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This paper proposes a nonlinear intelligent control of a two link robot arm by considering human voluntary components. In general, human arm viscoelastic properties are regulated in different manners according to various task requirements. The viscoelasticity consists of joint stiffness and viscosity. The research of the viscoelasticity can improve the development of industrial robots, rehabilitation and sports etc. So far, some results have been shown using filtered human arm viscoelasticity measurements. That is, human motor command is removed. As a result, the dynamics of human voluntary component during movements is omitted. In this paper, based on the feedforward characteristics of human multi joint arm, a model is obtained by considering human voluntary components using a support vector regression technique. By employing the learned model, a nonlinear intelligent control of two link robot arm is proposed. Experimental results confirm the effectiveness of this proposal.

This paper investigates the problem of trajectory tracking control in the presence of bounded model uncertainty and external disturbance. To cope with this problem, we propose a novel intelligent operator-based sliding mode control scheme for stability guarantee and control performance improvement in the closed-loop system. Firstly, robust stability is guaranteed by using the operator-based robust right coprime factorization method. Secondly, in order to further achieve the asymptotic tracking and enhance the responsiveness to disturbance, a finite-time integral sliding mode control law is designed for fast convergence and non-zero steady-state error in accordance with Lyapunov stability analysis. Lastly, the controller’s parameters are automatically adjusted by the proved stabilizing particle swarm optimization with the linear time-varying inertia weight, which significantly saves tuning time with a remarkable performance guarantee. The effectiveness and efficiency of the proposed method are verified on a highly nonlinear ionic polymer metal composite application. The extensive numerical simulations are conducted and the results show that the proposed method is superior to the state-of-the-art methods in terms of tracking accuracy and high robustness against disturbances.

The experimental assessment of power quality for a standalone inverter connected to AC load in a microgrid is investigated in this paper. The microgrid used in this work includes photovoltaic solar and storage systems. The proposed control technique combined an intelligent method and sliding mode control (SMC) to make its structural more flexible. The power quality for AC load supply is enhanced, and the power loss, due to the harmonics of chattering effect and converter switching, is reduced. Moreover, the LCL filter is adopted for this microgrid to improve the power quality. The system stability is proved using Lyapunov theory and analysis. To validate the proposed design methodology and digital controllers and to show its superiority compared to other control techniques, experimental results are performed using a designed laboratory test bench for a standalone inverter system application and DSPace DS1104 board. In order to check advantages of the fuzzy SMC, the experimental results performed using the proposed LCL filter are compared to the conventional one to demonstrate its effectiveness in terms of harmonic attenuation and minimal power distortion. The experimental results show that the proposed technique provides the better operation with minimal distortion at the inverter output voltage.

In order to meet the requirements of servo systems, including sensitive and rapid adjustment, high control and motion accuracy, and strong working adaptability, in special application fields, such as high thrust and long travel, an adaptive inversion control method is proposed for the lateral force and other nonlinear factors of multistage electromechanical servo system (MEMSS). The position tracking controller of permanent magnet synchronous motor (PMSM), based on an improved adaptive inversion method, was designed and its stability was analyzed, and the Luenberger load torque observer model of PMSM was established. The EMESS simulation model of an adaptive inversion controller was built using the Simulink platform, and the motor multibody dynamics model was established in ADAMS software. Through the joint simulation of Simulink and ADAMS software, the results of EMESS under adaptive inversion controller and traditional PID controller were compared, and the feasibility and reliability of the designed adaptive inversion controller were verified. Finally, the designed controller was tested based on the experimental platform. The experimental results show that the adaptive inversion controller designed in this paper has better robustness and stability than the traditional PID controller.

The idea of optimization can be regarded as an important basis of many disciplines and hence is extremely useful for a large number of research fields, particularly for artificial-intelligence-based advanced control design. Due to the difficulty of solving optimal control problems for general nonlinear systems, it is necessary to establish a kind of novel learning strategies with intelligent components. Besides, the rapid development of computer and networked techniques promotes the research on optimal control within discrete-time domain. In this paper, the bases, the derivation, and recent progresses of critic intelligence for discrete-time advanced optimal control design are presented with an emphasis on the iterative framework. Among them, the so-called critic intelligence methodology is highlighted, which integrates learning approximators and the reinforcement formulation.

Lane-keeping is a basic function of an intelligent vehicle. But the existing lane-keeping methods may not provide the expected effect. A vehicle often deviates from the desired lane despite the working lane-keeping controller in practice. For addressing this issue, we propose a novel lane-keeping control method based on the homogeneous domination control theory to improve the lane-keeping system performance in this paper. Firstly, a two-degree-of-freedom lane-keeping dynamic model is built. Then, the state equations of the lane-keeping control system are obtained based on the dynamic model. A lane-keeping state feedback controller is designed via the homogeneous domination method. We prove that the designed controller can globally asymptotically stabilize the system via the Lyapunov method. The proposed homogeneous domination method does not require the nonlinear terms of the nonlinear system to meet the strict linear growth condition. Numerical simulation and hardware-in-the-loop test results show that the proposed homogeneous controller has strong robustness, fast response, and low energy output which are more suitable for the lane-keeping system and improves the lane-keeping system performance.

A hybrid model predictive control (HMPC) strategy is proposed in this paper to autonomously regulate intelligent vehicle longitudinal velocity considering nonlinear tire dynamics. Since the tire longitudinal dynamics, which has significant influence on vehicle longitudinal velocity control performance, exhibits highly nonlinear dynamical behaviors, the piecewise affine (PWA) identification is conducted firstly based on experimental data to accurately model the tire longitudinal dynamics. On this basis, due to that the intelligent vehicle needs to be operated in two distinct modes (drive and brake) for autonomous velocity regulation and because of the affine submodel switching behaviors of the PWA-identified tire model, the intelligent vehicle longitudinal dynamics control process considered in this work can be regarded as a hybrid system with both continuous variables and discrete operating modes. Thus, the mixed logical dynamical framework is further used to model the intelligent vehicle longitudinal dynamics, and a HMPC controller, which allows us to optimize the switching sequences of the operation modes (binary control inputs) and the torques acted on the wheels (continuous control inputs), is tuned based on online mixed-integer quadratic programming. Simulation results finally demonstrate the effectiveness of the proposed HMPC controller for intelligent vehicle longitudinal velocity regulation under typical driving conditions.

Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics systems because of its robustness against parametric uncertainties and matched disturbances. Although SMC design has traditionally addressed process model-based approaches, the rapid advancements in instrumentation and control systems driven by Industry 4.0, coupled with the increased complexity of the controlled processes, have led to the growing acceptance of controllers based on data-driven techniques. This review article aims to explore the landscape of SMC, focusing specifically on data-driven techniques through a comprehensive systematic literature review that includes a bibliometric analysis of relevant documents and a cumulative production model to estimate the deceleration point of the scientific production of this topic. The most used SMC schemes and their integration with data-driven techniques and intelligent algorithms, including identifying the leading applications, are presented.

This study investigates the self-healing control problem for systems with unknown dynamics. An intelligent self-healing control method based on inverse reinforcement learning (IRL) is proposed, which distinguishes itself by establishing a unified methodology for automatically detecting and accommodating the nonlinear actuator faults in systems. First, a modified IRL algorithm is developed to identify the unknown performance index from offline fault-free data. Then, an online fault detection module is introduced to detect the actuator fault by monitoring the control performance optimality. In addition, a neural network-based fault compensation strategy is designed to eliminate the influence of the fault in real time. Unlike existing methods that rely on the system dynamic model or the performance index, the proposed one can automatically discover and preserve the underlying control performance optimality for systems under general nonlinear actuator faults (including bias and gain faults). Furthermore, the detectability and stability of the proposed self-healing control are rigorously investigated via Lyapunov theory. Two theorems prove that the faults can be detected and compensated by our method in finite time when they degrade the optimality of the control performance. At last, illustrative examples are presented to support the theoretical results and demonstrate the superiority of the proposed method.

As new advanced vehicles, electromagnetic suspension (EMS)-type maglev trains have received wide attention because of their advantages such as high speed, no mechanical friction, low noise, low cost and energy consumption, strong climbing ability, and green environmental protection. The open-loop instability is one of the key points and difficulties for the levitation control systems of maglev trains. The closed-loop feedback control method must be applied to realize stable levitation. However, there are currently many levitation control methods just in theory. Considering their advantages and disadvantages, it is a major demand for maglev trains to select efficient, stable, applicable, and cost-saving methods to improve their dynamic performance and safety, which motivated this review. First, the current status of research on maglev trains is introduced in this paper, including types, system components, and research modes in various countries, followed by an analysis of the levitation control methods for EMS-type maglev trains. Then, the technical characteristics of the levitation control systems are described according to the basic principles of levitation systems, model building, mathematical derivation, and control objectives. Next, three kinds of typical levitation control methods are reviewed, namely, linear state feedback methods, nonlinear control methods, and intelligent control methods, according to their improvements and applications. Lastly, we summarize and evaluate the advantages and disadvantages of the three methods, and future developments of levitation control are suggested.