A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied. The designed RL control algorithm is implemented by a radial basis function (RBF) neural network (NN), in which the actor NN is used to generate the control strategy and the critic NN is used to evaluate the execution cost. A new nonsingular fast terminal sliding mode technique is used to ensure the convergence of tracking error in fixed time, and the upper bound of convergence time is estimated. To solve the saturation problem of an actuator, a nonlinear antiwindup compensator is designed to compensate for the saturation effect of the joint torque actuator in real time. Finally, the stability of the closed-loop system based on the Lyapunov candidate is analyzed, and the timing convergence of the closed-loop system is proven. Simulation and experimental results show the effectiveness and superiority of the proposed control law.

The asymptotic stabilization problem for a class of nonlinear under-actuated systems is studied and solved. Its solution, together with the back-stepping and the forwarding control design methods, is exploited in the control of the nonlinear lateral dynamics of a vehicle. Even though the theoretical studies of the lateral control of autonomous vehicles are traditionally applied to lane keeping cases, the results can be applied to broader range of areas, such as lane changing cases. The comparison between the performances of the closed-loop systems with the given controller and a typical human driver is given and demonstrates the speediness and the effectiveness of the feedback controller.

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.

The stabilization problem for a class of nonlinear systems is solved via a novel method inspired by back-stepping. The method, that we call underactuated back-stepping, is introduced by solving the stabilization problem for an inertia wheel pendulum and it is then developed for a class of underactuated mechanical systems. The properties of the resulting closed-loop systems are studied in detail and case studies are given to show the effectiveness of the proposed method.

This paper proposes a less-disturbed ecological driving strategy for connected and automated vehicles (CAVs). The proposed strategy integrates the offline planning and the online tracking. In offline planning, an energy efficient reference speed is created based on traffic information (such as the average traffic speed) and characteristics of the vehicle (such as the engine efficiency map) via dynamic programming. The consideration of average traffic speed in speed planning avoids selfish optimisations. In online tracking, model predictive control is employed to update the vehicle speed in real-time to track the reference speed. A key challenge in applying ecological driving strategies is that the vehicle has to consider other traffic participants when tracking the reference speed. Therefore, this paper combines both longitudinal and lateral control to achieve better speed tracking by overtaking the preceding vehicle when necessary. The proposed less-disturbed ecological driving strategy has been evaluated in simulations in both single road segment scenario and real traffic environment. Comparisons of the proposed method with benchmark strategies and human drivers are made. The results demonstrate that the proposed strategy is more effective in energy saving. Compared to human drivers, the less-disturbed eco-driving strategy improves the fuel efficiency of CAVs by 4.53%.

An adaptive relative pose controller for docking ports of two uncertain spacecraft in autonomous rendezvous and docking is developed. A novel relative translational and rotational model represented in the chaser body-fixed frame is derived firstly based on the classical Newton-Euler equations. Based on the proposed model, a six-degrees-of-freedom adaptive control law is presented based on norm-wise estimations for the unknown parameters of two spacecraft to decrease the online computational burden. Meanwhile, an adaptive robust control input is designed by introducing an exponential function of states to improve the response performance with respect to the traditional adaptive robust control. Moreover, a linear anti-windup compensator is employed to ensure the bounded performance of the control inputs. The explicit tuning rules for designing parameters are derived based on the stability analysis of the closed-loop system. It is proved in Lyapunov framework that all closed-loop signals are always bounded and the pose tracking error ultimately converges to a small neighborhood of zero. Simulation results validate the performance of the proposed robust adaptive control strategy.