Jidong Liu

A “planning and control” scheme with hysteresis compensation is developed in this article for a compliant grinding device (CGD) driven by a pneumatic actuator with asymmetric full-state constraints. First, in the planning part, we propose an improved hysteresis model and construct its inverse model through the modified inverse multiplicative structure method to deal with the hysteresis nonlinearity between air pressure and contact force in CGD. In the control part, a neural state observer (SO) is developed to observe the rate of change of air pressure by combining a neural network (NN) and an SO. By fusing an NN and a disturbance observer (DO), a neural DO is presented to observe external disturbances. Asymmetric full-state constraints are realized without the feasibility conditions (FCs) of the virtual controller by the fusion of the backstepping design process and nonlinear state-dependent transformations. It is proven that the air pressure closely tracks the planned desired air pressure, as the tracking error of the air pressure is uniformly ultimately bounded. Finally, hardware experiments show that the force control accuracy of the proposed method is within 2N, which achieves a satisfactory force control effect and verifies the effectiveness and robustness of the presented method.

This paper proposes a “planning and control” scheme for a compliant mechanism (CM) based on a pneumatic actuator (PAC) with hysteresis nonlinearity and full-state constraints. In the planning part, a novel direct inverse model is presented to compensate for hysteresis nonlinearity, enabling more accurate planning of the desired air pressure based on the desired contact force. In the control part, by fusing fuzzy logic systems (FLSs) and an extended state observer (ESO), a fuzzy ESO is developed to observe the external disturbance and the rate of change of the air pressure. Additionally, the challenges in the controller design caused by full-state constraints are overcome by constructing barrier Lyapunov functions (BLFs). It is proved that all signals of the closed-loop system are bounded, and the tracking error of the air pressure can converge to a small neighborhood of the origin. Finally, the effectiveness and robustness of the proposed method are verified by hardware experiments, which also show that the root mean square errors of force control accuracies are within 2N, achieving satisfactory force control effects.

Traditional industrial robots often face challenges in achieving a perfectly polished surface on a workpiece because of their high mechanical rigidity. The active compliance force control device installed at the robotic arm’s end enables high-precision contact force control between the grinding tool and the workpiece. However, the complex hysteresis nonlinearity between cylinder air pressure and output force, as well as various random disturbances during the grinding process, can affect the accuracy of the contact force and potentially impact the grinding effect of the workpiece, even causing irreversible damage to the surface of the workpiece. Given the complex random variation of cylinder output force in the actual grinding process, a rate-dependent hysteresis model based on diagonal recurrent neural network and Pradtl–Ishlinskii models named dRNN-PI is designed to compensate for the complex nonlinear hysteresis of the cylinder and calculate the desired air pressure to maintain a steady contact force on the workpiece. The proxy-based sliding mode control (PSMC) is utilized to quickly track the desired air pressure without overshooting. This paper also proves the controller’s stability using the Lyapunov-based methods. Finally, the accuracy of the proposed hysteresis compensation model and the effectiveness and robustness of the PSMC are verified by experiment results.

In recent years, the blackout accident shows that the cause of power failure is not only in the power network, but also in the cyber network. Aiming at the problem of cyber network fault Cyber-physical power systems, combined with the structure and functional attributes of cyber network, the comprehensive criticality of information node is defined. By evaluating the vulnerability of ieee39 node system, it is found that the fault of high comprehensive criticality information node will cause greater load loss to the system. The simulation results show that the comprehensive criticality index can effectively identify the key nodes of the cyber network.

In this article, a decentralized adaptive finite-time tracking control scheme is proposed for a class of nonstrict feedback large-scale nonlinear interconnected systems with disturbances. First, a practical almost fast finite-time stability framework is established for a general nonlinear system, which is then applied to the design of the large-scale system under consideration. By fusing command filter technique and adaptive neural control and introducing two smooth functions, the ``singular'' and ``explosion of complex'' problems in the backstepping procedure are circumvented, while the obstacles caused by unknown interconnections are overcome. Moreover, according to the framework of practical almost fast finite-time stability, it is shown that all the closed-loop signals of the large-scale system are almost fast finite-time bounded, and the tracking errors can converge to arbitrarily small residual sets predefined in an almost fast finite time. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed finite-time decentralized control scheme.

This paper concentrates on the design of intelligent adaptive tracking controller for stochastic switched nonlinear pure-feedback systems with input saturation and non-lower triangular structure. It needs to be emphasized that both the issues of pure-feedback structure and non-differential saturation nonlinearity are involved in the studied system. With the help of the mean-value theorem, a novel intelligent adaptive tracking controller is developed in this work to overcome the difficulty resulted from pure-feedback structure, and the inherent property of Gaussian functions is utilized to handle functions that are unknown and include all state variables. Moreover, through the universal intelligent approximation technology, a novel control strategy is constructed under the framework of backstepping, which guarantees that the tracking error can converge to a small neighborhood near the origin in the sense of mean quartic value and all signals of the nonlinear closed-loop system can be bounded in probability. Eventually, the effectiveness of the presented scheme is further illustrated by the simulation of two practical examples.

An exact grasp of the electricity consumers' demand response characteristics (DRCs) plays an important role in designing and achieving the potential benefit of demand response (DR) programs. In order to analyze the DRCs of industrial electricity consumers in the time of use (TOU) pricing program, a DR model based on the Support Vector Machine (SVM) regression algorism is proposed in the paper. With the model, electricity consumers' daily load arrangement under different price rate (PR) of TOU pricing program could be simulated. Using the simulating data, the electricity consumers' DRCs could be calculated to make further analysis. The work provides reasonably theoretical reference for designing, evaluating and amending current TOU pricing programs and gives an access to quantitively evaluate electricity consumers' DR potential. Besides, the model can also be adapted to study other areas of DR.