with a pre-specified maximum overshoot, convergence rate and steady-state accuracy. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed controller for the crop harvesting operations in a practical perspective.

Robust adaptive PI control of wave energy converters with uncertain dynamics

There exist several approaches to design the optimal control strategy to harvest wave energy with a point absorber. However they are generally based on the assumption that the WEC and the PTO dynamics are well-known. In the practical WEC control implementation, this is generally not the case. The objective of this paper is to design a robust optimal control strategy that can take into account the uncertain WEC and PTO dynamics. Our choice is a robust adaptive PI control law. The proposed controller is validated and compared through simulation for irregular sea states.

Improved ELOS based path following control for underactuated surface vessels with roll constraint

This study aims to the path following control of underactuated surface vessels with an improved extended state observer based line-of-sight (ELOS). The large roll motion and nonzero drift angle can affect the guidance law and the course tracking accuracy in rough waves, the two factors should be considered when the path following control system is designed. The robustness of the tracking control system is enhanced by handling the system dynamic uncertainty and external random disturbances. First, the roll motion based track error dynamics are established, then a reduced order extended state observer is applied to estimate the time varying drift angle, and the adaptive heading guidance law is proposed. Finally, a robust adaptive heading control law is developed by combining dynamic surface control, sliding mode control and backstepping techniques. The ELOS based path following control methodology can ensure that all estimation and track errors are uniformly ultimately bounded. Simulations were used to illustrate the effectiveness of the proposed controller by comparing it with sliding mode control.

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.

Robust neural network‐based backstepping landing control of quadrotor on moving platform with stochastic noise

AbstractThis article introduces a novel nonlinear control approach for quadrotor landing on a moving ship. The dynamics of the relative position and attitude dynamics with uncertainties, unmodeled dynamics, external disturbances, and control input saturation is derived. Then, a novel robust adaptive constrained backstepping control law for the propeller and torques of the quadrotor is designed. To deal with uncertainties and un‐modeled dynamics, radial basis function is employed. The bounded‐in‐probability stability is used to alleviate the destructive influence of the stochastic Wiener process on the system positions and attitudes. Whereas the quadrotor is under‐actuated, a redundant control input technique, which affects the attitude commands, is presented. The actual control input saturation limits are turned into unknown saturation limits of the redundant control inputs. Also, since two phases for the landing problem are considered, a smooth switching mechanism between the control inputs is developed. Simulation results show the merits and applicability of the developed robust adaptive nonlinear controller.

Broad-RBFNN-Based Intelligence Adaptive Antidisturbance Formation Control for a Class of Cluster Aerospace Unmanned Systems with Multiple High-Dynamic Uncertainties

This paper investigates the antidisturbance formation control problem for a class of cluster aerospace unmanned systems (CAUSs) suffering from multisource high-dynamic uncertainties. Firstly, to estimate and compensate the uncertainties existing in CAUS coordinate dynamics, an adaptive antidisturbance formation control law, which is combined by a robust adaptive control law and the second order disturbance observer, has been designed. Secondly, aiming at the adverse influences caused by the nonlinear time-varying nonlinearities existing in the formation flight dynamics, the radial basis function neural network (RBFNN) is introduced. Furthermore, considering the rapidly varying characteristics of the aforementioned formation flight nonlinearities, a novel board RBFNN (B-RBFNN) has been constructed and utilized to improve the approximation and compensation performance. In virtue of the fusing of the B-RBFNN and the second-order disturbance observer-based adaptive formation control law, the rapid response rate and the higher control accuracy of the formation control system can be achieved. As a result, a novel B-RBFNN-based intelligence adaptive antidisturbance formation control algorithm has been established for CAUS trajectory coordination and formation flight. Numerical simulation results are proposed to illustrate the effectiveness and advantages of the proposed B-RBFNN-based intelligent adaptive formation control method for the CAUS.

Dynamic positioning of ship using backstepping controller with nonlinear disturbance observer

This paper studies the adaptive dynamic positioning control problem of the full-actuated ship with uncertain time-varying environmental disturbances. Considering the disturbances with unknown boundaries, the inversion control technique is combined with the disturbance observation compensation method to design the robust adaptive backstepping control law of the ship dynamic positioning system. The Lyapunov function is adopted to prove the errors of the ship’s position and heading angle are uniformly ultimately bounded using the designed control law. The nonlinear disturbance observer can adaptively estimate and compensate for uncertain external disturbances caused by winds, waves and currents. Afterward, the verification of the proposed controller through a typical CyberShip Ⅱ model subject to environmental disturbances is carried out using a hardware-in-the-loop simulation where a thrust distribution model is established. The simulation results show the effectiveness of the proposed control law.

Output feedback synchronization control for supply vessels during underway replenishment with unknown dynamics and disturbances under input saturation

ABSTRACT This paper investigates the synchronization control problem for a supply vessel during underway replenishment with unknown dynamics, unknown disturbances, input saturation and unavailable velocities. A virtual trajectory shifted by a distance at an angle relative to the trajectory of the supplied vessel is planned. A high-gain observer observer is employed to estimate unavailable vessel velocities and an auxiliary design system is constructed to address the effect of input saturation. Then, the adaptive radial basis function neural networks are employed to approximate unknown dynamics and the adaptive laws are derived to estimate bounds of unknown disturbances. Incorporating the above into the dynamic surface control method, an output feedback robust adaptive neural synchronization control law is developed to force the supply vessel to track the virtual trajectory such that the synchronization navigation between the supply and the supplied vessels is realized. The theoretical analysis and simulation results validate our developed control scheme.

A PI Controller with a Robust Adaptive Law for a Dielectric Electroactive Polymer Actuator

Dielectric electroactive polymer actuators are new important transducers in control system applications. The design of a high performance controller is a challenging task for these devices. In this work, a PI controller was studied for a dielectric electroactive polymer actuator. The pole placement problem for a closed-loop system with the PI controller was analyzed. The limitations of a PI controller in the pole placement problem are discussed. In this work, the analytic PI controller gain rules were obtained, and therefore extension to adaptive control is possible. To minimize the influence of unmodeled dynamics, the robust adaptive control law is applied. Furthermore, analysis of robust adaptive control was performed in a number of simulations and experiments.

Robust observer-based adaptive synchronization control of uncertain nonlinear bilateral teleoperation systems under time-varying delay

Due to the practical restrictions such as uncertain dynamics, external disturbances and time-varying delays and lack of enough information about the remote environment, the performance of teleoperation systems cannot be guaranteed in most cases. However, some practical tasks such as tele-surgery conducted by the nonlinear teleoperation system require high performance. Recent research on teleoperation systems motivates us to propose a new robust adaptive control law for nonlinear bilateral teleoperation systems with time-varying delays, external disturbances and uncertain dynamics in a unified framework. To this end, a novel adaptive torque observer is developed to relax the system from force sensors. The main advantage of this paper is to design properly a mechanism, which is free from joint accelerations due to the acceleration measurement difficulty in robotic systems. Moreover, the key point of the proposed algorithm is to enhance the robust behavior of the system in the presence of various uncertainties by adding an auxiliary term in the control loop. Furthermore, the stability and the convergence of synchronization error to zero are also proven with the aid of both Lyapunov-Krasovskii function and linear matrix inequality. Simulation results emphasize on the effectiveness of the proposed Observed-based control approach in comparison with the related research.

Adaptive State-Feedback Shared Control for Constrained Uncertain Mechanical Systems

This study develops two kinds of adaptive state feedback shared-control algorithms for a class of nonlinear mechanical systems in the presence of output constraints described by a set of linear inequalities. The parametric uncertainties and unknown external disturbances in the dynamical model are compensated by the proposed adaptive control laws for the feedback controller. In particular, an adaptive fixed-time state-feedback control law is also developed after the classical robust adaptive state-feedback control law design. The properties of the closed-loop shared-control systems are studied by using Lyapunov method. Simulation results demonstrate the effectiveness of the proposed approach.

Robust Adaptive Tracking Control for Hypersonic Vehicle Based on Interval Type-2 Fuzzy Logic System and Small-Gain Approach.

This paper presents a novel robust adaptive tracking control method for a hypersonic vehicle in a cruise flight stage based on interval type-2 fuzzy-logic system (IT2-FLS) and small-gain approach. After the input-output linearization, the vehicle model can be decomposed into two uncertain subsystems by considering matching disturbances and parametric uncertainties. For each subsystem, an interval type-2 Takagi-Sugeno-Kang fuzzy logic system (IT2-TSK-FLS) is then employed to approximate the unavailable model information. Following the idea of a small-gain approach, a composite feedback form for each subsystem is constructed, based on which the final robust adaptive tracking control law is developed. Rigorous stability analysis shows that all signals in the derived closed-loop system are kept uniformly ultimately bounded (UUB). The main contribution of this paper is that the proposed control law for the hypersonic vehicle is with only two adaptive parameters in total which can greatly alleviate the computation and storage burden in practice; meanwhile its superiority over the conventional minimal-learning-parameter (MLP)-based one is specifically illustrated. Comparative numerical simulations of three cases demonstrate the effectiveness of our proposed control method with respect to complicated uncertainties.

Adaptive Robust Tracking Control for Hybrid Models of Three-Dimensional Bipedal Robotic Walking Under Uncertainties

Abstract This paper introduces an adaptive robust trajectory tracking controller design to provably realize stable bipedal robotic walking under parametric and unmodeled uncertainties. Deriving such a controller is challenging mainly because of the highly complex bipedal walking dynamics that are hybrid and involve nonlinear, uncontrolled state-triggered jumps. The main contribution of the study is the synthesis of a continuous-phase adaptive robust tracking control law for hybrid models of bipedal robotic walking by incorporating the construction of multiple Lyapunov functions into the control Lyapunov function. The evolution of the Lyapunov function across the state-triggered jumps is explicitly analyzed to construct sufficient conditions that guide the proposed control design for provably guaranteeing the stability and tracking the performance of the hybrid system in the presence of uncertainties. Simulation results on fully actuated bipedal robotic walking validate the effectiveness of the proposed approach in walking stabilization under uncertainties.

Design and Optimization of Robust Path Tracking Control for Autonomous Vehicles With Fuzzy Uncertainty

Uncertainty is a major concern in vehicle path tracking control design. The coefficients of the uncertainty bound are unknown. They are assumed to lie within prescribed fuzzy sets. First, based on the path tracking kinematic model, this paper innovatively formulates the vehicle path tracking task as a constraint-following problem. Second, we put forward a deterministic adaptive robust control law with a tunable parameter to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop system. Third, an optimal scheme for the tunable parameter is proposed based on the fuzzy uncertainty. The resulting optimal robust control (ORC) minimizes a comprehensive fuzzy performance index that involves the fuzzy system performance and the control cost. The results of the CarSim-Simulink co-simulation and the Hardware-in-loop (HIL) experiment together show that the proposed optimal robust control exhibits a superior path tracking performance.

Intelligent coordinated control of an autonomous tractor-trailer and a combine harvester

This paper addresses the tracking control of an autonomous tractor-trailer robot with a desired distance and orientation angle relative to a combine harvester in the presence of model uncertainties for the autonomous unloading of the harvested cereals in agriculture applications. A coordinate transformation and the prescribed performance technique are employed to develop a second-order Euler-Lagrange formulation of the tracking errors. Then, a neural adaptive proportional-integral-derivative (PID) tracking controller is proposed to guarantee that the tracking errors exponentially converge to an arbitrary small ultimate bound with a pre-specified maximum overshoot and convergence rate. By an effective application of the prescribed performance technique, the controller non-singularity, the collision avoidance and connectivity between two vehicles are preserved continuously. The model uncertainties including unknown vehicle parameters, variable trailer mass and moment of inertia during the crop collection, surface friction, climate and crop conditions and external disturbances are compensated by an effective combination of a multi-layer neural network and an adaptive robust control law. Lyapunov's direct method is employed to prove that the tracking errors are semi-globally uniformly ultimately bounded and converge to a neighborhood of the origin with a prescribed performance. Finally, the computer simulation results are presented to demonstrate the effectiveness of the proposed controller.

Energy-saving and accurate motion control of a hydraulic actuator with uncertain negative loads

Pump controlled hydraulic actuators are wildly used in the aerospace industry owing to the advantages of energy-saving and integrated configurations. Negative loads may occur to actuators due to external force loads or the inertial force when the actuator decelerates significantly. Uncertain negative load working conditions may cause cavitation, actuator vibration, and even instability to the motion control if the actuator is without sufficient meter-out damping. Various types of hydraulic configuration schemes have been proposed to deal with negative loads of hydraulic actuators. However, few of them can simultaneously achieve energy saving and high control accuracy. This study proposes an energy-saving and accurate motion tracking strategy for a hydraulic actuator with uncertain negative loads. The actuator’s motion is driven by a servomotor pump, which gives full play to the advantage of energy-saving. The meter-out pressure is controlled by proportional valves to provide the optimized meter-out damping. The nonlinear adaptive robust control law is designed, which guarantees the control stability and achieve high tracking accuracy. An integrated direct/indirect adaptation law obtains satisfactory parameter estimations and model compensation for asymptotic motion tracking. Comparative experiments under different working conditions were performed to validate the advantages of the proposed control strategy.

Adaptive robust cross-coupling position synchronization control of a hydraulic press slider-leveling.

To achieve a high performance synchronized motion trajectory tracking of the hydraulic press slider-leveling electrohydraulic control system, an adaptive robust cross-coupling control strategy that incorporates the cross-coupling approach into adaptive robust control (ARC) architecture has been proposed. The primary objective of this study was describe that the nonlinear ARC controller together with a cross-coupling control (CCC) controller was integrated to solve the slider-leveling synchronization control system using four axes. A discontinuous projection-based ARC controller was constructed. A robust control method with dynamic compensation type fast adaptation was introduced to attenuate the effects of parameter estimation errors, unmodeled dynamics and disturbances, and improved the transient tracking performance of the system. The stability of the controller was proven by Lyapunov theory and the trajectory tracking error asymptotically convergences to zero. The simulation of a desired reference trajectory was included. The max tracking error of the proposed ARC controller of single axis was kept within-0.06 mm. The trajectory tracking error asymptotically converges to zero, which guaranteed the system would possess good transient behavior and confirmed the stability performance of the control system. The four axes synchronous errors of reference trajectory with cross-coupling controller indicated the maximum synchronization error of the proposed ARC + CCC controller between axis was within ±0.1 mm. The ARC together with a CCC controller for four hydraulic cylinders used parameter adaptation to obtain estimates of model parameters for reducing the extent of parametric uncertainties, and used a robust control law to attenuate the effects of parameter estimation errors, unmodeled dynamics, and disturbances. This study result shows that the proposed cross-coupling synchronization control scheme, together with the ARC law, provides excellent synchronization motion performance in a control system with four axes.

Robust Attitude Stabilization of Spacecraft Under Constrained Network With Hysteresis Quantizer

This article investigates the application of hysteresis quantizer for the spacecraft regulation problem under a constrained network. The hysteresis quantizer helps in quantizing the continuous control command at a lower rate of communication while avoiding the chattering in the quantized signal. The proposed scheme employs robust adaptive control law along with a filter error similar to the terminal sliding surface. Two separate adaptive laws are implemented to avoid the overestimation problem. The filter error is specially designed owing to the fact that the quaternion-based attitude representation faces the problem of unwinding. The theoretical analysis of the proposed scheme guarantees the uniformly ultimately bounded (UUB) stability of the closed-loop system, which is subjected to the quantization error, inertial uncertainties, and external disturbances. Moreover, it is also proved that the unwinding problem does not arrive in the proposed strategy. The validation of theoretical results is tested through extensive numerical simulation. Furthermore, the comparative analysis with the existing results shows the efficacy of the proposed approach.

Enhancement of High-Fidelity Haptic Feedback Through Multimodal Adaptive Robust Control for Teleoperated Systems

Achieving high-fidelity haptic feedback in teleoperated robotic systems is crucial, particularly in applications, such as medical, industrial, military, space, and underwater explorations. The quality of haptic feedback depends to a great extent on the controller design objectives, such as stability and transparency. In addition to the time delay, the most challenging issues that affect haptic feedback are model uncertainties, parametric variations, and external disturbances. In this article, such issues have been addressed by developing a novel multimodal adaptive robust control algorithm for teleoperated systems with time-varying delay. Here, parametric convergence is achieved swiftly with a milder condition than a stronger persistent excitation condition on the regressor signal, resulting in the enhancement of transient performance. The proposed method ensures the robustness of the system against bounded perturbations and high-frequency unmodeled dynamics using a modified terminal sliding-mode controller alongside the adaptive scheme. The proposed method promises high-fidelity haptic feedback to the operator by ensuring improved robust global asymptotic stability, synchronization, and transparency, addressing parametric variations and disturbances. The validity of the proposed adaptive robust control law is substantiated through simulations and comparisons with the existing methods for a 2-R teleoperation system. The experimental results for a 1-R teleoperation system are also presented.