This paper presents a reduced-order extended state observer (ESO) based sliding mode control scheme for friction compensation of a three-wheeled omnidirectional mobile robot. Compared with previous works, the proposed control approach is attractive from an implementation point of view. It does not require any explicit friction model, with quite low computation cost. Firstly, a dynamic model with unknown friction forces is given. Then the controller is designed, consisting of two parts. One part of the control effort is to compensate the friction effects, which are estimated by a reduced-order ESO without using any explicit friction model. The inverse of inertia matrix is also avoided in the proposed reduced-order ESO. The other part of the control effort is designed based on a second order sliding mode technique known as super-twisting algorithm, in presence of parameter uncertainties. In addition, stability analysis of the designed control system is presented. Extensive experiments are conducted to verify the effectiveness and robustness of the proposed control design in compensating different friction effects.

The Koopman operator theory offers a way to construct explicit control-oriented high-dimensional linear dynamical models for the original nonlinear systems, solely using the input–output data of the dynamical system. The modeling accuracy of the Koopman model largely depends on the basis functions (lifting functions), dimensionality, and data quality. However, there has not been a systematic way to solve the problems mentioned above. In this article, a Koopman-operator-based robust data-driven control framework is proposed for wheeled mobile robots, via incorporating tools from control theory, to solve the problem of modeling errors of the Koopman model. By employing an extended state observer, the modeling errors of the Koopman model, including unknown external disturbances, are online estimated and compensated in the control signal in real time. Then, sliding-mode control is used to synthesize the controller. Importantly, the method of virtual control input is proposed, to cope with the model errors arising from the rotational motion of all the mobile robots. Besides, stability analysis is conducted, and the optimal dimensionality of the Koopman model is experimentally selected. Finally, experimental tests on an omnidirectional mobile robot are carried out to verify the effectiveness of the proposed control scheme, in terms of tracking performance and robustness.

This paper proposes a pressing method for wall-climbing robots to prevent them from falling. In order to realize the method, the properties of the utilized suction cup are studied experimentally. Then based on the results, a guide rail is designed to distribute the attached suction cup force and implement the pressing method. A prototype of a wall-climbing robot that utilizes passive suction cups and one motor is used to demonstrate the proposed method. An experimental test-bed is designed to measure the force changes of the suction cup when the robot climbs upwards. The experimental results validate that the suction cup can completely attach to the surface by the proposed method, and demonstrate that the robot can climb upwards without falling.

Active disturbance rejection control (ADRC) is an efficient control technique to accommodate both internal uncertainties and external disturbances. In the typical ADRC framework, however, the design philosophy is to ``force'' the system dynamics into a double-integral form by an extended state observer (ESO) and then the controller is designed. Especially, the systems' physical structure has been neglected in such a design paradigm. In this article, a new ADRC framework is proposed by incorporating at a fundamental level the physical structure of the Euler-Lagrange (EL) systems. In particular, the differential feedback gain can be selected considerably small or even 0, due to the effective exploitation of the system's internal damping. The design principle stems from an analysis of the energy balance of EL systems, yielding a physically interpretable design. Moreover, the exploitation of the system's internal damping is thoroughly discussed, which is of practical significance for applications of the proposed design. Besides, a sliding-mode ESO is designed to improve the estimation performance over traditional linear ESO. Finally, the proposed control framework is illustrated through tracking control of an omnidirectional mobile robot. Extensive experimental tests are conducted to verify the proposed design as well as the discussions.

In this paper, an adaptive model predictive control (MPC) scheme with friction compensation, subject to incremental control input constraints and parameter uncertainties, is proposed for a three-wheeled omnidirectional mobile robot (OMR). The proposed control framework is in a cascaded structure, wherein the outer-loop is kinematic-based control and the inner-loop is designed based on adaptive linear MPC. First, a complex nonlinear dynamic model of the OMR in the world coordinate frame is transformed and partially linearized into a reduced nonlinear model in the moving coordinate system. The nonlinearity of the reduced model only arises from Coulomb friction. Then an estimated system is established for the reduced nonlinear system, with an adaptive update law estimating the system uncertain parameters. To facilitate the linear MPC design, part of the control efforts is derived by feedback compensation of the Coulomb friction forces, resulting in a linear estimated system. The other part is designed by a constrained linear MPC. Feasibility and stability analyses are given for the proposed adaptive MPC scheme. Finally, experimental comparisons with model-based MPC are carried out to verify the effectiveness of the proposed control scheme.