Backstepping Based Global Exponential Stabilization of a Tracked Mobile Robot with Slipping Perturbation

While the nonholonomic robots with no-slipping constraints are studied extensively nowadays, the slipping effect is inevitable in many practical applications and should be considered necessarily to achieve autonomous navigation and control purposes especially in outdoor environments. In this paper the robust point stabilization problem of a tracked mobile robot is discussed in the presence of track slipping, which can be treated as model perturbation that violates the pure nonholonomic constraints. The kinematic model of the tracked vehicle is created, in which the slipping is assumed to be a time-varying pa- rameter under certain assumptions of track-soil interaction. By transforming the original system to the special chained form of nonholonomic system, the integrator backstepping procedure with a state-scaling technique is used to construct the controller to stabilize the system at the kinematic level. The global exponential stability of the final system can be guaranteed by Lyapunov theory. Simulation results with different initial states and slipping parameters demonstrate the fast convergence, robustness and insensitivity to the initial state of the proposed method.

A new modeling scheme for powered parafoil unmanned aerial vehicle platforms: Theory and experiments

A novel framework is established for accurate modeling of Powered Parafoil Unmanned Aerial Vehicle(PPUAV). The model is developed in the following three steps: obtaining a linear dynamic model, simplifying the model structure, and estimating the model mismatch due to model variance and external disturbance factors. First, a six degree-of-freedom linear model, or the structured model, is obtained through dynamic establishment and linearization. Second, the data correlation analysis is adopted to determine the criterion for proper model complexity and to simplify the structured model. Next, an active model is established, combining the simplified model with the model mismatch estimator. An adapted Kalman filter is utilized for the real-time estimation of states and model mismatch. We finally derive a linear system model while taking into account of model variance and external disturbance. Actual flight tests verify the effectiveness of our active model in different flight scenarios.

Quantified assessment of deep brain stimulation on Parkinson’s patients with task fNIRS measurements and functional connectivity analysis:a pilot study

Background:Deep brain stimulation(DBS)has proved effective for Parkinson’s disease(PD),but the identification of stimulation parameters relies on doctors’subjective judgment on patient behavior.Methods:Five PD patients performed 10-meter walking tasks under different brain stimulation frequencies.During walking tests,a wearable functional near-infrared spectroscopy(fNIRS)system was used to measure the concentration change of oxygenated hemoglobin(ΔHbO_(2))in prefrontal cortex,parietal lobe and occipital lobe.Brain functional connectivity and global efficiency were calculated to quantify the brain activities.Results:We discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters,and the DBS pattern enabling the highest brain efficiency was optimal for each patient,in accordance with the clinical assessments and DBS treatment decision made by the doctors.Conclusions:Task fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment.Trial registration:Name:Accurate treatment under the multidisciplinary cooperative diagnosis and treatment model of Parkinson’s disease.Registration number is ChiCTR1900022715.Date of registration is April 23,2019.