Explore the vast range of titles available.

This paper investigates the application of Neural Network Backstepping Control (NN-BSC) for enhancing the rotational speed control of Oscillating Water Column (OWC) wave energy systems. Traditional control methods face limitations when dealing with nonlinearities, irregular wave conditions, and actuator disturbances. To address these challenges, this research paper introduces a Chebyshev NN within the BSC framework, leveraging its high approximation accuracy and computational efficiency. The design of the NN-BSC involves estimating the disturbance term using the Chebyshev NN and validating the stability OWC control system through Lyapunov analysis. The proposed NN-BSC law effectively handles nonlinearities and improves system robustness under dynamic conditions. Numerical simulations have been conducted using MATLAB/SIMULINK to compare the performance of the uncontrolled OWC system, conventional PI and BSC, and NN-BSC, under scenarios with and without actuator disturbances. The parameters for PI, BSC, and NN-BSC are optimized using a Particle Swarm Optimization (PSO) algorithm, which minimizes a fitness function defined by the Integral Squared Error (ISE). Results indicate that NN-BSC achieves smoother rotor speed tracking, particularly under actuator disturbances, where the conventional PI and BSC exhibits significant performance degradation in terms of ISE. Under actuator disturbance scenarios: (1) NN-BSC achieved the lowest ISE value of 22.5433, outperforming PI (40.6381) and BSC (37.1192), and (2) NN-BSC demonstrated the lowest maximum peak overshoot (0.9651 rad/s ) and fastest settling time (0.0561 s ).

An intelligent servo drive system for a permanent magnet-assisted synchronous reluctance motor (PMASynRM) that can adapt to the control requirements considering the motor’s nonlinear and time-varying natures is developed in this study. A recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed to achieve this. In this study, first, a maximum torque per ampere (MTPA) controlled PMASynRM servo drive is introduced. A lookup table (LUT) is created, which is based on finite element analysis (FEA) results by using ANSYS Maxwell-2D dynamic model to determine the current angle command of the MTPA. Next, a backstepping control (BSC) system is created to accurately follow the desired position in the PMASynRM servo drive system while maintaining robust control characteristics. However, designing an efficient BSC for practical applications becomes challenging due to the lack of prior uncertainty information. To overcome this challenge, this study introduces an RWFNN as an approximation for the BSC, aiming to alleviate the limitations of the traditional BSC approach. An enhanced adaptive compensator is also incorporated into the RWFNN to handle potential approximation errors effectively. In addition, to ensure the stability of the RWFNN, the Lyapunov stability method is employed to develop online learning algorithms for the RWFNN and to guarantee its asymptotic stability. The proposed intelligent backstepping control with recurrent wavelet fuzzy neural network (IBSCRWFNN) demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive, as evidenced by the experimental results.

This paper investigates the performance of two different adaptive control schemes for controlling the angular position of an electronic throttle (ET) plate. The adaptive backstepping controller and adaptive sliding mode backstepping controller are the controllers under consideration. The control design based on these adaptive controllers is firstly addressed and the stability analysis of each controller has been presented and the convergence of both position and estimation errors for both controllers have been proved. A comparison study of the performance of both controllers has been conducted in terms of system transient characteristics and the behavior of their associated adaptive gain. The simulation has been implemented within the environment of the MATLAB package.

In this work, we present a new four-dimensional chaotic hyperjerk system with a half-line of equilibrium points. In the chaos literature, it is well-known that chaotic systems with an infinite number of equilibrium points exhibit hidden attractors. Thus, we deduce in this research work that the new chaotic hyperjerk system has hidden attractors. We next study the new chaotic hyperjerk system for a dynamic analysis using bifurcation plots and Lyapunov Exponents (LE) diagrams.We exhibit that the new hyperjerk system has a special property of multistability with coexisting attractors. Using Multisim version 14.2, we carry out an electronic circuit simulation for the proposed 4-D chaotic hyperjerk system with a half-line of equilibrium points. Finally, as an application in control engineering, we apply backstepping control for achieving antisynchronization of a pair of new chaotic hyperjerk systems taken as master-slave systems, which has important applications in communication systems.

Coupling characteristics of integrated mechanical-hydraulic-control systems for active hydro-suspension with uncertain and time-varying parameters make it difficult to achieve system-level optimal performances if only through physical or control system design. A novel collaborative design framework is proposed to optimize selected variables with objectives of structural lightweight, controllable suspension performances, and energy consumption. To improve ride/handling performances of active hydro-suspension under limited chatter space and allowable tire dynamic load, nonlinear filter barrier-Lyapunov-function-based backstepping upper controller is designed to generate target force under uncertain body weight, and projection-based adaptive backstepping sliding mode bottom controller is presented for valve current adjustment to drive asymmetric actuator precisely track required target force under time-varying fluid parameters. Based on designed hierarchical controller, physical/control collaborative design problem for system-level optimization is formulated by tailored optimal objective functions/constraints, independent and coupling design variables. The solution efficiency is improved through reduced calls of physical/control systems using response extreme difference sensitivity analysis, updated initial sets, and dynamic search interval for subsequent optimization. Finally, numerical simulation is presented to verify the effectiveness and benefits of the proposed collaborative optimization hierarchical control design method with eliminated conflicts between ride comfort and suspension deformation, improved control performances, better robustness, and lighter structure.

This paper is aimed at addressing the design of an effective adaptive nonlinear control of a photovoltaic (PV) water pumping system powering a submersible induction motor and a centrifugal water pump. Four objectives are achieved using an adaptive Backstepping controller. First, it is applied to ensure maximum power point tracking, and uses the latter as a reference in regulation of the rotor speed to convert the maximum electrical power into maximum mechanical power. Second, the adaptive controller is synthesized to control motor rotor flux and restrict the magnetic circuit to its linear interval. Third, it is used to online estimate the rotor time-constant and the load torque disturbance estimation. Finally, this controller is employed to limit the stator currents to protect induction motor windings. Mathematical modelling of the main elements of the system is presented. A sliding mode rotor flux estimator is employed in the output feedback control of the whole system. DC-AC converter is controlled by pulse width modulation. The feasibility, the robustness and the effectiveness of the proposed adaptive nonlinear controller are evaluated through simulations in MATLAB/Simulink environment.

In the process of extension sleeve press-fitting of the bearing pressing machine, electro-hydraulic servo system of valve-controlled symmetrical cylinder (ESSVSC) is an important and critical control module, and its performance has a significant impact on the working accuracy of such equipment. People have proposed various related control algorithms. However, external disturbances and unmodeled dynamic factors are often inevitable in practical work and may produce significant influences on the performance of the control system. Existing researches on this issue remains to be enriched and the performance of related algorithms still needs further improvement. An adaptive backstepping control algorithm (ABCA) is proposed in this paper. Firstly, a mathematical model of the ESSVSC is established which takes into account external disturbances and unmodeled dynamic factors. Secondly, the adaptive backstepping controller is designed by using the backstepping algorithm, and the control law and adaptive parameter estimation law are given. The stability of the control system is also proved. The analysis results of the numerical examples show that the proposed algorithm can effectively suppress the adverse effects of typical external disturbances and unmodeled dynamic factors and maintain good control accuracy. The output displacement error of the proposed algorithm is smaller and the tracking performance is better. The control accuracy of our algorithm is improved by 48.33% and 94.76% compared to BSMCA and PID, respectively, which illustrates the rationality of the established mathematical model and the effectiveness of the algorithm. This work is expected to provide useful reference for improving the control performance during the pressing process of the extension cylinder and the algorithm design of ESSVSC.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become widely used in many fields, ranging from agriculture to military operations, due to recent advances in technology and decreases in costs. Quadrotors are particularly important UAVs, but their complex, coupled dynamics and sensitivity to outside disturbances make them challenging to control. This paper introduces a new control method for quadrotors called Backstepping Sliding Mode Control (BSMC), which combines the strengths of two established techniques: Backstepping Control (BC) and Sliding Mode Control (SMC). Its primary goal is to improve trajectory tracking while also reducing chattering, a common problem with SMC that causes rapid, high-frequency oscillations. The BSMC method achieves this by integrating the SMC switching gain directly into the BC through a process of differential iteration. Herein, a Lyapunov stability analysis confirms the system’s asymptotic stability; a genetic algorithm is used to optimize controller parameters; and the proposed control strategy is evaluated under diverse payload conditions and dynamic wind disturbances. The simulation results demonstrated its capability to handle payload variations ranging from 0.5 kg to 18 kg in normal environments, and up to 12 kg during gusty wind scenarios. Furthermore, the BSMC effectively minimized chattering and achieved a superior performance in tracking accuracy and robustness compared to the traditional SMC and BC.

The Suspensions are among the important components of vehicles, providing comfort for passengers and protecting the chassis and freight. They are normally provided with dampers that mitigate these harmful and uncomfortable vibrations. This work aims at investigating the potential of the use of the Magnetorheological Damper as a semi-active suspension for passenger vehicles to improve their ride dynamics and to, ensure their manipulability, and reduce unwanted vibrations levels. To achieve desired performances, a hybrid controller based on a backstepping-sliding mode control strategy is derived to increase the dynamic performance of the automotive suspension component and providing comfortable passenger traveling. Numerical simulation is used to test the efficiency of the proposed controller. The obtained results show that the proposed backstepping sliding mode controller is more efficient and robust against road profile excitation and external disturbances in comparison to a classical controller based on backstepping and sliding mode control.

In this paper, the bipartite consensus tracking control problem is investigated for a class of nonlinear fractional-order multi-agent systems (FOMASs) with unknown dynamics, actuator faults, and input nonlinearities. Based on the adaptive backstepping technique, an adaptive bipartite consensus tracking control framework is constructed for FOMASs, where both cooperative and competitive relationships among agents are implemented. Furthermore, a fault compensation mechanism is proposed to relax the restriction on the number of actuators that can fail, and allow the existence of different types of input nonlinearities for each actuator. In addition, an improved adaptive self-triggered control mechanism that can be dynamically adjusted depending on the bipartite consensus error is extended to FOMASs to save network resources. Then, by means of the fractional-order Lyapunov stability criterion, it is theoretically proved that the proposed control scheme ensures that all signals of the closed-loop systems are bounded and drives the bipartite consensus error into a desired neighborhood of the origin. Finally, simulation results are provided to confirm the effectiveness of the proposed control scheme.