To enhance the lower limbs' rehabilitation training of stroke patients, in this article, a deterministic adaptive robust control with control gain parameters optimized by a novel cooperative game theory is proposed for the two-degree-of-freedom (DOF) lower limb exoskeleton robot system (LLERs) with uncertainties and external disturbances. On the one hand, the deterministic adaptive robust control put forward will guarantee the uniform boundedness and uniform ultimate boundedness of gait constraint deviation and parameter estimation error. On the other hand, for uncertainties and external disturbance (possibly fast time-varying), which will arise in the two-DOF LLERs inevitably, we suppose that these uncertainties and disturbances are bounded, and their bounds are lying within the fuzzy sets, which can be characterized by membership functions; with such descriptions and control performance analysis, a novel cooperative game with two players participated will be formulated to seek the Pareto optimal solutions for the control gains of deterministic adaptive robust control proposed, and furthermore, the existence of optimal solutions is also shown by invoking a numerical technique. Eventually, the simulation results presented have verified the effectiveness of our proposed methodology on improving rehabilitation training of lower limbs.

This paper attempts to address the trajectory following control problem of nonholonomic mobile AGV by proposing an improved sliding mode control approach in which, based on the kinematics and attitude deviations established for AGV, the motion characteristics are analyzed and a backstepping sliding mode control with a novel reaching law is designed. This reaching law integrates the merits of the power and exponential reaching laws and promotes the convergence rates of tracking errors. Moreover, with the improved sliding mode controller, the asymptotic stability of tracking deviations can be strictly guaranteed. The simulations have demonstrated the effectiveness and superiority of the proposed approach for mobile AGV.

In this paper, a novel adaptive robust control approach has been proposed for a class of uncertain mechanical systems. First, aiming for the unknown uncertainties which may be fast time-varying, a novel adaptive mechanism in leakage-type will be developed. Unlike the prevalent adaptive methods, this adaptive mechanism put forward mainly accounts for estimating all the lumped uncertainty terms, and does not require any information of uncertainties other than they are bounded; Second, through transforming the gait following assignment to the constraint control, an adaptive robust constraint-following controller constructed will render the constraints uniformly bounded and uniformly ultimately bounded. To testify the efficacy of proposed approach, a lower limb exoskeleton robot is considered as an illustrative example, the simulations indicate that the proposed control can indeed improve the level of passive rehabilitation training.

Aiming to address the time-varying formation control problem of uncertain and networked multiagent systems, this article develops a novel robust constraint-following control approach, which is implemented in two steps. First, a kinematic model (i.e., kinematic constraint) for each agent is constructed by employing local tracking errors; this constraint mainly ensures formation maintenance and trajectory following. Second, based on the kinematic model, and to attenuate time-varying uncertainties, a robust constraint-following controller is meticulously designed. This controller not only ensures the strict satisfaction of kinematic constraints but also sufficiently suppresses time-varying uncertainties. Ultimately, an application to quadrotor swarm is provided to illustrate the proposed control scheme.