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This paper addresses the synchronization problem of coupled harmonic oscillators via event-triggered impulsive control. Different from the existing results where both position and velocity measurements are required to achieve the synchronization, two kinds of event-triggered impulsive mechanisms, that is, the position-only and velocity-only approaches are proposed respectively, which effectively extends the event-triggered impulsive control strategy to the synchronization problem with limited information. Sufficient conditions are proposed to exhibit the convergence behavior of the coupled harmonic oscillators. Meanwhile, it is derived that Zeno behavior can be excluded by using the designed controller. Two simulation examples are illustrated to substantiate the performance of the proposed synchronization protocols.

In this paper, the iterative learning control (ILC) problem is investigated for a class of time-invariant parabolic singular distributed parameter systems. Initially, the singular distributed parameter systems is decomposed into infinite number of singular systems based on the separation principle. Meanwhile, the slow-fast subsystems are introduced via singular value decomposition method. Then, a novel mixed PD-type ILC algorithm with finite dimension is designed for the low dimensional slow part and the corresponding convergence conditions are manifested. With the proposed controller, the output error of high dimensional fast complement can satisfy the given value instead of neglecting the effect of high dimensional modes. Furthermore, under the aforesaid ILC law and the appropriate number of the low dimensional slow part, the resulting tracking error of parabolic singular distributed parameter systems can converge to any small tracking accuracy. Finally, simulation results on the distributed building automatic temperature system verify the convergence and effectiveness of the mixed PD-type ILC algorithm.

This paper proposes an improved back-stepping control approach and its application to small nonlinear UAV control systems with uncertainties such as external disturbance. Unlike traditional back-stepping control methods, the idea of prescribed performance function (PPF) is incorporated into the control design, such that both the transient and steady-state control performance can be strictly guaranteed. Moreover, we design a novel tracking differentiator to avoid the “differential expansion” problem well caused by the calculation of derivative. Significantly, the function approximators (e.g. neural networks) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. Finally, the numerical simulation verifies the convergence and robustness of the system, and the results show that the control strategy can obtain better transient and steady-state performance.

In this study, an adaptive linear active disturbance rejection control is proposed to achieve steady levitation of the magnetic levitation ball. The proposed algorithm was designed and its convergence was proven via derivation. It can address the difficulty in parameter tuning of the controller and realize the real-time self-adaptive optimization of parameters. Besides, to verify the effectiveness of the proposed parameter tuning strategy, we analysed the anti-interference ability and tracking effects of SMC, LADRC and A-LADRC in different cases. Finally, the results showed that the anti-interference ability of A-LADRC is stronger than SMC and LADRC. In addition, the anti-interference effect intensifies when the interference increases. The designed controller also performs the best tracking performance among these three controllers. Therefore, A-LADRC exhibits better robustness and dynamic performance.

In this paper, an unknown system dynamics estimator (USDE)-based sliding mode control (SMC) is presented for quadrotor attitude tracking with the aims of conquering control oscillation problem and achieving great robustness. To enhance the anti-disturbance ability of quadrotors, the invariant manifold-based USDE, with features of simple structure and exponential error convergence, as well as low computational consumption, is introduced to recover the unknown disturbances by basic filter operation and algebraic calculation. Aiming at accelerating sluggish transient convergency in conventional disturbance compensation-based SMC, a novel quadrotor attitude SMC strategy containing a continuous double hyperbolical reaching law (DHRL) is designed, which greatly alleviates control oscillations without sacrificing dynamic response. The dramatical advantage of proposed scheme is that the chattering-free property and fast dynamic response with precise static tracking performance are simultaneously achieved. Meanwhile, all the error signals involved in the closed-sloop system are proved to be bounded by Lyapunov analysis. Eventually, the effectiveness of explored control strategy is illustrated through simulations and experiments.

Both non-equal trail lengths and non-zero initial errors are practical challenges to learning control of robotic and mechatronic systems. Iterative learning to update input is still desired, because of the repetitive motion nature of the controlled objects. This paper concerns with the adaptive iterative learning control method for performing non-identical tasks. The time scaling technique is applied to normalize non-equal trail lengths, while the error-tracking approach is adopted for coping with initial errors. Theoretical results for performance analysis are presented in detail. The uniform convergence of the tracking error is examined, while boundedness of the variables in the closed-loop is characterized. It is shown that the fully-saturated learning algorithm plays an important role in assuring uniform boundedness of the control input. The proposed control design method does not require the magnitude transformation, and removes the assumption of identical initial conditions. The time scaling technique is verified to be effective in assuring the expected performance, for tracking tasks with non-equal trail lengths and initial errors.

This paper mainly addresses the issue of fast finite-time consensus (FTC) algorithms for a group of high-order uncertain nonlinear non-strict-feedback Multi-agent Systems (MASs) with time-varying asymmetric full state constraints. In order to guarantee the state constraints are satisfied, an appropriate asymmetric nonlinear mapping (NM) is employed to transform the original full state constrained system into a corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory and backstepping recursive design scheme, the uncertainties and non-strict-feedback form are considered to propose a distributed adaptive FTC protocol which can guarantee tracking error reaching a region in finite time. Finally, an example is given to show that the designed control law can perform effective control.

Leader follower formation of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has found numerous applications such as surveillance of critical infrastructure, industrial automation and disaster management emergency. For completion of high precision group tasks, the choice of appropriate control mechanism is of utmost importance. In presence of environmental effects,external disturbances and parametric uncertainties in the UAVs and UGV models, the controller design process is a challenging task. In order to address the aforementioned problems and to ensure minimum tracking errors and fast convergence of the states, this article proposes an adaptive robust formation and trajectory tracking control scheme for a leader follower formation of UAVs and UGV using Non-Singular Terminal Super Twisting Sliding Mode Control Method.Adaptive compensators are derived based on Lyapunov function method and stability of the proposed controllers is guaranteed. Two variants of the control schemes namely Adaptive Super Twisting SMC (AST-SMC) and Adaptive Non-Singular Terminal Super Twisting SMC (ANSTS-SMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, and with the proposed ANSTS-SMC control scheme, the measured $[X~Y]$ tracking errors for leader, follower1 and follower2 UAVs are $[{0~0.01}]m$ , $[{0.01~0.02}]m$ and $[{0.01,~0.02}]m$ respectively, While with the AST-SMC method the peak $[X~Y]$ tracking errors for leader, follower1 and follower2 UAVs are $[{0.05~0.05}]m$ , $[{0.1~0.2}]m$ and $[{0.05~0.1}]m$ respectively. The proposed leader follower formation can be effectively used to monitor solar/PV panels and cables in large solar parks.

Biological pest control has a strong advantage in its non-chemical effects on the environment. In this study, a fixed-time synergetic control scheme for the biological pest control problems represented by the $n$ -dimensional Lotka-Volterra model was proposed. The proof of stability shows that the proposed controller can regulate the biological pest control systems with the characteristic of fixed-time convergence. The performance of the proposed scheme was demonstrated through simulation studies. The simulation results show that the pre-specified bound of the settling time can be satisfied regardless of the initial conditions, confirming a desired fixed-time convergence characteristic. Moreover, unlike the existing control policy based on the sliding mode control, the control inputs of the proposed policy are free from chattering.

This article presents a novel simplified method to implement the finite set - model predictive control technique for photovoltaic generation systems connected to the ac network. This method maintains the advantages of the conventional finite set - model predictive control, such as fast response, simple implementation, and easy understanding; but it also eliminates the use of a cost function and hence the weighting factors, instead, it finds the optimal operating state directly from the model and the discrete number of valid states of the converter. Although the proposed algorithm does not compute a cost function, it is able to select the inverter state that minimizes the tracking error by using a hexagonal convergence region. The main advantage of this technique is to reduce the computational cost in 43% of the algorithm that selects the best state, presenting a simple and complete algorithm without compromising the predictive control performance. The proposed algorithm properly operates under various conditions such as changes in the network frequency and changes in the system parameters.

This paper investigates command filter backstepping fault-tolerant control for a class of nonlinear systems with nonlinear faults. In particular, the system nonlinear faults are non-affine, which are handled by the filtered signal and fuzzy logic systems approximate Lemma. Command filter is used in this paper, which eliminates the “explosion of complexity” of the classical backstepping and compensates the output of the filter to the derivative of the virtual control. In addition, the controller design procedure has been shown in detail, while the stability of the closed-loop system and the convergence of the tracking error are proved via Lyapunov Theorem. Simulation examples are given to illustrate the feasibility and effectiveness of the proposed scheme.

This paper proposes a neural-network-based state estimation (NNSE) method that aims to achieve higher time efficiency, improved robustness against noise, and extended observability when compared with the conventional weighted least squares (WLS) state estimation method. NNSE consists of two parts, the linear state estimation neural network (LSE-net) and the unobservable state estimation neural network (USE-net). The LSE-net functions as an adaptive approximator of linear state estimation (LSE) equations to estimate the nominally observable states. The inputs of LSE-net are the vectors of synchrophasors while the outputs are the estimated states. The USE-net operates as the complementary estimator on the nominally unobservable states. The inputs are the estimated observable states from LSE-net while the outputs are the estimation of nominally unobservable states. USE-net is trained off-line to approximate the veiled relationship between observable states and unobservable states. Two test cases are conducted to validate the performance of the proposed approach. The first case, which is based on the IEEE 118-bus system, shows the comprehensive performance of convergence, accuracy, and robustness of the proposed approach. The second case study adopts realworld synchrophasor measurements, and is based on the Jiangsu power grid, which is one of the largest provincial power systems in China.

This paper investigates an adaptive coordinated control problem of spacecraft formation flying (SFF) system subject to uncertain mass and external perturbations. First, a general unified structure is proposed to represent the adjustable performance functions like the exponentially and appointed-time convergent ones. Then, an adaptive coordinated controller is developed based on a two-layer performance envelope to characterize the transient and steady-state formation maneuvering behaviors quantitatively of SFF system. Compared with the existing coordinated control approaches, the prominent advantage of the proposed one is that the preassigned position tracking and consensus tracking performance is guaranteed simultaneously while maintaining the scheduled formation configuration. Meanwhile, the users can configure and adjust the convergence rate (like the exponential or finite-time convergence) of the formation tracking errors by choosing different parameters arbitrarily in the unified structure for the performance functions. Finally, a group of numerical examples are organized to validated the effectiveness of the proposed coordinated control approach.

This paper proposes a new model-free sliding mode tracking differentiator with adaptive gains for reliable filtering and derivative estimations from noisy signals by improving a Levant and Yu’s sliding mode tracking differentiator. Particularly, the proposed tracking differentiator employs a nested generalized signum function for reducing overshoot during convergence. Moreover, a model-free adaptive gain scheduling is adopted for balancing the tracking and filtering performances. The advantages of the proposed tracking differentiator is validated through numerical examples.

In this paper, we investigate the problem of adaptive finite-time tracking control for a class of nonlinear multi-input and multi-output (MIMO) systems with a state coupling relationship between subsystems, which is called the structure of nonstrict-feedback. Based on the quasi-fast finite-time design technique, an adaptive controller is obtained for the considered MIMO systems. The advantages of the presented control scheme lie in that the “explosion of complexity” problem in the backstepping process is solved by utilizing the command filter technique, and the design difficulties caused by nonstrict-feedback structure are also overcome. It is shown that all the signals in the closed-loop system are bounded and the convergence performance with the bounded tracking errors is guaranteed in a finite time. Finally, a practical example is given to show the effectiveness of the proposed method.

In this paper, a robust and safety distributed formation control with unknown external disturbances is researched. For a multi-quadrotors system, a novel nonsingular terminal sliding mode control strategy is studied to realize the formation control with collision avoidance and inter-quadrotor avoidance. The formation controller is redesigned once the system reaches the small field of sliding surface to solve the existence of singular of quadrotor formation and realize the fixed time convergence of singularity region. Then, the position controller and attitude controller are designed to maintain formation configuration with collision avoidance and track the desired angular rate with disturbances, respectively. The global fixed time convergence of quadrotor formation is verified by Lyapunov theory with the fixed time convergence characteristic of singularity region and nonsingular region. At last, simulation results are presented to demonstrate the efficiency of the developed algorithm.

This paper proposed a finite-time backstepping control for a robotic manipulator under the presence of actuator fault, saturation constraints, output constraints, and external disturbance to obtain requirements about the robustness, fast convergence, and high accuracy tracking performance. To manage the above challenges, the proposed control is designed on a transformed model with the backstepping approach and extended state observer. The transformed model is resulted from converting a constrained system based on a transformation technique. So, it provides an ability for the proposed control to obtain the prescribed performance of the output response. Additionally, an extended state observer is conducted to deal with the lumped uncertainties in the system. The essential characteristic of the proposed control is no required knowledge of the actuator faults and external disturbance to be available. Furthermore, fractional-order terms are added in the control laws to enhance the rate of output responses. To demonstrate the advantages of the proposed control in terms of global asymptotic stability, the Lyapunov approach is used to verify the whole controlled system in theory. The proposed control is applied to a 2-degree of freedom (DOF) manipulator and simulated by MATLAB Simulink. Its simulation results are compared to other state-of-the-art methods to exhibit the effectiveness of the proposed control.

In this paper, a finite-time control method is presented for bilateral teleoperators to ensure coordination of the master and slave manipulators in the presence of time-varying delays, external disturbances, and dynamic uncertainties. Without the requirement of acceleration measurement, the master and slave manipulators are assured to coordinate with each other within finite time in free motion. Additionally, the coordination errors are ensured to converge to a neighborhood of the origin in finite time. In the constrained motion, where both the master and slave manipulators are affected by unknown external force from the human operator and the removed environment, the bilateral teleoperation system is proved to be stably provided a certain degree of transparency performance during operation. Stability and finite-time tracking are investigated via Lyapunov-Krasovskii theory for the bilateral teleoperation system under time-varying delays and external forces. Subsequently, numerical examples and experiments are presented to demonstrate the efficiency and efficacy of the proposed control approach for teleoperators.

In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in high-frequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course.

This work studies a novel sampled-data adaptive extended state observer (ESO) based composite control approach for the digital nano-positioning systems with sampling restrictions. In the proposed control architecture, a sampled-data ESO with an output predictor is designed to estimate states and disturbances within a continuous period of time, where the observer gains are adaptively updated. To deal with the inter-sample disturbances, a multirate disturbance compensator is then worked out by upsampling, with which a discrete composite feedback controller with linear and nonlinear parts is employed aiming at improving the transient performance of positioning. Moreover, the estimation convergence and the stability are analyzed by means of Lyapunov methods. The proposed control method is then applied to the simulations and experiments in a piezoelectric nano-positioning system so as to validate its effectiveness.

This paper presented an adaptive and fractional-order sliding mode control(FOSMC) method for the unmanned quadrotor helicopter. The aircraft system includes actuator fault and external disturbances. The switching sliding mode law enables the system to reach the predefined sliding surface from arbitrary states. Then the equation control law keeps the trajectory stay over the sliding hyperplane. In order to make sure sliding motion from the arbitrary states to the surface within limited time, a novel fractional-order power switching control law is developed. System actuator failures are compensated online with adaptive control laws. The controllers are derived from the Lyapunov theory, which guarantees that the controllability and feasibility. This novel control strategy has higher tracking accuracy through the timely faults and disturbances compensation law. The presented fractional-order sliding mode scheme improves the speed of system convergence and shortens the reaching time. The adaptive strategy estimated the bounds of the disturbances and good robustness has been achieved. Simulation results shown that the presented strategy has numerous advantages in terms of attitude and position tracking.

This paper presents a unique two-fold adaptive linear neural network (ADALINE) for fast and accurate measurement of fundamental, harmonics, sub-harmonics, inter-harmonics and decaying DC components of a distorted current signal with additive noise. The preceding parallel approach is termed as Master-Slave ADALINE (MS ADALINE). The Slave-ADALINE adopts least mean square (LMS) algorithm with a fixed and large step-size for weight vector adjustment. During the training interval or transients, this filter performs a significant role. On the other hand, the Master-ADALINE uses a variable step-size LMS algorithm for achieving a small steady-state error. At the end of each iteration, the local averages of the squared errors of both the ADALINE's are calculated and weights of the Master-ADALINE are updated accordingly. The amplitudes and phases of desired frequency components can be worked out from Master-ADALINE's weights. The proposed architecture improves the convergence speed by establishing an independent control action between the steady-state error and the speed of convergence. The simulation results of this method under various operating situations are analyzed and compared with single fold ADALINE structure that obeys dynamic step-size LMS (DSSLMS) adaptation rule. Eventually, a scaled laboratory prototype has been developed for the validation of the proposed technique in real-time utilization. This innovative research finding makes the power system smart and precise.

This article proposes a synchronization technique for uncertain hyperchaotic systems in the modified function projective manner using integral fast terminal sliding mode (I-FTSM) and adaptive second-order sliding mode algorithm. The new I-FTSM manifolds are introduced with the aim of having the fast convergence speed. The proposed continuous controller not only results in the robustness and high-accuracy synchronization in the presence of unknown external disturbances and/or model uncertainties but also helps alleviating the chattering effect significantly. Numerical simulation results are provided to illustrate the effectiveness of the proposed control design technique and verify the theoretical analysis.

The note focuses on the issue of iterative learning control for a kind of non-affine nonlinear switched systems with state time delay and external noise. According to the structural properties of the systems, a proportional-type scheme is considered. Then, the effect of both constant time delay and external noise on the tracking performance is explored by employing contraction mapping principle and Bellman-Gronwall inequality. Results reveal that time delay has insignificant influence on learning performance if it is less than dwell time, and the output follows into a permissible neighbourhood of the desired trajectory on the whole operation time interval if noise fluctuates within finite boundary. The system switching will reduce the convergence speed of the ILC algorithm, but will not change the convergence in iterative domain. Finally, simulation is given to illustrate the feasibility of the theoretical analysis.

An adaptive second order recursive terminal sliding mode (SO-RTSM) is developed for the steering angle control of a four-wheel independent steer-by-wire (4WI-SbW) system. A recursive sliding mode structure is presented based on which the reaching phase is eliminated. Moreover, the finite time convergence of the tracking error is guaranteed compared to common integral sliding mode control (ISMC). Furthermore, to suppress chattering, instead of a first-order sliding mode used in a conventional ARTSM controller, a second order nonsingular terminal sliding mode is adopted such that reaching control input is obtained in an integral form. The advantage of the proposed controller is that the chattering can be effectively reduced without decreasing the control accuracy. Experiments are carried out on a real 4WI-SbW vehicle to demonstrate the fast convergence rate and small chattering of the proposed controller.

In this paper, we consider iterative learning control for trajectory tracking of robotic manipulator with uncertainty. An improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic manipulator. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. To deal with uncertainty, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are incorporated for estimation of uncertain parameters by constructing extended system states. The performances between the two filters are also compared. Simulations on a 2DOF Robot manipulator demonstrate that the improved Q-ILC with parameter estimators can achieve faster convergence and better transient performance compared to the original Q-ILC, in the presence of measurement noise and model uncertainty.

This article presents a robust control method for trajectory tracking and balancing of a ballbot with uncertainty. Since the ballbot is an underactuated system, previous studies have designed controllers using a hierarchical strategy and/or multi-loop approach. However, multi-loop control systems require several controllers and the hierarchical strategy has a local minimum problem that does not guarantee the convergence of all errors globally. To overcome these drawbacks, we introduce a virtual angle and design a sliding mode controller with a single-loop control system. As a result, the proposed controller is simple and can achieve simultaneous tracking and balancing of the ballbot. From Lyapunov stability theory, it is proven that the tracking and balancing errors of the ballbot are uniformly ultimately bounded and can be made arbitrarily small. Finally, simulation results are presented to verify the proposed control system.

Proportional-Derivative (PD) control is one of the most widely used controllers, especially for robot manipulators. When the robot presents gravitational terms, PD control cannot guarantee position convergence, therefore compensation is required such as PD with gravity compensation, PD+G. PD+G control requires knowledge of the gravitational term and there exist several results that prove global asymptotic stability. However, there is no method to tune the PD gains. In this work, a novel method to tune the PD+G controller is proposed. The tuning method is obtained using the global asymptotic stability result of the La Salle's theorem and robot dynamics properties. A comparison between previous works is realized via simulations and experiments to verify our approach. The results show fast and smooth convergence to the desired reference without overshoots.

Nanopositioning systems are very popular and playing an increasingly vital role in micro and nano-scale positioning industry due to their unique ability to achieve high-precision and high-speed operation. However, hysteresis, commonly existing in piezoelectric actuators, degrades the precision seriously. Uncertain dynamics and sensor noises also greatly affect the accuracy. To address those challenges, a variable bandwidth active disturbance rejection control (VBADRC) is proposed and realized on a nanopositioning stage. All undesired issues are estimated by a time-varying extended state observer (TESO), and cancelled out by a variable bandwidth controller. Convergence of the TESO, advantages of a TESO over a linear extended state observer (LESO), and the closed-loop stability of the VBADRC are proven theoretically. Improvements of the VBADRC versus the linear active disturbance rejection control (LADRC) are validated by simulations and experiments. Both numerical and experimental results demonstrate that the VBADRC is not only able to provide the same disturbance estimation ability as the LADRC, but also more powerful in noise attenuation and reference tracking.

In this paper, a dynamic fractional order sliding mode control method based on a double feedback fuzzy neural network controller is proposed to deal with the unknown parameters and upper bound of uncertainty. Firstly, the switching function of the dynamic fractional order sliding mode control is designed, which not only fixes switching function of the ordinary sliding mode control, but also increases the fractional order, so that the switching function has a higher degree of freedom. In addition, the expert experience of fuzzy logic and the self-learning ability of neural network are used to improve the control accuracy and estimate the upper bound of uncertainty. Meanwhile, by using Lyapunov stability theory, the adaptive laws of unknown parameters in the system are derived to realize online adjustment, which increases the robustness of the system. Finally, the simulation results show that the proposed control method is more effective than the ordinary adaptive sliding mode control method in terms of convergence speed, parameter fitting effect, output signal tracking speed and tracking error.

In this paper, we introduce decentralized robust adaptive load frequency control schemes for interconnected multi-area power system networks in the presence of uncertainty. The uncertainty in power networks may appear from load variations, modeling errors, fault disturbances and changes of the power system structure for bilateral contracts between distribution and transmission companies for future open and liberal electricity trading policy. First design comprises state feedback vector terms with the robust and adaptive terms to learn and compensate uncertainty associated with multi-area power system networks. Second design integrates state and sliding mode feedback vectors with the robust and adaptive terms to deal with uncertainty. The control algorithms are designed by using Lyapunov method. It is shown in our convergence analysis that the variation of the states of multi-area power system networks are bounded and their bounds asymptotically convergeto zero. Compared with existing results, the proposed method does not require the exact bounds of the uncertainty associated with the power system networks. The design is simple and easy to implement as it does not require the exact model and matched uncertainty of the power system networks. Evaluation results on multi-area power system networks are given to demonstrate the effectiveness of the proposed method for real-time applications.

This paper investigates distributed asymptotic consensus protocol for a group of cloud connected Euler-Lagrange nonlinear systems with the presence of bounded uncertainty. The consensus protocol is designed by combining linear sliding surface vectors with robust adaptive learning algorithms. The sliding surface is designed by comprising position and velocity signals of the leader and neighboring follower Lagrange systems. Adaptive learning algorithm uses to learn and compensate bounded uncertainty associated with parameters and other external disturbance uncertainty. Lyapunov and sliding mode control theory uses to design and illustrate the convergence of the closed loop system under proposed protocol. The convergence analysis has three parts. In first part, it proves that the position and velocity consensus error states are bounded provided that the parameter estimates are continuous and bounded by positive constant. The second part guarantees that the sliding mode motion occurs for each Lagrange system in finite-time. The third part ensures that the states for a group of follower Lagrange systems can achieve asymptotic consensus tracking provided that the interaction communication topology has a directed spanning tree. This analysis shows asymptotic consensus property of the position and velocity consensus error states on the sliding mode surface. The design and implementation of the proposed asymptotic consensus protocol is easier as it does not use the exact bound of the uncertainty.

Artificial intelligence technique based on fuzzy logic is increasingly used for the design of controller for maximum power point tracking (MPPT) in order to harvest maximum energy from photovoltaic (PV) generators. In this paper, a detailed analysis of the literature review of relevant works of fuzzy logic based MPPT algorithms for PV applications is elaborated first. Fuzzy Logic (FL) based MPPT techniques available in the literature are classified into three categories: An adaptive FL-based MPPT, FL combined with a classical technique (HC, InC, P&O) and FL combined with another intelligent technique (PSO, ANFIS, GA). Then a Fuzzy Logic Control (FLC) based MPPT algorithm for PV system is proposed. The FLC scheme design and rule table are presented in detail and based on the concept of asymmetric membership functions. The proposed scheme is implemented in real time using a dSPACE DS1104 board. From the experimental results, it is found that the proposed MPPT algorithm is simple, accurate and provides faster convergence to the maximum power point compared to the FL methods available in the literature.

This work investigates robust asymptotic consensus tracking problems for a group of cloud-connected leader-follower unmanned aerial vehicles with uncertainty. The protocols for attitude and position subsystems dynamics are constructed by using the states of the local and neighboring vehicles provided that they are connected by local area networks. Robust adaptive learning algorithms are also integrated with both protocols to learn and adapt to the modeling errors and external disturbance uncertainties. Lyapunov method and Graph theory use to prove that the proposed protocol allows the vehicles to reach an agreement with follower vehicles and track the states of the leader vehicle asymptotically. Convergence analysis shows that consensus protocol can force the states of the follower MAVs to track the state of the leader MAV asymptotically. The protocol designs are simple and easy to implement as they do not need the exact bound of the uncertainty that appears from external disturbances and the modeling errors. The design does not require the bound of the input of the leader vehicle. The protocol design can ensure faster and robust consensus in the presence of uncertainty as opposed to the convergence of other asymptotic consensus designs.

In this paper, we introduce an online estimation algorithm to monitor the electromechanical oscillations in the interconnected power system operation. The proposed continuous-time adaptive algorithm, provides online estimations of the frequency and damping factor of exponentially damped sinusoidal signals. We discuss the convergence of the estimated parameters to their nominal values under several scenarios. In contrast to most of the existing adaptive techniques, the proposed algorithm does not require an a-priori knowledge about the parameters of oscillations. Furthermore, tuning of the parameters of the proposed structure is straightforward. Simulation studies are also provided to corroborate our claims.

This paper describes a novel nonlinear flatness-based state observer for vehicle dynamics control. The differential flatness property of a nonlinear vehicle model is used to derive a state observer for the lateral dynamics of a vehicle. Furthermore, a second state observer for the combined lateral and longitudinal movement is presented. Additionally, the flat outputs and the state transformations to bring both models into observability normal form are shown and the observer equations are derived. For this purpose, it is shown that the description of the vehicle model in suitable coordinates is necessary. The observers are applied to measurement data of a passenger car. The results show very good estimation results in convergence and tracking. The approach enables linear state estimation for a nonlinear vehicle model with a camera based ground sensor.

This paper presents an improved incremental conductance (Imp-IC) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems. This algorithm is based on the slope condition of the current-versus-voltage characteristic of the PV panel. Its principle consists in comparing the PV panel instantaneous conductance with the incremental conductance of the DC-DC boost converter to determine whether to turn on or turn off the converter power switch in order to achieve the maximum power point. The resulted algorithm is simple, easy to implement and does not require any parameter or step size for its adjustment. Tracking performances of the proposal are experimentally evaluated using the prEN 50530 standard dynamic tests. A comparison with perturb and observe algorithm is performed. Superiority of the proposed algorithm in terms of extracted power, tracking features, convergence speed and oscillatory behaviors cancellation is clearly proven.

In this paper, a new approach for extracting the angular position estimation error (APEE) of an Interior Permanent Magnet Synchronous Machine (IPMSM) is proposed. APEE is obtained from the measurable currents in the stationary reference frame (α,β) without the use of high-frequency injection or Back-electromotive force (BEMF). Moreover, using the APEE and, from the mechanical system of the IPMSM, a High Order Sliding Mode Observer (HOSMO) is used to estimate the angular position, speed and disturbance of the IPMSM. An analysis of convergence of the proposed observer is established using a Lyapunov approach. Furthermore, experimental results and a comparative study are presented to illustrate the performance of the proposed approach.

In this paper, an adaptive continuous sliding mode (SM) control approach is proposed for buck converters by introducing a novel zero-crossing checking mechanism into the twisting algorithm. Instead of the traditional first-order SM approaches, the twisting algorithm can solve their inherent chattering problem and realize the control continuity at the price of unnecessary constant control gain and low precision. Differing from the traditional adaptive mechanisms based on fuzzy logic or Lyapunov stability, the convergence characteristics of the controlled buck converter system are analyzed and further an adaptive twisting algorithm is proposed based on zero-crossing checking online to achieve a time-varying control gain. The number of the zero-crossing checking points can be calculated and the system stability is investigated. Comparative simulations with the traditional twisting algorithm validate the improved algorithm with advantages of high accuracy and excellent performance.

This paper presents an adaptive position observer using normalized position error input in wide-speed sensorless interior permanent magnet synchronous motors (IPMSM) drives. The normalized position error observers of saliency-and model-based field orientations method are established, for which the position information can be unified extracted in wide-speed operation. However, this process introduces extra parameter factors and wide-speed operation especially in regeneration mode worsens the dynamic performance of drives. To realize smooth switch between two different drives and fast dynamic response, concept of equivalent control is incorporated into the observer design. The position error dynamic is extended to a third-order model, and the super-twisting algorithm is adopted to estimate high-order total disturbance. The key step is to introduce the corrective terms and the filtered equivalent control term, which forces the error dynamics to approach a finite-time converge second-order differentiator. A rigorous equivalent control stability analysis is set forth, and a convergence analysis of observer states is elaborated. Effective simulation results are included to verify the feasibility of the proposed scheme.

Induction motors are currently the workhorse of industry due to its simplicity, robustness, and relatively low cost. Performance degradation of induction motors significantly affects the total consumed energy including losses. The conventional approach is undesirable due to loss of productivity time imposed. Moreover, the operational condition is different between the conventional no-load and short-circuit test and the real loaded operation of induction motors affecting the accuracy of the actual parameter values. In this paper, a new method for estimating induction motor rotor parameters during loaded operation is proposed. The proposed method is based on a model reference adaptive system, where Newton-Raphson algorithm is utilized as the adaptive mechanism process. The proposed model automatically adapts the rotor parameters if mismatch between actual and estimation powers is existed. Experimental results are included to verify the proposed method which has convergence in 5th iteration and estimated rotor parameter error less than 1% due to work operations. The proposed method is successfully estimating the actual induction motor rotor parameters under various loading conditions.

Magnetic Levitation System (MLS) feedback linearization uses a suitable control law to linearize its input-output dynamics, enabling multiple linear control strategies to be utilized to track the output trajectories to the desired one. The exact knowledge of the MLS parameters like resistance, inductance, mass, and force constant is required for accurate closed-loop control. In this paper, a Dynamic Regressor Estimation and Mixing (DREM) estimator is proposed which evaluates an entire closed-loop structure without considering any prior knowledge of the MLS parameters. Because of finite-time convergence and enhanced transient responsiveness, DREM is used to estimate the underlying MLS parameters, and these parameter estimates are updated in the transformed system and control law. A systematic approach for feedback linear control of MLS with DREM is presented in this paper. The results of simulations on the MLS model illustrates the effectiveness of the proposed approach.

This paper presents a novel multi-stage method for robust hybrid vision/force control of industrial robots, subject to model uncertainties. It aims to improve the performance of the three phases of the control process: a) free-motion using the image-based visual servoing (IBVS) before the interaction with the workpiece; b) the moment that the end-effector touches the workpiece; and c) hybrid vision/force control during the interaction with the workpiece. First, the camera motion is decomposed into transitional and angular movements. Then, utilizing a switching method, the rotational and translational movements of the camera are controlled in the first two stages, respectively. In the last stage, hybrid vision/force control is activated. For each stage, super-twisting sliding mode controller (STSMC) is utilized. Employing STSMC results in robustness against uncertainties while addressing the chattering problem. A variable-gain sliding surface is also proposed to address the instability and convergence speed issues of the traditional switch IBVS. The experimental results demonstrate the effectiveness and superiority of the proposed multi-stage method compared to other traditional approaches.

To enable effective model-based control for autonomous vehicles, accurate vehicle states and model parameters are required. Most of the state estimation and model-based control consider a linear time-invariant model with fixed system parameters by using their approximate values, such as the weight of the vehicle and tire cornering stiffness. However, these parameters may vary significantly due to the highly nonlinear behavior of the tires of the vehicle and changes in the driving environment. In this work, we propose a state and parameter estimation approach based on the dual extended Kalman filter (DEKF) to obtain accurate states and time-varying parameters. The system is modeled as a linear parameter varying system. New formulations of parameter correction for general DEKF are proposed to achieve faster convergence. Results of simulations and experiments on a real vehicle are included to demonstrate the effectiveness of the proposed method and its benefits for model-based control approaches.

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex. Current sampling-based methods such as Rapidly Exploring Random Trees (RRTs) are not ideal for this problem because of the high computational cost. Supervised learning methods such as Imitation Learning lack generalization and safety guarantees. To address these problems and in order to ensure a robust framework, we propose a Hierarchical Reinforcement Learning (HRL) structure combined with a Proportional-Integral-Derivative (PID) controller for trajectory planning. HRL helps divide the task of autonomous vehicle driving into sub-goals and supports the network to learn policies for both high-level options and low-level trajectory planner choices. The introduction of sub-goals decreases convergence time and enables the policies learned to be reused for other scenarios. In addition, the proposed planner is made robust by guaranteeing smooth trajectories and by handling the noisy perception system of the ego-car. The PID controller is used for tracking the waypoints, which ensures smooth trajectories and reduces jerk. The problem of incomplete observations is handled by using a Long-Short-Term-Memory (LSTM) layer in the network. Results from the high-fidelity CARLA simulator indicate that the proposed method reduces convergence time, generates smoother trajectories, and is able to handle dynamic surroundings and noisy observations., 7 pages, 5 figures

Unmanned vehicle navigation concerns estimating attitude, position, and linear velocity of the vehicle the six degrees of freedom (6 DoF). It has been known that the true navigation dynamics are highly nonlinear modeled on the Lie Group of $\mathbb{SE}_{2}(3)$. In this paper, a nonlinear filter for inertial navigation is proposed. The filter ensures systematic convergence of the error components starting from almost any initial condition. Also, the errors converge asymptotically to the origin. Experimental results validates the robustness of the proposed filter., 24th International Intelligent Transportation Systems Conference

An algorithm based on event-triggered global adaptive dynamic programming is proposed for optimal control of multi-agent system consistency. The algorithm converts the multi-agent problem of consistency control to solving the Hamilton-Jacobi-Bellman equation of the optimal solution, and a method of the sum of squares iteration is used to calculate the optimal control strategy. The process of approximating the optimal control strategy and cost function by neural network training through a large number of basic functions is eliminated, to reduce the computation complexity of the system. By introducing event trigger conditions, the update times of the controller and actuator in the multi-agent system are reduced, and the frequency of information transmission between adjacent agents is also reduced. Using the optimal control theory and Lyapunov stability theory, the convergence of the system in a period of time after the event is triggered is analyzed. Finally, the effectiveness of the theoretical results is verified by MATLAB simulation.

The present paper deals with the issue of measurement delays which constitute a very prominent problem faced by most control systems. The system considered is that of a bioreactor used for the biological treatment of wastewater. The recording of the output substrate concentration of such systems usually involves a delay which results from the dependency of the flow of the liquid on its consistency. A PI observer-based control is applied and its performance is analysed in terms of the maximum delay for which the system is stable and converges as well as in terms of the convergence times. Based on these parameters, the results are compared to those obtained by a feedback linearising observer-based system. This comparative analysis showed that the PI controller performed better than the feedback controller, hence confirming its efficiency in biomass regulation in the presence of delayed measurements.