In this article, a continuous terminal sliding mode control algorithm is proposed for servo motor systems. A novel full-order terminal sliding mode surface is proposed based on the bilimit homogeneous property, such that the sliding motion is finite-time stable independent of the system's initial condition. A new continuous terminal sliding mode control algorithm is proposed to guarantee that the system states reach the sliding surface in finite-time. Not only the robustness is guaranteed by the proposed controller but also the continuity makes the control algorithm more suitable for the servo mechanical systems. Finally, a numerical example is presented to depict the advantages of the proposed control algorithm. An application in the rotary servo system is done to validate the effectiveness of the proposed control strategy.

This paper presents a new cascaded extended state observer (CESO) based sliding mode control (SMC) for an underactuated flexible joint robot (FJR). The control of FJR has many challenges including coupling, underactuation, nonlinearity, uncertainties and external disturbances, and the noise amplification especially in the high order systems. The proposed control integrates CESO and SMC, in which the CESO estimates the states and disturbances, and the SMC provides the system robustness to the uncertainty and disturbance estimation errors. Firstly, a dynamic model of the FJR is derived and converted from an underactuated form to a canonical form via the Olfati transformation and flatness approach, which reduces the complexity of the controller design. Furthermore, by taking the advantage of available measurable states, the CESO is adopted to attenuate the noises and make SMC feasible for high order systems. Moreover, the CESO estimates the disturbances, which relaxes the upper bound of the disturbance in the SMC and reduces the chattering due to smaller switching gains. A stability analysis of the closed loop system is presented based on the Lyapunov method. The effectiveness of the proposed control is verified experimentally on a real time FJR system.

Flexible joint robot (FJR) manipulators can offer many attractive features over rigid manipulators, including light weight, safe operation, and high power efficiency. However, the tracking control of the FJR is challenging due to its inherent problems, such as underactuation, coupling, nonlinearities, uncertainties, and unknown external disturbances. In this article, a terminal sliding mode control (TSMC) is proposed for the FJR system to guarantee the finite-time convergence of the systems output, and to achieve the total robustness against the lumped disturbance and estimation error. By using two coordinate transformations, the FJR dynamics is turned into a canonical form. A cascaded finite-time sliding mode observer (CFTSMO) is constructed to estimate states and lumped disturbance in a finite time based on two measurable states, which not only attenuates the measurement noise but also reduces the peaking phenomenon. The closed-loop stability and the finite-time convergence are rigorously proved by using Lyapunov theorem. The upper bound of the finite convergence time is derived for the reaching and sliding phase. Comparative study is conducted experimentally in real time on the FJR manipulator to verify the effectiveness of the proposed control method.

In this paper, the modeling of steer-by-wire (SbW) systems is further studied, and a sliding mode control scheme for the SbW systems with uncertain dynamics is developed. It is shown that an SbW system, from the steering motor to the steered front wheels, is equivalent to a second-order system. A sliding mode controller can then be designed based on the bound information of uncertain system parameters, uncertain self-aligning torque, and uncertain torque pulsation disturbances, in the sense that not only the strong robustness with respect to large and nonlinear system uncertainties can be obtained but also the front-wheel steering angle can converge to the handwheel reference angle asymptotically. Both the simulation and experimental results are presented in support of the excellent performance and effectiveness of the proposed scheme.

This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation.