adaptive control

摘要： Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this paper.Both continuous and discontinuous activations are considered forMNNs.And the mixed delays which are closer to reality are taken into the system.Besides,two kinds of control schemes are proposed,including feedback and adaptive control strategies.Based on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are acquired.Moreover,the upper bound of settling time(ST)which is independent of the initial values is given.Finally,the feasibility of our theory is attested by simulation examples.

摘要： It is nontrivial to achieve global zero-error regulation for uncertain nonlinear systems.The underlying problem becomes even more challenging if mismatched uncertainties and unknown time-varying control gain are involved,yet certain performance specifications are also pursued.In this work,we present an adaptive control method,which,without the persistent excitation(PE)condition,is able to ensure global zero-error regulation with guaranteed output performance for parametric strict-feedback systems involving fast time-varying parameters in the feedback path and input path.The development of our control scheme benefits from generalized-dependent and-dependent functions,a novel coordinate transformation and“congelation of variables”method.Both theoretical analysis and numerical simulation verify the effectiveness and benefits of the proposed method.

摘要： This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

摘要： Updates to traffic signal timing plans are expected to either improve operations or mitigate the effects of increased volumes. Longitudinal before-after studies are important when validating changes to traffic signal systems, but they have historically required field data collection as well as deployment of extensive detection and communication equipment. These infrastructure-based techniques are costly and hard to scale. This study utilizes commercially available connected vehicle (CV) trajectory data to assess the change in performance between August 2020 and August 2021 on a 22-intersection corridor associated with the implementation of a semi-automated adaptive control system. Approximately 1 million trajectories and 13.5 million GPS points are analyzed for weekdays in August 2020 and August 2021. The vehicle trajectory data is used to compute corridor travel times and linear referenced relative to the far side of each intersection to generate Purdue Probe Diagrams (PPD). Using the PPDs, operational measurements such as arrivals on green (AOG), split failures (SF), and downstream blockage (DSB) are calculated. Additionally, traditional Highway Capacity Manual (HCM) level of service (LOS) is estimated. Even though there was a 35% increase in annual average daily traffic (AADT), the weighted average vehicle delay only increased by two seconds, LOS did not change, AOG improved by 1%, and SF and DSB remained the same. Based on the small changes in operational performance and considering the increase in traffic volume it is concluded that the implementation of the semi-automated adaptive control system had a significant positive impact in the corridor. The presented framework can be utilized by agencies to use CV data to perform before-after studies to evaluate the impact of signal timing plan changes. The presented methodology can be applied to any location where CV trajectory data is available.

摘要： This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible joint.The proposed AGDI control law will be comprised of two control elements.The baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives.The control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized inversion.To boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode principle.In discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction.The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed.Rigorous computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

摘要： An adaptive controller involving a neural network(NN)compensator is proposed to resist the uncertainties in the Euler-Lagrangian system(ELS).Firstly,a proportional-differential(PD)control law is designed for the nominal model.Meanwhile,the uncertainties including model error and external disturbance are separated from the closed-loop system.Then,an adaptive NN compensator based on the online training mode is proposed to eliminate the adverse effect of the uncertainties.In addition,the stability of the closed-loop system is proved by Lyapunov theory.Finally,the effectiveness of the proposed approach is verified on a two-degree-of-freedom robot manipulator.

摘要： In this paper,we address simultaneous control of a flexible spacecraft’s attitude and vibrations in a three-dimensional space under input disturbances and unknown actuator failures.Using Hamilton’s principle,the system dynamics is modeled as an infinite dimensional system captured using partial differential equations.Moreover,a novel adaptive fault tolerant control strategy is developed to suppress the vibrations of the flexible panel in the course of the attitude stabilization.To determine whether the system energies,angular velocities and transverse deflections,remain bounded and asymptotically decay to zero in the case wherein the number of actuator failures is infinite,a Lyapunov-based stability analysis is conducted.Finally,extensive numerical simulations are performed to demonstrate the performance of the proposed adaptive control strategy.

摘要： This paper solves the robust control problem of robotic manipulator systems with uncertain dynamics by friction compensation approach. A weighting factor is introduced to distinguish the role of friction in control process by comparing the directions of sliding vector and friction. Utilizing the weighting factor, model-based and model-free adaptive friction compensation controllers are designed to achieve asymptotical tracking of the desired joint-space trajectory according to the knowledge of friction. The damping property of friction is fully used to improve the control performance by compensating the friction harmful for the stability, and on the other hand, utilizing the beneficial friction. Numerical simulations are given to demonstrate the control performance of the proposed approach.

摘要： This paper proposes a sliding-mode disturbance observer(SMDOB)-based tracking controller for a class of nonlinear systems with modeling uncertainties and external disturbances.The SMDOB is constructed using an extended state observer embedded by a filtered sliding mode term.The chattering caused by the sliding mode is compressed by the frequency bandwidths of both the extended state observer and the control system.The novelties of the proposed controller are as follows:(1)The semiglobal asymptotical stability of the combined controller-observer system is guaranteed without the boundedness assumption of the time derivatives of modeling uncertainties;(2)the SMDOB can be implemented with a low complexity because of only three parameters to be tuned.Applications to robot manipulators illustrate the effectiveness of the SMDOB-based tracking control strategy.

摘要： This paper presents an adaptive state-feedback strategy for state-constrained stochastic highorder nonlinear systems.By adding a power integrator and adaptive backstepping techniques,a new adaptive controller is constructed without imposing feasibility conditions,which guarantees that all closed-loop signals are bounded almost surely,full-state constraints are not violated almost surely,and the trivial solution of the closed-loop system is stochastically asymptotically stable.

摘要： A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle（NHV) in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive control （OGPC) method and functional link network（FLN) adaptive adjustment law. The output prediction defined on finite horizon is carried out via Taylor series expansion. The FLN adaptive law is used to cancel the unmodeled dynamics and unknown disturbances in flight through online learning. The stability analyses of the close-loop system are provided also. The simulation results show satisfactory performance of the controller for the tracking of reference attitude angles and angular accelerations，and the robustness to parameters variations and the disturbance rejection are successfully accomplished.

摘要： A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle（NHV) in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive control （OGPC) method and functional link network（FLN) adaptive adjustment law. The output prediction defined on finite horizon is carried out via Taylor series expansion. The FLN adaptive law is used to cancel the unmodeled dynamics and unknown disturbances in flight through online learning. The stability analyses of the close-loop system are provided also. The simulation results show satisfactory performance of the controller for the tracking of reference attitude angles and angular accelerations，and the robustness to parameters variations and the disturbance rejection are successfully accomplished.

摘要： A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle（NHV) in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive control （OGPC) method and functional link network（FLN) adaptive adjustment law. The output prediction defined on finite horizon is carried out via Taylor series expansion. The FLN adaptive law is used to cancel the unmodeled dynamics and unknown disturbances in flight through online learning. The stability analyses of the close-loop system are provided also. The simulation results show satisfactory performance of the controller for the tracking of reference attitude angles and angular accelerations，and the robustness to parameters variations and the disturbance rejection are successfully accomplished.

摘要： A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle（NHV) in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive control （OGPC) method and functional link network（FLN) adaptive adjustment law. The output prediction defined on finite horizon is carried out via Taylor series expansion. The FLN adaptive law is used to cancel the unmodeled dynamics and unknown disturbances in flight through online learning. The stability analyses of the close-loop system are provided also. The simulation results show satisfactory performance of the controller for the tracking of reference attitude angles and angular accelerations，and the robustness to parameters variations and the disturbance rejection are successfully accomplished.

摘要： A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle（NHV) in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive control （OGPC) method and functional link network（FLN) adaptive adjustment law. The output prediction defined on finite horizon is carried out via Taylor series expansion. The FLN adaptive law is used to cancel the unmodeled dynamics and unknown disturbances in flight through online learning. The stability analyses of the close-loop system are provided also. The simulation results show satisfactory performance of the controller for the tracking of reference attitude angles and angular accelerations，and the robustness to parameters variations and the disturbance rejection are successfully accomplished.