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IntroductionIsokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference.MethodsThis paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed.ResultsTo validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11.DiscussionThe research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

This article presents the complete design of a nonlinear control system for multiple stationary bikes connected to a mechanical energy conversion system equipped with squirrel cage induction generators (SIGs) linked to AC/DC converters. The primary objective of this study is to investigate the feasibility of powering multiple treadmills using the DC bus via DC/AC converters. The novelty of our approach lies in the integration of a new strategy of control system to achieve multiple control objectives within a gym microgrid environment, and an energy management algorithm to ensure the energy flow between intermittent generation and the considered, somewhat random, demand. The system is composed of several subsystems: i) stationary bikes connected to the DC bus via inverters acting as intermittent power sources; ii) a Li-ion battery-based energy storage system interfaced through a Buck-Boost converter; iii) electromechanical loads, including treadmills, powered by DC/AC converters, in addition to other DC loads. The main control objectives are as follows: a) each stationary bike extracts energy from the DC network by regulating the torque applied by the athlete, ensuring that the torque follows the reference torque; b) the treadmill’s speed must follow the reference generated based on the athlete’s condition; c) ensuring the protection of the energy storage system by monitoring its current and voltage; d) all mentioned objectives are achieved while maintaining the DC bus voltage at a reference value. To achieve these objectives, a nonlinear control approach based on Lyapunov theory is used to design the controllers. A formal analysis is conducted to assess the performance of the studied system. The system’s performance is demonstrated using the MATLAB/Simulink environment. Numerous simulations demonstrate that every control objective is achieved. Specifically, the system maintained the DC bus voltage at 500 V with a maximum deviation of 1 V, and the treadmill speed followed the reference within a margin of 0.1 m/s.

The DC motor is being rapidly replaced in the industry by the permanent magnet synchronous motor (PMSM), which has a number of benefits over it. Nonlinear equations are used to describe the dynamics of the PMSM. It is susceptible to unidentified external disturbances (load), and its properties change over time. These constraints make it more difficult to exercise control. To overcome the non-linearities and the aforementioned shortcomings, non-linear controls are necessary. This manuscript refers to the development of a sturdy high-caliber position tracking controller that incorporates integral action for PMSM. A predictive control law for the speed loop is established, combined with the backstepping control law for the inner loop. The overall strategy can be divided into two distinct elements. The initial stage involves the derivation of a reference electromagnetic torque computed through the generalized non-linear predictive control method. Subsequently, the controller law is formulated utilizing the robust backstepping control technique. One of the cardinal merits of this method lies in its exemption from the requirement of measuring and observing the external disturbances and parametric uncertainties. The efficacy of this cutting-edge control approach is rigorously evaluated in simulation with MATLAB/Simulink environment and experimentally using OPAL-RT, under diverse operating conditions. The findings demonstrate steadfast resilience amidst external disruptions and adjustments to parameters, while ensuring swift convergence, a testament to its robustness and reliability.

A 3-RPS parallel leveling mechanism based on the proportional integral (PI) robust sliding mode system is designed to solve the problem of data acquisition inaccuracy caused by vehicle tilting during the robot walking. Firstly, the configuration synthesis is carried out according to the spiral and inverse spiral theory. Then, the dynamic analysis and calculation of the 3-RPS leveling mechanism are carried out to construct its dynamic model. On this basis, the adaptive robust sliding mode algorithm is introduced to optimize and improve the PI controller to improve the stability and adaptive correction ability. The stability of the nonlinear control system is verified by Lyapunov function. Finally, the control error curve is obtained by Matlab/Simulink comparative simulation experiment. The simulation results show that the adaptive correction effect of the PI control system optimized by the robust sliding mode algorithm is remarkable, and the steady-state error of the control is greatly reduced. The research provides a basis for the development of physical prototype of 3-RPS parallel leveling mechanism.

Abstract Malicious attacks are often inevitable in cyber-physical systems (CPS). Accuracy in Cyber physical system for position tracking of servos is the major concern now a days. In high precision industrial automation, it is very hard to achieve accuracy in tracking especially under malicious cyber-attacks, control saturations, parametric perturbations and external disturbances. In this paper, we have designed a novel predefined time (PDT) convergence sliding mode adaptive controller (PTCSMAC) for such kind of cyber physical control system. Main key feature of our control is to cope these challenges that are posed by CPS systems such as parameter perturbation, control saturation, and cyber-attacks and the whole system then upgrade to a third-order system to facilitate adaptive control law. Then, we present an adaptive controller based on the novel PDT convergent sliding mode surface (SMS) combined with a modified weight updated Extreme Learning Machine (ELM) which is used to approximate the uncertain part of the system. Another significant advantage of our proposed control approach is that it does not require detailed model information, guaranteeing robust performance even when the system model is uncertain. Additionally, our proposed PTCSMAC controller is nonsingular regardless of initial conditions, and is capable of eradicating the possibility of singularity problems, which are frequently a concern in numerous CPS control systems. Finally, we have verified our designed PTCSMAC control law through rigorous simulations on CPS seeker servo positioning system and compared the robustness and performance of different existing techniques.

Planar underactuated mechanical systems have been a popular research issue in the area of mechanical systems and nonlinear control. This paper reviews the current research status of control methods for a class of planar underactuated manipulator (PUM) systems containing a single passive joint. Firstly, the general dynamics model and kinematics model of the PUM are given, and its control characteristics are introduced; secondly, according to the distribution position characteristics of the passive joints, the PUM is classified into the passive first joint system, the passive last joint system, and the passive intermediate joint system, and the analysis and discussion are carried out in respect to the existing intelligent control methods. Finally, in response to the above discussion, we provide a brief theoretical analysis and summarize the challenges faced by PUM, i.e., uncertainty and robustness of the system, unified control methods and research on underactuated systems with uncontrollable multi-passive joints; at the same time, the practical applications have certain limitations that need to be implemented subsequently, i.e., anti-jamming, multi-planar underactuated robotic arm co-control and spatial underactuated robotic arm system development. Aiming at the above challenges and problems in the control of PUM systems, we elaborate on them in points, and put forward the research directions and related ideas for future work, taking into account the contributions of the current work.

A three-dimensional active maglev (magnetic levitation) actuating system based on force imbalance is proposed. By combining the principle of force imbalance control with the control algorithm, the stable levitation and controllable levitating motion of the magnetic ball can be realized. The four electromagnetic actuating structures are used to stabilize the force of the controlled object, and the dual-hall sensor group and hardware differential method are used to improve control stability and accuracy. By combining the fine adjustment of the active maglev actuating system with the coarse adjustment of the mechanical arm, the three-dimensional levitation motion of the magnetic ball in curved pipes is realized. Experimental results show that the proposed control algorithm solves problems such as the increase of deviation between the controlled object and the steady-state operating point and the rapid deterioration of tracking performance in the model-based control method. In the vertical direction, the overshoot is within 0.418%, regardless of axis motion or non-axis motion. In the horizontal direction, the offset limits left and right of the axis are 4.590 mm and 3.536 mm, respectively. The fluctuation of vertical and horizontal motion is within the allowable range of ±0.2 mm. This can be applied to the non-destructive quality detection of the inner walls and the internal dredging of long and thin pipes in examinations and industrial fields.

Reinforcement learning is a compelling area of research within machine learning because it enables the improvement of control strategies for future manipulation of dynamic systems, leveraging previous data even without a precise model of the system. It usually makes complex, model-free predictions from data alone, which is actually consistent with the purpose of control in that they both aim to design systems using richly structured perceptions to execute planning and control strategies that adequately adapt to changing environments. The robust trajectory tracking control of intricate mechanical systems presents a challenging problem that necessitates effective control methods. In this paper, we propose a novel nonlinear control strategy based on deep reinforcement learning to solve the trajectory tracking problem of a 3-degree-of-freedom (3-DOF) control moment gyroscope (CMG). First, dynamic modeling of the 3-DOF CMG is used as a policy solver for the reinforcement learning training environment, and transfer learning is employed to bridge the reality gap. Then, the hyperparameters and reward functions of the neural network are optimized using the asynchronous successive halving algorithm. Ultimately, the twin delay depth determination policy gradient algorithm is trained in simulation to yield an agent capable of tracking user-defined trajectory routes as a nonlinear controller for the system. Both simulation and experimental results show that the proposed method works well for both high-frequency and low-frequency varying trajectory tracking control, and that the proposed method has better response speed and robustness than classic linear parameter-varying control methods and the state-of-the-art nonlinear parameter-varying method and the neural network-based feedback linearization adaptive control method. [ABSTRACT FROM AUTHOR]

This paper presents a joint synthesis algorithm of trajectory and controlled invariant funnel (CIF) for locally Lipschitz nonlinear systems subject to bounded disturbances. The CIF synthesis refers to a procedure of computing controlled invariance sets and corresponding feedback gains. In contrast to existing CIF synthesis methods that compute the CIF with a predefined nominal trajectory, our work aims to optimize the nominal trajectory and the CIF jointly to satisfy feasibility conditions without the relaxation of constraints and obtain a more cost‐optimal nominal trajectory. The proposed work has a recursive scheme that mainly optimize trajectory update and funnel update. The trajectory update step optimizes the nominal trajectory while ensuring the feasibility of the CIF. Then, the funnel update step computes the funnel around the nominal trajectory so that the CIF guarantees an invariance property. As a result, with the optimized trajectory and CIF, any resulting trajectory propagated from an initial set by the control law with the computed feedback gain remains within the feasible region around the nominal trajectory under the presence of bounded disturbances. We validate the proposed method via two applications from robotics. [ABSTRACT FROM AUTHOR]

In this paper, a previously proposed converter to be used for inductive wireless power transmission is modeled and a control strategy is proposed. The converter topology combines into a single stage, two buck converters and a half-bridge converter to feed a resonant stage. This simple and symmetrical topology is straightforward to design; only a buck converter and a parallel resonant tank must be specified. It would be desirable for the converter to feed a wide range of loads and be robust under input voltage variations. These objectives can not be attained with a linear model and control. For this reason, in this paper, a nonlinear converter model is derived step by step and controller strategy is developed without relying on system linearization. The proposed controller does not measure the output but only its peak value. This can be conducted because it takes advantage of the square current pulse fed into a resonant tank; it outputs an approximately sinusoidal signal. The control strategy is completed with a scheme to build the required pulse at the input of the resonant tank. The resulting nonlinear controller has a fast closed-loop performance; furthermore, it is robust under parameter uncertainty, and load and input voltage variations. Despite its features, the controller is fairly simple to implement. [ABSTRACT FROM AUTHOR]

Active tuned mass damper (ATMD) devices are recommended in various structures to reduce vibrations. The performance of the ATMD system is closely tied to the control design utilized, according to research in the literature. The designed control input will take into account as much system dynamics as possible, making it possible to use it as a generalized controller in all similar systems. Nonlinear behavior is the natural behavior of engineering structures, and in order to obtain a more realistic and high-performance ATMD system, it can be considered as an appropriate approach to design the controller by considering the structural nonlinearities. In addition, unexpected external influences and parameter uncertainties must be taken into account during control design to ensure that control systems perform successfully in similar systems in all conditions. Therefore, in this study, the nonlinear model of the multi-story building was reconstructed by adding nonlinear disturbance to represent unknown external effects. Band-limited white noise is used as a disturbance function and Gaussian white noise is added to measured states in this study. To obtain a robust controller, unknown structural parameters are compensated for by using adaptive compensation terms. With the controller design supported by the Lyapunov-based stability analysis, the stability of the vibrating structure featuring an ATMD is theoretically guaranteed while achieving the main control goal. Performance analyses of the designed controllers are carried out with simulation studies. The efficiency of the developed Lyapunov-based controller in dampening the unwanted vibrations that occurred on the building is seen in simulation results. [ABSTRACT FROM AUTHOR]

The paper presents a new internal model control (IMC) based control technique for lateral trajectory tracking of autonomous vehicles. The controller's proposed structure employs a robust, fault-tolerant nonlinear internal servo control with optimal reference generation concerning vehicle yaw stability and physical limitations. The presented inscription of the reference generation creates a convex optimization task that can be used in real-time applications. Improvements in yaw-rate stability of vehicle motion control are first shown through simulation results performed in a Simulink environment. The controller structure was also implemented in a real-time model and was examined in a Mercedes C-Class vehicle. In this article, the simulation results and the real-time measurements are presented. The results show that the proposed controller has high efficiency in disturbance rejection and lower sensitivity towards parameter changes compared to a model predictive control (MPC) structure. [ABSTRACT FROM AUTHOR]

Advanced nonlinear controllers are a desirable solution to rotorcraft flight control as they can solve the system high nonlinear dynamic behavior. However, conventional nonlinear controllers such as Nonlinear Dynamic Inversion (NDI) controller heavily rely on the availability of accurate model knowledge and this can be problematic for rotorcraft. Therefore, incremental control theory can solve the modelling errors sensitivity by relying on the information obtained from the sensors instead. The paper applied the Incremental Nonlinear Dynamic Inversion (INDI) controller to rotorcraft case. It will be demonstrated that, for rotorcraft, the incremental nonlinear controllers depend on the delays introduced in the controller by the rotor dynamics. To correct this behaviour, residualization and synchronization methods need to be applied accordingly in order to remove the effects of rotor flapping (disctilt) dynamics from the controller. These particularities of rotorcraft in dealing with advanced controllers shows that incremental nonlinear controllers can have relatively small stability robustness margin and careful controller design is needed in order to account properly for rotorcraft time delays and unmodelled dynamics. [ABSTRACT FROM AUTHOR]

Summary: In this article, we introduce a novel family of Lyapunov‐based adaptive kinematic control laws developed to solve the trajectory tracking problem for a differential‐drive mobile robot under the influence of both longitudinal and lateral wheel slip. Each adaptive controller in this family is constructed by augmenting a nonadaptive nominal controller, originally designed for the slip‐free case, with an update rule capable of estimating the longitudinal slip. In the absence of lateral slip and under constant longitudinal slip, we establish the convergence of the trajectory tracking error to zero and, assuming a persistent excitation condition, we also demonstrate the convergence of the slip estimate error to zero. When lateral slip is present, we analyse a particular control law from our family of adaptive controllers. This law ensures the trajectory tracking error is uniformly ultimately bounded around the origin, demonstrating the robustness of our adaptive control scheme in dealing with time‐varying longitudinal and lateral slip. The validity of our approach is assessed through comprehensive numerical simulations. [ABSTRACT FROM AUTHOR]

The frequent traffic congestion on the highways has necessitated the formulation of a suitable traffic control strategy that reduces transportation time. The main objective of any highway traffic control strategy is to regulate the traffic flow in such a manner that the participating vehicles can travel as fast as possible without any congestion. To accommodate this goal and to maintain a steady stream of vehicles on the highway, this work proposes a Model Predictive based Sliding mode Cooperative and Integrated Control that produces a real-time applicable, optimal, robust, and stable control command. In this sense, the traffic control problem is reformulated as a second-order nonlinear affine state space model, and then, the resulting control signal is derived via Model Predictive based Sliding Model Control (MPSMC). The proposed method delivers better results w.r.t established strategies in terms of Total Time Spent (TTS) and computation time. Furthermore, the Utilization Cost Function has been introduced in this work, rather than TTS, and it has been applied to the optimization problem of MPSMC to better utilize the existing characteristics of the highway, which has yielded a smoother traffic flow. [ABSTRACT FROM AUTHOR]

This article is concerned with the systematic design of robust stabilising and tracking controllers for a certain class of nonlinear systems suffering from unmatched and nonvanishing disturbances and nonlinearities, the combined action of which is referred to as 'perturbations'. The proposed composite state feedback controllers combine a linear and a nonlinear component, the design of which is based on simple and mature control design methodologies (pole placement and Lyapunov redesign). The novel design of the nonlinear component guarantees the Uniform Ultimate Boundedness (UUB) of the error between the plant and a model representing the ideal behaviour. It is also shown how the stabilisation result can be generalised for the case where matched uncertainties and nonlinearities are also present in the plant. The efficacy of the proposed methodologies is demonstrated via numerical examples. [ABSTRACT FROM AUTHOR]

The paper presents a nonlinear field-oriented control technique based on the tensor product representation of the nonlinear induction machine model and the solvability of linear matrix inequalities. The nonlinear model has 32 quasi linear parameter-varying equivalent variants, and it is shown that only half of the models result in feasible controller. Two control goals are realized: torque control and speed control. The controller is a nonlinear state feedback controller completed by integral action. A new block diagram is investigated for speed control. The controller gains are designed by the solution of linear matrix inequalities to solve the Lyapunov inequality to obtain a stable and fast response and constraints on the control signal. The presented methods are verified and compared by simulations. [ABSTRACT FROM AUTHOR]

This study introduces a novel framework for optimizing energy efficiency and computational load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph Attention Networks for state awareness and decision-making, the framework employs adaptive sensor and filter toggling strategies to dynamically manage system resources through real-time inferential processes. Our framework maintains continuous robot operation in the presence of sensor noise and environmental disturbances by activating additional sensors, thus preventing system shutdowns or stalls. Few-shot meta-learning techniques further augment the model's adaptability, allowing it to generalize and make real-time decisions across varying operational conditions. An extensive evaluation reveals a reduced average energy consumption, compared to 'always-on' configurations, by 13.71% and CPU utilization by 29.07%, without compromising system performance and safety. We also introduce Matching Networks and Siamese Networks with different loss functions to assess the system's capability to adapt to different levels of criticality. Our experiments demonstrate that the system prioritizes performance and safety in high-critical scenarios while maximizing energy efficiency in less critical situations. The framework's real-time decision-making capability is particularly crucial in human–robot environments and holds significant implications for future applications in nonlinear control systems and resilient robotic systems. [ABSTRACT FROM AUTHOR]

Manipulators are multi-rigid-body systems composed of multiple moving joints. During movement, the Coriolis force, centrifugal force, and gravity of the system undergo significant changes. The last three degrees of freedom (DOFs) of the wrist joint of a manipulator control the end attitude. Improving the command tracking accuracy of the wrist joint is a key challenge in controlling the end attitude of manipulators. In this study, a dynamics model of the mechanical arm–wrist joint is established based on the Lagrange method. An adaptive continuous robust integral of the sign of the error (ARISE) controller is designed using the reverse step method. Additionally, a linear extended state observer (LESO) is employed to estimate the time-varying interference existing in the system and compensate for it in the designed control rate. The stability of the Lyapunov function and the boundedness of the observer are proven. The proposed control method for the wrist joint is compared with other controllers on an experimental platform of multi-DOF hydraulic manipulators. The results demonstrate that the proposed method improves the control performance of hydraulic manipulators. The application of this method offers a new strategy and idea for achieving high-performance tracking control in hydraulic manipulators. [ABSTRACT FROM AUTHOR]

Most applications of automatic differentiation concern the field of optimization in the broadest sense. This means that many applications only need first and second order derivatives. An exception are control engineering problems, where higher order derivatives are required. This contribution addresses a control engineering problem, namely the estimation of variables that are not measured directly. This problem can be solved with high gain observers and high gain differentiators. They are typically calculated symbolically. We show how automatic differentiation can be used for the implementation of high gain differentiators. [ABSTRACT FROM AUTHOR]

This study focuses primarily on sulfur dioxide (SO2) emissions control problem in a wet flue gas desulfurization (WFGD) process, and our objective is to design an intelligent control system so that the outlet SO2 concentration satisfies the SO2 emission standard. In our approach, a multimodel control framework, which is made up of a linear robust controller and a neural controller, is integrated with the invasive weed optimization (IWO) algorithm in an elegant fashion and used for SO2 emissions control purposes. A case study is carried out based on operation data from a 600 MW coal-fired unit, and simulation results show that IWO-based automatic clustering can identify different operating modes in the WFGD process with high accuracy. Further, the established multimodel control system can remove SO2 emissions effectively. Experimental results show that SO2 emissions can be removed effectively with the proposed method, and this could provide engineering guidance to design a WFGD control system. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for regulating the pose of a free-flying dual-arm anthropomorphic space manipulator system (SMS) using a finite-time state-dependent Riccati equation (SDRE) controller. The proposed system finds applications in on-orbit satellite inspection, servicing, space structure assembly, and debris manipulation. The dual-arm SMS presented in this work consists of two 7 degrees of freedom (DoF) robotic arms mounted on a free-flying spacecraft, resulting in a complex 20-DoF system. Due to the high number of DoFs, advanced controller design and efficient computations are necessary. The finite-time SDRE controller relies on the state-dependent coefficient (SDC) parameterization matrices, which are nonlinear apparent linearizations of the dynamics. Conventionally, the computation of SDC matrices is offline and relies on the a priori derivation of the analytical equations governing the dynamics of the system. However, this strategy becomes computationally impractical for high DoF plants. To overcome this issue and deliver a more viable solution, a numerical method to construct and update the SDC matrices at each time step is presented. This approach relies on a screw-theory-based recursive Newton–Euler algorithm designed to reconstruct the manipulator inertia and Coriolis matrices. These quantities are the building blocks of the SDC parameters used in the synthesis of the SDRE controller. Simulation results demonstrate the performances of the finite-time SDRE controller augmented with the online update of the state-dependent coefficients. • Automatic derivation of the dynamics of free-flying robot with multiple open chains. • Implementation of a Newton-Euler approach for updating state-dependent coefficients. • Finite-time SDRE to control a 20 DoFs, anthropomorphic dual-arm space manipulator. [ABSTRACT FROM AUTHOR]

The paper addresses the exact linearisation of flat nonlinear discrete-time systems by generalised static or dynamic feedbacks which may also depend on forward-shifts of the new input. We first investigate the question which forward-shifts of a given flat output can be chosen in principle as a new input, and subsequently how to actually introduce the new input by a suitable feedback. With respect to the choice of a feasible input, easily verifiable conditions are derived. Introducing such a new input requires a feedback which may in general depend not only on this new input itself but also on its forward-shifts. This is similar to the continuous-time case, where feedbacks which depend on time derivatives of the closed-loop input – and in particular quasi-static ones – have already been used successfully for the exact linearisation of flat systems since the nineties of the last century. For systems with a flat output that does not depend on forward-shifts of the input, it is shown how to systematically construct a new input such that the total number of the corresponding forward-shifts of the flat output is minimal. Furthermore, it is shown that in this case the calculation of a linearising feedback is particularly simple, and the subsequent design of a discrete-time flatness-based tracking control is discussed. The presented theory is illustrated by the discretised models of a wheeled mobile robot and a 3DOF helicopter. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional-order time-varying sliding mode control method with predefined-time convergence for a class of arbitrary-order nonlinear control systems with compound disturbances. The method has global robustness and strongly predefined-time stability. All state errors of the system can converge to zero at a desired time, which can be set arbitrarily with a simple parameter. The strongly predefined-time convergence of the system is clearly demonstrated by the analytic expression of state error, which is obtained by solving fractional-order differential equations corresponding to the sliding mode function. The simulation results show that the proposed method still has good control performance in the presence of input saturation and external interference. • The main contributions of this paper are summarized as follows: • A predefined-time fractional-order time-varying sliding mode control scheme is proposed for a class of arbitrary order nonlinear control systems with complex disturbances. All state errors are simultaneously converged to zero by the scheme at a time, which can be presented by a simple parameter. • Different from most of the existing methods, this paper obtains the analytical expression of the state errors by solving the fractional-order differential equation to prove the predefined-time convergence of the system. • The fractional-order term and time-varying term are introduced into the proposed control scheme, which improves the control performance and makes the system have global robustness. The simulation results show that the proposed controller has strong robustness and resistance to input saturation. [ABSTRACT FROM AUTHOR]

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Flip ambiguities are a notorious issue with distance-based formation control due to the presence of unwanted equilibrium points in the formation dynamics. We propose a switched control system for preventing these ambiguities in 3D formations composed of tetrahedra. The approach contains a switching strategy that steers the formation of mobile robots towards the desired configuration for all initial positions, excluding certain collocated, collinear, or coplanar cases, by applying the standard distance-based controller and/or rigid-body maneuvers to subformations. Simulations demonstrate that the proposed formation control system can lead to faster formation acquisition and less control effort than an existing method. [ABSTRACT FROM AUTHOR]

In this paper, a novel feedback control strategy for quadrotor trajectory tracking is designed and experimentally tested with proof of exponential stability, using the Lyapunov transformations theory. The controller is derived from an inner-outer loop control structure, namely by considering the position system coupled through an interconnection term with the attitude system. For the design of the position controller, the considered dynamics are worked on the body frame, which is uncommon in the literature, and its synthesis derives from theories such as Pontryagin’s maximum principle, Lyapunov theory, and Linear Quadratic Regulator (LQR), which ensure Input-to-state stability, steady-state optimality, and global exponential stability. The attitude system is based on an error quaternion parameterization via a nonlinear coordinate transformation matrix followed by a state input feedback, rendering the system linear and time-invariant. Under a correct transformation, LQR theory ensures almost exponential stability and steady-state optimality for the overall interconnected closed-loop systems. Experimental and simulation results illustrate the performance of the tracking system onboard a quadrotor. [ABSTRACT FROM AUTHOR]

Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed. [ABSTRACT FROM AUTHOR]

The paper considers the application of feedback control to orbital transfer maneuvers subject to constraints on the spacecraft thrust and on avoiding the collision with the primary body. Incremental reference governor (IRG) strategies are developed to complement the nominal Lyapunov controller, derived based on Gauss variational equations, and enforce the constraints. Simulation results are reported that demonstrate the successful constrained orbital transfer maneuvers with the proposed approach. A Lyapunov function based IRG and a prediction‐based IRG are compared. While both implementation successfully enforce the constraints, a prediction‐based IRG is shown to result in faster maneuvers. [ABSTRACT FROM AUTHOR]

Tail-sitters aim to combine the advantages of fixed-wing aircraft and rotorcraft but require a robust and fast stabilization strategy to perform vertical maneuvers and transitions to and from aerodynamic flight. The research conducted in this work explores different nonlinear control solutions for the problem of stabilizing a tail-sitter when hovering. For this purpose, the first controller is an existing strategy for tail-sitter control obtained from the literature, the second is an application of Nonlinear Dynamic Inversion (NDI), and the last one is its incremental version, INDI. These controllers were implemented and tuned in a simulation in order to stabilize a model of the tail-sitter, complemented by estimation methods that allow the feedback of the necessary variables. These estimators and controllers were then implemented in a microcontroller and validated in a Hardware-in-the-Loop (HITL) scenario with simple maneuvers in vertical flight. Lastly, the developed control solutions were used to stabilize the aircraft in experimental flight while being monitored by a motion capture system. The experimental results allow the validation of the model of the X-Vert and provide a comparison of the performance of the different control solutions, where the INDI presents itself as a robust control strategy with accurate tracking capabilities and less actuator demand. [ABSTRACT FROM AUTHOR]

The primary objectives behind transitioning from fossil fuels to green energy sources, with a particular focus on reducing both electricity costs and carbon emissions. This transition has prompted various sectors and sports bikes, to embrace renewable energy alternatives, with a specific emphasis on technologies such as photovoltaic systems, energy storage solutions, and power generation from machines. The core subject of investigation in this paper is the application of renewable energy sources within sports bikes, with a particular emphasis on a hybrid system. This hybrid system incorporates DC/DC, AC/DC, and DC/AC converters to meet the energy requirements of the facility. The central aim of the research is to identify the most economically efficient scale for a self-sufficient hybrid photovoltaic system that integrates stationary generators and battery storage. The research seeks to optimize the balance between cost-effectiveness and sustainable energy provision in the context of sports facilities. [ABSTRACT FROM AUTHOR]

For the speed-tracking control of permanent magnet synchronous motor (PMSM) systems, fast dynamic response and high reference speed require a large transient current to provide sufficient electromagnetic torque. However, the excessive transient current may damage the drive circuit and threaten the system safety. Besides, it is also important to pay attention to the influence of multiple disturbances including system parameter uncertainties and unknown load torque variation. To solve these problems, we propose a nonlinear speed-tracking control approach for uncertain PMSM systems. More specifically, based on the cascade control structure and by introducing auxiliary smooth function, we first develop a nonlinear current-constrained controller (CCC) to guarantee the speed tracking, disturbance rejection, and overcurrent protection simultaneously. In order to improve the robustness of the desired performances, we further implement extended state observer (ESO) techniques to propose an ESO-based CCC (ESO-CCC). Finally, comparative experiments are implemented in a 5.5 kW PMSM platform to verify the performance of the proposed two controllers. [ABSTRACT FROM AUTHOR]