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In critical manoeuvres where the maximum tire-road friction capacity is used, the vehicle's dynamic behaviour is highly nonlinear, and there are strong couplings between longitudinal and lateral dynamics. If the tire-road friction conditions change suddenly during these manoeuvres, the vehicle control will be very complicated. The innovation of this research is a control algorithm to manage vehicles on a curved path with sudden tire-road friction change. The main advantage of the proposed controller is that it is robust to the change of the friction coefficient and other unmodeled uncertainties and ensures vehicle stability with low computational volume. The evaluation of the proposed adaptive controller has been done using the full vehicle model in CarSim software and by defining three different manoeuvres, moving at a constant speed on a curved road, lane-change, and lane-change with braking. Also, in the obtained results, the noise of the yaw speed signals and longitudinal and lateral accelerations are considered. The estimation of the longitudinal and lateral velocities is also done using these data. The obtained results showed that the proposed integrated control can manage the highly nonlinear dynamics of the vehicle in the existence of a sudden and significant change in the friction coefficient. [ABSTRACT FROM AUTHOR]

This paper discusses the modeling and trajectory tracking control of a Bi-rotor. A novel class of vertical flight robots that can perform vertical takeoff, landing, and passenger transportation. Bi-rotor aircraft utilize a simplified mechanism compared to helicopters while maintaining the ability to perform complex maneuvers. With six degrees of freedom and four actuators, including two tilt actuators for steering and two propellers for thrust generation, they are classified as underactuated systems. The trajectory tracking controller employs a combination of feedback linearization and backstepping control methods, with an inner loop controlling the Euler angles and an outer loop regulating the Bi-rotor position and calculating desired angles for trajectory tracking. Control algorithms in the limited existing literature often rely on simplified mathematical models, which tend to overlook crucial nonlinear coupling terms. However, neglecting these terms can have significant implications for the dynamic behavior of the system. The dynamic modeling of the Bi-rotor aircraft was validated using the ADAMS software and integrated with the Simulink environment in MATLAB software. The obtained results represent the effectiveness of the proposed algorithm for the control of the Bi-rotor. [ABSTRACT FROM AUTHOR]

In this article, a novel control algorithm called the combined backstepping and feedback linearisation method, along with an uncertainty estimator, is presented for quadrotors. The objective is to develop a robust control algorithm capable of handling various flight conditions, compensating for uncertainties and disturbances, and effectively controlling high-speed manoeuvres. To accomplish this, the quadrotor dynamics model is first derived using the Newton–Euler method. Subsequently, a backstepping control algorithm is designed for the quadrotor's internal control layer, followed by the application of the feedback linearisation method to the external control layer. An estimator is also designed to mitigate the effects of disturbances and uncertainties. A comparison is made between the proposed combined backstepping and feedback linearisation algorithm and the backstepping method, revealing that the combined backstepping and feedback linearisation algorithm outperforms the backstepping method across different aspects. Notably, the combined backstepping and feedback linearisation algorithm achieves faster trajectory tracking and demonstrates fewer steady-state errors. Additionally, the integration of the uncertainty estimator into the combined backstepping and feedback linearisation algorithm effectively mitigates the detrimental effects of disturbances and uncertainties. Comparative results for tracking control are presented to evaluate the performance of the proposed algorithm across various scenarios and case studies. [ABSTRACT FROM AUTHOR]

Electrostatic micro electro mechanical system (MEMS) as an actuator is one of the most widely used actuators in micro dimension systems. Due to the high nonlinear characteristics in the micro actuator's model, nanopositioning has been a challenging problem for researchers. In addition, the system is subject to uncertainties associated with the system's unknown parameters and un‐modelled dynamics. Thus, to overcome these problems and achieve precise positioning a robust non‐linear control method is required. In this research, an optimal voltage control algorithm is initially designed utilizing the nonlinear predictive control approach. The Taylor expansion sequence is used to estimate the final position of the parallel plate actuator. To approximate unknown disturbances, a radial basis function neural network (RBFNN) has been designed. The estimations made were used in updating the control input in the previous step, and the obtained results showed an increase in the robustness of the designed controller. The stability of the closed‐loop system is also analyzed. Finally, a comparison is accomplished between the potential of the proposed controller and that of the feedback linearization and sliding mode controllers. The obtained results indicate better performance and robustness of the suggested controller. [ABSTRACT FROM AUTHOR]

SUMMARY: The purpose of this paper is to design a stabilizing controller for a car with n connected trailers. The proposed control algorithm is constructed on the Lyapunov theory. In this paper, the purpose of navigating the system toward the desired point considering the slip phenomenon as a main source of uncertainty is analyzed. First mathematical models are presented. Then, a stabilizing control approach based on the Lyapunov theory is presented. Subsequently, an uncertainty estimator is taken into account to overcome the wheel slip effects. Obtained results show the convergence properties of the proposed control algorithm against the slip phenomenon. [ABSTRACT FROM AUTHOR]

SUMMARY: Today, automatic diving robots are used for research, inspection, and maintenance, extensively. Control of autonomous underwater robots (AUVs) is challenging due to their nonlinear dynamics, uncertain models, and the system underactuation. Data collection using underwater robots is increasing within the oceanographic research community. Also, the ability to navigate and cooperate in a group of robots has many advantages compared with individual navigations. Among them, the effectiveness of using resources, the possibility of robots' collaboration, increasing reliability, and robustness to defects can be pointed out. In this paper, the formation control of underwater robots has been studied. First, the kinematic model of the AUV is presented. Next, a novel Lyapunov-based tracking control algorithm is investigated for the leader robot. Subsequently, a control law is designed using Lyapunov theory and feedback linearization techniques to navigate a group of follower robots in a desired formation associated with the leader and follow it simultaneously. In the obtained results for different reference paths and various formations, the effectiveness of the proposed algorithm is represented. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) are highly nonlinear underactuated systems with uncertain dynamics and a challenging control problem. The main focus of this paper is to present a control law that shows desirable performance in the presence of modeling uncertainties. In this study, uncertainties are considered to be bounded and the AUV mathematical model is obtained in the presence of such uncertainties. Forces and torques applied to the AUV are also designed using a nonlinear dynamic controller. Appropriate adaptive rules are also presented to overcome system uncertainties and external disturbances. The adaptive nonlinear dynamic controller is designed based on upper bounds of system uncertainties and its stability is proven using the Lyapunov theory. In this article, the performance of the proposed control algorithm for tracking reference trajectories in an obstacle-rich environment is investigated. Therefore, the control algorithm is combined with potential fields for obstacle avoidance. Obtained results show the efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Tractor-trailer systems as multibody modular robotic systems have been widely used to increase load transportation capacity. Control of these systems started from motion aid facilities in human-driven vehicles to fully autonomous mobile robots in recent years. The mobility of these systems is restricted due to the presence of nonholonomic constraints of wheels and also to the system severe underactuated nature. Several control problems are under research for autonomous navigation of such systems. Trajectory tracking is one of the main problems in the context of autonomous nonholonomic systems. In this paper, dynamic modeling and control of a car with $n$ trailers have been developed. First, a dynamic model of the system is obtained. Next, an output feedback kinematic controller and a feedback linearization kinetic controller have been used for tracking control of the system. Finally, experimental results are presented to show the merits of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, trajectory tracking control of a wheeled mobile robot (tractor) with two trailers including three nonholonomic constraints is analyzed. This system is a nonlinear, under actuated mechanical system subjected to nonholonomic kinematic constraints. First, system dynamic equations are obtained. A non-model-based control algorithm using PD-action filtered errors has been used in order to control the wheeled robot. Non-model-based controllers are always more appropriate than model-based algorithms due to lower dependency on dynamic models, lower computational costs, and also robustness to uncertainties. Asymptotic stability of the closed-loop system has been investigated using Lyapunov method and Barbalat’s lemma. Finally, in order to show the effectiveness of the proposed approach simulation and experimental results have been presented. Obtained results show that without requiring a priori knowledge of plant dynamics, and with reduced computational burden, the tracking performance of the presented algorithm is quite satisfactory. [ABSTRACT FROM PUBLISHER]

Tractor–trailer systems have been widely used to increase load transportation capacity. Control of these systems started from motion aid facilities in human-driven vehicles to fully autonomous mobile robots in recent years. In fact, tractor–trailer wheeled robot has been proposed as a modular robotic system in which an actuated mobile robot tows a passive trailer. This is a highly nonlinear underactuated system subjected to nonholonomic kinematic constraints, stable tracking control of which is investigated in this article. To this end, first dynamic model and nonholonomic constraints of the system will be developed in the presence of uncertainties and external disturbances. Next, feasible reference trajectories for the system are generated considering the system constraints, and a Lyapunov kinematic control law is used to stabilize tracking errors. Then, an adaptive dynamic sliding mode controller is presented to control the wheeled mobile robot in the presence of external disturbances and uncertainties. Appropriate adaptive rules based on vigorous Lyapunov stability theorems are designed to compensate the wheeled mobile robot upper-bounded lumped uncertainties and automatically update controller gains. Next, the experimental setup is described, and the obtained results are presented and discussed. Experimental and comparative results reveal merits of the proposed approach in successful control of the overall system, that is, a wheeled mobile robot towing a passive trailer, leading to asymptotic tracking of reference trajectories. [ABSTRACT FROM PUBLISHER]