This paper focuses on the problem of delay- dependent stability analysis of neural networks with variable delay. Two types of variable delay are considered: one is differentiable and has bounded derivative; the other one is continuous and may vary very fast. By introducing a new type of Lyapunov-Krasovskii functional, new delay-dependent sufficient conditions for exponential stability of delayed neural networks are derived in terms of linear matrix inequalities. We also obtain delay-independent stability criteria. These criteria can be tested numerically and very efficiently using interior point algorithms. Two examples are presented which show our results are less conservative than the existing stability criteria.

An LMI approach to exponential stability analysis of neural networks with time-varying delay

In this paper, an adaptive neural network (NN)-based tracking control algorithm is proposed for the wheeled mobile robotic (WMR) system with full state constraints. It is the first time to design an adaptive NN-based control algorithm for the dynamic WMR system with full state constraints. The constraints come from the limitations of the wheels’ forward speed and steering angular velocity, which depends on the motors’ driving performance. By employing adaptive NNs and a barrier Lyapunov function with error variables, then, the unknown functions in the systems are estimated, and the constraints are not violated. Based on the assumptions and lemmas given in this paper and the references, while the design and the system parameters chose properly, our proposed scheme can guarantee the uniform ultimate boundedness for all signals in the WMR system, and the tracking error converge to a bounded compact set to zero. The numerical experiment of a WMR system is presented to illustrate the good performance of the proposed control algorithm.

Adaptive Neural Network-Based Tracking Control for Full-State Constrained Wheeled Mobile Robotic System

In this paper, a dynamic model of a wheeled mobile robotic (WMR) system with coupled control input is developed, which will increase the complexity of its tracking control with time-varying advance angle. To deal with this problem, a partial reinforcement learning neural network (PRLNN)-based tracking algorithm is proposed for the WMR systems. The main contributions of the PRLNN adaptive tracking control method is that it is the first control method to introduce the PRLNN adaptive control to the WMR system, which determines to solve the WMR tracking control with the time-varying advance angle. The critic neural network (NN) and action NN adaptive laws for the decoupled controllers are designed using the standard gradient-based adaptation method. According to the Lyapunov stability analysis theorem, the uniform ultimate boundedness of all signals in the WMR system can be guaranteed with the design parameters chose properly, and the tracking error converge to a small compact set nearby zero. A numerical simulation is presented to verify the effectiveness of the proposed control algorithm.

Adaptive Partial Reinforcement Learning Neural Network-Based Tracking Control for Wheeled Mobile Robotic Systems

As one of the most important issues in the field of mobile robotics, self-localization allows a mobile robot to identify and keep track of its own position and orientation as the robot moves through the environment. In this work, a hybrid localization approach based on the particle filter and particle swarm optimization algorithm is presented, focusing on the localization tasks when an a priori environment map is available. This results an accurate and robust particle filter based localization algorithm that is able to work in symmetrical environments. The performance of the proposed approach has been evaluated for indoor robot localization and compared with two benchmark algorithms. The experimental results show that the proposed method achieves robust and accurate positioning results in indoor environments, requiring fewer particles than the benchmark methods. This advance could be integrated in a wide range of mobile robot systems, helping to reduce the computational cost and improve the navigation efficiency.

An improved particle filter for mobile robot localization based on particle swarm optimization

Four-wheel independently driven mobile robots are widely used in industrial automation, intelligent inspection, and outdoor exploration The traditional kinematic control is usually applied for them, where only the chassis kinematics is taken into account and the robot dynamics (especially the wheel dynamics) is normally ignored It may lead to some performance limitations such as the chattering phenomenon during robot rotating, because of the overactuation characteristic by four driving wheels To address these problems, the integrated dynamic model is proposed, which includes chassis kinematics, chassis dynamics, wheel–ground interaction, and wheel dynamics Subsequently, different from kinematic control, a model-based coordinated adaptive robust controller is developed, which generally consists of three-level designs for different parts of robot dynamics, and directly generates the motor driving torque commands for four wheels The stability and tracking performance are theoretically guaranteed Comparative experiments are carried out, and the results show the better performance of our proposed scheme

Model-Based Coordinated Control of Four-Wheel Independently Driven Skid Steer Mobile Robot with Wheel–Ground Interaction and Wheel Dynamics

As a major representative nonholonomic system, wheeled mobile robot (WMR) is often used to travel across off-road environments that could be unstructured environments. Slippage often occurs when WMR moves in slopes or uneven terrain, and the slippage generates large accumulated position errors in the vehicle, compared with conventional wheeled mobile robots. An estimation of the wheel slip ratio is essential to improve the accuracy of locomotion control. In this paper, we propose an improved adaptive controller to allow WMR to track the desired trajectory under unknown longitudinal slip, where the stabilisation of the closed-loop tracking system is guaranteed by the Lyapunov theory. All system states use neural network online weight tuning algorithms, which ensure small tracking errors and no loss of stability in robot motion with bounded input signals. We demonstrate superior tracking results using the proposed control method in various Matlab simulations.

Adaptive motion control of wheeled mobile robot with unknown slippage

Microstructural evolution and mechanical properties of vacuum brazed Ti 2 AlNb alloy and Ti60 alloy with Cu75 P t filler metal

In this paper, we investigate the global exponential stability of delayed neural network systems. For this purpose, the activation functions are assumed to be globally Lipschitz continuous. The properties of norms and the relationship of homeomorphism are adjusted to ensure the existence as well as the uniqueness of the equilibrium point. Then by employing suitable Lyapunov functional, some delay-independent sufficient conditions are derived for exponential convergence toward global equilibrium state associated with different input sources. The obtained results are shown to be more general and less restrictive than the previous results derived in the literature. Lastly, a number of examples are provided to demonstrate the validity of the results proposed.

New conditions for global exponential stability of continuous-time neural networks with delays

The effect of Pt content dispersed in Ag-CuO fillers on the interfacial microstructure and properties of Al2O3 ceramic joints brazed in air was investigated. Pt addition did not influence the variety of phases formed during reactive air brazing of Al2O3 joints. The position and morphology of CuO phase changed with Pt content which varied from 0 to 13.6 wt. %. Pt dissolved into Ag matrix completely. The typical interfacial sequence of brazed joints was Al2O3/CuAl2O4/CuO/Ag(s, s)/CuO/CuAl2O4/Al2O3. The addition of Pt to the Ag-CuO filler enhanced significantly the strength of Al2O3 joint from room temperature and up to 600℃. The joints brazed with Ag-CuO-9.0 wt. % Pt had the highest average strength at room temperature and at 600℃. The strength increased by 17 % and almost 100 %, respectively, compared with the Pt-free joint.

Xingguo Song

Papers

Adaptive motion control of wheeled mobile robot with unknown slippage

Microstructural evolution and mechanical properties of vacuum brazed Ti 2 AlNb alloy and Ti60 alloy with Cu75 P t filler metal

New conditions for global exponential stability of continuous-time neural networks with delays

Reactive air brazing of Al2O3 ceramic with Ag-CuO-Pt composite fillers: Microstructure and joint properties

Gait optimization of step climbing for a hexapod robot