This paper addresses the low-complexity prescribed performance control problem for steer-by-wire (SbW) systems with input nonlinearity, model uncertainty, full-state constraints, and dual-channel (i.e., the controller-to-actuator and sensor-to-controller channels) event-triggering communication. Firstly, considering the coupling of longitudinal and lateral dynamics, the nonlinearity of tire force, the time-varying characteristics of longitudinal velocity and road adhesion coefficient, the time-varying nonlinear models of the 8-DOF vehicle dynamics system and SbW system are established. Secondly, a dual-channel event-triggering mechanism and low-complexity prescribed performance control method is proposed, in which the communication resources of sensor-to-controller and controller-to-actuator channels and the computing resources of the controller can be saved, also, the model uncertainty and measurement error can be against by the inherent robustness of the proposed methods. Thirdly, theoretical analysis is presented to show that the full-state and tracking errors of the SbW systems can be constrained to the prescribed ranges, and all signals of the closed-loop system are globally uniformly bounded. Finally, simulations and experiments are given to verify the validity of developed methods.

The net power of the automobile proton exchange membrane fuel cell (PEMFC) system evidently varies with air mass flow and pressure. To enhance net power across various operating conditions, this study introduces an adaptive control strategy integrating operation parameter optimization. Initially, the fuzzy Q-learning method is proposed to select suitable reference signals for oxygen excess ratio (OER) and pressure to maximize net power. Additionally, to coordinate the air compressor and flow valve, an adaptive neural network control scheme is suggested for the multiple-input-multiple-output (MIMO) air management system. Furthermore, a prescribed performance function is devised to ensure minimal overshoot and rapid convergence for OER and pressure tracking. Through a sequence of simulations and hardware-in-loop (HIL) tests, the proposed control strategy is shown to effectively optimize net power under diverse operating conditions, exhibiting superior transient and steady-state performance compared to conventional methods. Quantitative comparisons further demonstrate the superiority of reinforcement learning (RL) algorithms and adaptive control techniques in enhancing both power and control system performance.

This paper investigates the problem of accurate voltage tracking control for direct current-direct current (DC-DC) boost converter under unknown system parameters and load. Firstly, uncertainties caused by the perturbation of the inductor, capacitor, input voltage and load are approximated by neural networks. Meanwhile, a finite-time observer is proposed to obtain the estimates of lumped uncertainty without any true parameters of the system. Finally, to improve the convergence of output voltage, a finite-time control scheme is proposed for the DC-DC boost converter. It is proven that all signals of the closed-loop system are bounded and both the estimate errors and tracking errors can converge to a small neighborhood of the origin in finite time. Numerical simulations and real-time experiments are presented to demonstrate the effectiveness and superiority of the proposed controller.

With consideration of tire force saturation in vehicle motions, a novel path-following controller is developed for autonomous vehicles with unknown-bound disturbances and unknown actuator parameters. An adaptive sliding-mode fault-tolerant control (ASM-FTC) strategy is designed to stabilize the path-following errors without any information of disturbance boundaries, actuator fault boundaries and steering ratio from the steering wheel to the front wheels. By selecting the distance from the center of gravity to the center of percussion as the preview length, the effects of the lateral rear-tire force are decoupled and cancelled out, and then the preview error, which represents the path-following performance, can be only commanded by the front-tire force. To further address the issue of unknown tire-road friction limits, a modified ASM-FTC strategy is presented to improve the path-following performance as the lateral tire force is saturated. Simulation results show that the modified ASM-FTC controller demonstrates superior tracking performance over the normal ASM-FTC while the autonomous vehicle follows desired paths.

This paper addresses the prescribed tracking performance control problem for uncertain steer-by-wire (SbW) systems with input nonlinearity (including dead-zone and actuator fault) and the limitation of controller-area-network (CAN) bandwidth. An adaptive interval type-2 fuzzy logic system (IT2 FLS) is introduced to approximate the lumped model uncertainty, and a switching event-triggering mechanism (ETM) is applied to save the communication resources. Combining the backstepping approach and barrier Lyapunov function techniques, a prescribed tracking performance control method is proposed for SbW systems, where the initial values of state errors are no longer required in the controller design. Theoretical analysis shows that the tracking error can converge to the predefined residual set within preset time instead of the time tending infinite, while the closed-loop system is semi-globally stable. Simulations and vehicle experiments are presented to verify the effectiveness of the proposed control method.

This article addresses the event-triggered output feedback control problem of steer-by-wire (SbW) systems subject to uncertain nonlinearity and time-varying disturbance. First, to solve the uncertainty and remove unnecessary sensors, an interval 2 fuzzy logic system and an adaptive state observer are proposed to estimate the uncertain nonlinearity and unavailable states of SbW systems. Then, an event-triggered output feedback control method is constructed for SbW systems to achieve the prespecified tracking performance. Much significantly, the jumping phenomenon of the control input caused by event-triggering communication can be eliminated by the proposed event-triggered control systems. Furthermore, theoretical analysis shows that the tracking error can converge to the preset neighborhood of origin within finite time, while the Zeno behavior can be avoided. Finally, simulations and vehicle experiments are presented to verify the validity of the proposed methods.

Polymer electrolyte membrane fuel cell (PEMFC) air supply systems are usually affected negatively by model uncertainties, external disturbance, and unmeasured variables. In this article, we propose a composite adaptive type-2 fuzzy controller based on a high-gain observer and a disturbance observer for oxygen excess ratio (OER) of PEMFC air supply systems. First, the derivatives of system output, which are unavailable due to limited sensors, are estimated via the high-gain observer. Then, interval type-2 fuzzy logic systems (IT2 FLSs) are adopted to approximate the unknown system dynamics and the disturbance observer is designed to estimate compound disturbance including unknown external disturbance and fuzzy approximation error. Finally, in order to improve the tracking performance, two composite adaptive updating laws are constructed by utilizing the estimated tracking error and the modeling error. Theoretical analysis shows that the system tracking error is uniformly ultimately bounded by Lyapunov stability theory. Numerical simulations and hardware-in-loop experiments are presented to demonstrate the effectiveness and superiority of the proposed controller.

As tire forces approach road friction limits, vehicles may always exhibit performance degradation and even instability. The actual capacity of the available road friction imposes coupled limits on a vehicle’s longitudinal and lateral accelerations. In this paper, a varying speed method is proposed to design feasible speeds and accelerations, which ensure that the autonomous vehicle will not reach the tire-road friction limits during traversing a clothoid-based path. With the consideration of uncertain traction losses and vehicle parameters, a second-order super-twisting (ST) based speed control strategy is proposed to track above feasible speeds based on varying speed method, and to stabilize the sliding-mode variable of the speed tracking error with relative degree 1. Meanwhile, a second-order quasi-continuous (QC) based path-following control strategy is proposed to follow a desired transition path, and to stabilize the sliding-mode variable of the composite path-following errors with relative degree 2. On this basis, the proposed controllers have been verified to lead good robustness for tracking the ideal speeds and following the desired paths. As compared with the boundary of the autonomous vehicle running at a constant speed, the feasible speed boundary using varying speed method without exceeding the tire-road friction limits can be enlarged up to about 1.59 times, which is decided by the direction change between the entry and exit points of the desired path.

This paper investigates the design and implementation of the discrete-time adaptive neural network control with disturbance observer (DO) on a steer-by-wire (SbW) system, to simultaneously realize accurate tracking and anti-interference performance. Specifically, to approximate the lumped system uncertainty including the friction torque and self-aligning torque, the neural network is employed. To improve the steering tracking performance, the discrete-time identification model is proposed so that the tracking error and modeling error can be utilized to adjust the neural network updating law. Then, the unknown compound disturbances caused by external disturbance, Euler approximation errors and neural network approximation error are restrained by two DOs. Finally, the Lyapunov stability theory shows that the system tracking error is uniformly ultimately bounded. Both numerical simulations and experiments are implemented to show the superiority of the proposed controller.

Modeling of friction forces has been a challenging task in mechanical engineering. Parameterized approaches for modeling friction find it difficult to achieve satisfactory performance due to the presence of nonlinearity and uncertainties in dynamical systems. This paper aims to develop adaptive fuzzy friction models by the use of data-mining techniques and system theory. Our main technical contributions are twofold: extraction of fuzzy rules and formulation of a static fuzzy friction model and adaptation of the fuzzy friction model by the use of the Lyapunov stability theory, which is associated with a control compensation of a typical motion dynamics. The proposed framework in this paper shows a successful application of adaptive data-mining techniques in engineering. A single-degree-of-freedom mechanical system is employed as an experimental model in simulation studies. Results demonstrate that our proposed fuzzy friction model has promise in the design of uncertain mechanical control systems.

Since the contemporary integrated manufacturing system (CIMS) which is designed according to the conventional five layer Purdue model architecture separates control process and management process virtually, it leads to high production cost and low ore recovery ratio for the complicated process of ore dressing, this paper presents a systematic approach to develop the CIMS based on the three-layer enterprise resource planning (ERP)/manufacturing executive system (MES)/process control system (PCS) architecture for the ore dressing plant. The effective and efficient real time supervision techniques of production statistics & analysis, material current, production cost, equipments and quality are proposed to integrate control process and management process. The successful implementation of ore dressing plant CIMS of JiuQuan steel Company in China confirms the viability and effectiveness of the approach.

In this paper, an observer-based robust adaptive fuzzy tracking control for rigid robotic systems is presented with plant unknown. It is assumed that only the joint angular positions are measured, the joint angular velocities are estimated via a fuzzy observer. First, we design a nonlinear observer based on fuzzy basis functions (FBF) to estimate the joint angular velocities in which a fuzzy logic system is introduced to learn these unknown dynamics by an adaptive algorithm. Then, an indirect adaptive fuzzy controller based on observer is presented. The developed control scheme is simple and computationally efficient, since it does not require a knowledge of either the mathematical model or the parameterization of the robotic dynamics. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.

This paper presents an application of an adaptive fuzzy system for compensating the effects induced by the friction in mechanical system. An adaptive fuzzy system based on fuzzy basis functions is employed, and a bound on the tracking error is derived from the analysis of the tracking error dynamics. The hybrid-controller is a combination of a PD controller and an adaptive fuzzy controller which compensates for nonlinear friction. The proposed scheme is implemented and tested on a DC motor control system. The algorithm and simulations results are described. The results are relevant for many precision drives, such as those found in robot.