AILC for nonlinear systems with unknown time-varying control gain matrices

In this work, a novel adaptive iterative learning control (AILC) scheme is proposed for a class of uncertain multi-input multi-output (MIMO) systems, where the control gain matrices are both unknown and time-varying. In order to develop the AILC scheme without requiring the exact knowledge of the control gain matrix, a directional parameter is firstly introduced to indicate the control direction, which thus paves the way to the utilization of the Nussbaum gain technique. Furthermore, the parametric uncertainty and the unknown control gain matrix are transformed into a norm-based function, based on which both the feedback control law and the parametric updating law are established to ensure the perfect tracking performance of the system states along the iteration axis. The convergence of the tracking error is rigorously analyzed under the framework of the composite energy function (CEF). Finally, a numerical example is illustrated to demonstrate the effectiveness of the proposed AILC scheme.</p

Feedforward enhancement through iterative learning control for robotic manipulator

This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunov-like composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78° to 1.09°, and 21.09% to 3.99%, respectively, within three iterations

Robust adaptive learning-based path tracking control of autonomous vehicles under uncertain driving environments

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the λ-norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods. </p

Active lane change for safety enhanced autonomous driving

This paper proposed an active lane change algorithm for autonomous vehicles using safety-critical model predictive control method. By incorporating safety-critical constraints into the algorithm, the safety hazard caused by surrounding vehicles are addressed. The proposed algorithm can actively react to mandatory lane change requirement. This has been evaluated in random generated driving scenarios. A safety hazard caused by merging surrounding vehicle is highlighted to demonstrate the effectiveness of the control algorithm.</p

Learning feedforward control for industrial manipulators

In this work, an iterative learning control (ILC) algorithm is proposed for industrial manipulators. The proposed ILC algorithm works coordinately with the inverse dynamics of the manipulator and a feedback controller. The entire control scheme has the ability of compensating both repetitive and non-repetitive disturbances; guaranteeing the control accuracy of the first implementation; and improving the control accuracy of the manipulator progressively with successive iterations. In order to build the the convergence of the proposed ILC algorithm, a composite energy function is developed. A case study on a four degree of freedom industrial manipulator is demonstrated to illustrate the effectiveness of the proposed control scheme. By implementing the ILC algorithm, the maximum root mean square error of the control accuracy is improved from 0.0262 rad to 0.0016 rad within ten iterations

Cooperative power management for range extended electric vehicle based on internet of vehicles

The dramatic progress in internet of vehicles (IoVs) inspires further development in electrified transportation, and abundant information exchanged in IoVs can be infused into vehicles to promote the controlling performance of electric vehicles (EVs) via vehicle-environment cooperation. In this paper, a cooperative power management strategy (PMS) is advanced for the range extended electric vehicle (REEV). To this end, the studied REEV is accurately modelled first, laying an efficient platform for strategy design. Based on the advanced framework of IoVs, the cooperative PMS is meticulously developed via incorporating the self-learning explicit equivalent minimization consumption strategy (SL-eECMS) and adaptive neuro-fuzzy inference system (ANFIS) based online charging management within on-board power sources in the REEV. The brand-new SL-eECMS achieves preferable balance between the optimal effect and instant implementation capability through integrating the improved quantum particle swarm optimization (iQPSO), and ANFIS grasps future driving status macroscopically, offering the predicted charging request for online charge management. The substantial simulations and hardware-in-the-loop (HIL) test manifest that the proposed cooperative PSMS can coherently and efficiently manage power flow within power sources in the REEV, highlighting its anticipated preferable performance