Adaptive Nonlinear Tire-Road Friction Force Estimation for Vehicular Systems Based on a Novel Differentiable Friction Model

A novel adaptive nonlinear observer-based parameter estimation scheme using a newly continuously differentiable friction model has been developed to estimate the tire-road friction force. The differentiable friction model is more flexible and suitable for online adaptive identification and control with the advantage of more explicit parameterizable form. Different from conventional estimation methods, the filtered regression estimation parameter is introduced in the novel adaptive laws, which can guarantee that both the observer error and parameter error exponentially converge to zero. Lyapunov theory has been used to prove the stability of the proposed methods. The effectiveness of the estimation algorithm is illustrated via a bus simulation model in the Trucksim software and simulation environment. The relatively accurate tire-road friction force was estimated just by the easily existing sensors signals wheel rotational speed and vehicle speed and the proposed method also displays strong robustness against bounded disturbances

Joint friction estimation and slip prediction of biped walking robots

Friction is a nonlinear and complex phenomenon. It is unwanted at the biped joints since it deteriorates the robot’s walking performance in terms of speed and dynamic behavior. On the other hand, it is desired and required between the biped feet and the walking surface to facilitate locomotion. Further, friction forces between the feet and the ground determine the maximum acceleration and deceleration that the robot can afford without foot slip. Although several friction models are developed, there is no exact model that represents the friction behavior. This is why online friction estimation and compensation enter the picture. However, when online model-free estimation is difficult, a model-based method of online identification can prove useful. This thesis proposes a new approach for the joint friction estimation and slip prediction of walking biped robots. The joint friction estimation approach is based on the combination of a measurementbased strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, it utilizes the force and acceleration measurements in a reduced dynamical model of the biped. The latter adopts a friction model to represent the joint friction when the leg is swinging. The model parameters are identified adaptively using the estimated online friction whenever the foot is in contact. Then the estimated joint friction contributes to joint torque control signals to improve the control performance. The slip prediction is a model-free friction-behavior-inspired approach. A measurement-based online algorithm is designed to estimate the Coulomb friction which is regarded as a slip threshold. To predict the slip, a safety margin is introduced in the negative vicinity of the estimated Coulomb friction. The estimation algorithm concludes that if the applied force is outside the safety margin, then the foot tends to slip. The proposed estimation approaches are validated by experiments on SURALP (Sabanci University Robotics Research Laboratory Platform) and simulations on its model. The results demonstrate the effectiveness of these methods

Identification and Control of Nonlinear Singularly Perturbed Systems Using Multi-time-scale Neural Networks

Many industrial systems are nonlinear with "slow" and "fast" dynamics because of the presence of some ``parasitic" parameters such as small time constants, resistances, inductances, capacitances, masses and moments of inertia. These systems are usually labeled as "singularly perturbed" or ``multi-time-scale" systems. Singular perturbation theory has been proved to be a useful tool to control and analyze singularly perturbed systems if the full knowledge of the system model parameters is available. However, the accurate and faithful mathematical models of those systems are usually difficult to obtain due to the uncertainties and nonlinearities. To obtain the accurate system models, in this research, a new identification scheme for the discrete time nonlinear singularly perturbed systems using multi-time-scale neural network and optimal bounded ellipsoid method is proposed firstly. Compared with other gradient descent based identification schemes, the new identification method proposed in this research can achieve faster convergence and higher accuracy due to the adaptively adjusted learning gain. Later, the optimal bounded ellipsoid based identification method for discrete time systems is extended to the identification of continuous singularly perturbed systems. Subsequently, by adding two additional terms in the weight's updating laws, a modified identification scheme is proposed to guarantee the effectiveness of the identification algorithm during the whole identification process. Lastly, through introducing some filtered variables, a robust neural network training algorithm is proposed for the system identification problem subjected to measurement noises. Based on the identification results, the singular perturbation theory is introduced to decompose a high order multi-time-scale system into two low order subsystems -- the reduced slow subsystem and the reduced fast subsystem. Then, two controllers are designed for the two subsystems separately. By using the singular perturbation theory, an adaptive controller for a regulation problem is designed in this research firstly. Because the system order is reduced, the adaptive controller proposed in this research has a simpler structure and requires much less computational resources, compared with other conventional controllers. Afterward, an indirect adaptive controller is proposed for solving the trajectory tracking problem. The stability of both identification and control schemes are analyzed through the Lyapunov approach, and the effectiveness of the identification and control algorithms are demonstrated using simulations and experiments