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This study numerically investigates mixed convective cooling in a two‐dimensional horizontal channel containing periodically heated blocks by applying proportional (P), proportional‐integral (PI), and proportional‐integral‐derivative (PID) controllers. Three different controller configurations regulate the amount of cold air entering the chamber. The air's non‐dimensional temperature is continuously monitored at the set point to compare the controllers' performance, and the percentage of overshoot and the steady‐state error are analysed. The investigated chamber comprises one inlet and two exit ports, a temperature sensor, and two heated blocks that are isotherm heat sources. The Galerkin finite element approach computationally solves the equations of continuity, momentum, and energy to analyse the thermo‐fluid phenomena occurring within the chamber. Parametric simulation is carried for different values of the proportional gain (Kp = 0.005, 0.010, 0.050 m s−1 K−1), the integral gain (Ki = 0.05, 0.10, 0.15 m s−2 K−1), the derivative gain (Kd = 10−5, 10−4, 10−3 m K−1) to achieve a consistent and expeditious response. Variations of Reynolds, Richardson, and mean Nusselt numbers with time are plotted to compare the system's performance. The investigation indicates that the PI controller produces a comparable level of performance with the PID controller, reducing the necessity to add a derivative controller. [ABSTRACT FROM AUTHOR]

This paper presents a novel input channel gain adaptive active disturbance rejection control (ICGA‐ADRC) framework for the nonlinear control system with large load variation. This paper first introduces the design of ICGA‐ADRC and proves its stability through the rigorous Lyapunov approach. Subsequently, the proposed controller is simulated by subjecting it to a nonlinear mass‐spring‐damping system characterized by significant load variations. Furthermore, the position‐tracking performance of the controller is tested experimentally by using a motor‐driven pendulum system with various loads. Finally, the results of both simulations and experiments show that the ICGA‐ADRC is capable of compensating for system model uncertainty and external perturbation while accurately estimating the system input channel gain in real‐time. The innovation of this paper is that the robustness of active disturbance rejection control (ADRC) to the system load variation is significantly strengthened by integrating robust adaptive finite‐time parameter estimation method into the conventional ADRC control architecture. [ABSTRACT FROM AUTHOR]

This article concentrates on the challenging adaptive tracking problem of multi‐input and multi‐output (MIMO) nonlinear systems with unknown nonlinear dynamics, for which a novel optimization‐based data‐driven adaptive control (ODDAC) equipped with an extended dynamic linearization method is developed. Through considering MIMO nonlinear systems in the fully‐actuated case and over‐actuated case separately, the ODDAC is proposed consisting of a parameter updating algorithm and an adaptive control law. A new design of parameter updating algorithm is presented such that the estimation can be guaranteed to be bounded by a strict contraction process. By leveraging properties of the nonnegative matrix, a grouping‐based contraction mapping (GCM) analysis method is proposed for the convergence of tracking error. Notably, the GCM does not require the contraction mapping condition to hold at all time instants. The proposed ODDAC is data‐based, avoiding reliance on model information, and its validity is verified through simulations. [ABSTRACT FROM AUTHOR]

For controlling nonlinear processes represented by state‐space models, a state observer is needed to estimate the states from the trajectories of measured variables. While model‐based observer synthesis is traditionally challenging due to the difficulty of solving pertinent partial differential equations, this article proposes an efficient model‐free, data‐driven approach for state observation, which is suitable for data‐driven nonlinear control without accurate nonlinear models. Specifically, by using a Chen–Fliess series representation of the observer dynamics, state observation is endowed with an online least squares regression formulation that can be solved by gradient flow with performance guarantees. When the target state trajectories for regression are unavailable, by exploiting the Kazantzis–Kravaris/Luenberger observer structure, state observation is reduced to a dimensionality reduction problem amenable to an online implementation of kernel principal component analysis. The proposed approach is demonstrated by a limit cycle dynamics and a chaotic system. [ABSTRACT FROM AUTHOR]

This article discusses the challenges and research priorities related to traffic mixed with connected and automated vehicles (CAVs). The article highlights the need to understand how different types of vehicles operate and interact in heterogeneous traffic flow, as well as the evolution mechanism of mixed traffic. The special issue of the journal includes papers on driving behavior modeling, optimization, and traffic flow modeling. The papers cover topics such as simulation platforms for hybrid transportation systems, trajectory reconstruction methods, lane management strategies, fuel consumption modeling, deadlock detection and recovery methods, crash analysis, traffic flow prediction, and gantry positioning methods. The authors conclude that there is still room for future research in areas such as the relationship between the cyber and physical network of transportation systems, driving characteristics of regular vehicles, and macro-level traffic prediction and control. [Extracted from the article]

Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed. [ABSTRACT FROM AUTHOR]

In this study, the adaptive backstepping control of a class of multi‐input multi‐output nonlinear systems with immeasurable states, structural uncertainties and periodic perturbations is researched using neural network (NN) based nonlinear observer. Firstly, a series of harmonic components obtained from Fourier series expansion (FSE) are employed to estimate the unknown periodic perturbations. Then treating the estimated perturbations as inputs, a radial basis function‐based neural network (RBFNN) is constructed as a component of the observer, and the observer is proved to be uniformly ultimately bounded (UUB) in estimating the immeasurable system states with the negative‐gradient adaptive laws of NN parameters. Subsequently, the adaptive backstepping control is designed to track reference signals at the outputs of system based on the FSE‐RBFNN observer, and the stability of the closed‐loop system is proved on a finite set of system states. Finally, the proposed methods are applied to a triangular tethered satellite formation model to test the stability of the observer and control, and the effect of FSE in estimating perturbations is comparatively tested as well. [ABSTRACT FROM AUTHOR]

This work develops a transfer learning (TL) framework for modeling and predictive control of nonlinear systems using recurrent neural networks (RNNs) with the knowledge obtained in modeling one process transferred to another. Specifically, transfer learning uses a pretrained model developed based on a source domain as the starting point, and adapts the model to a target process with similar configurations. The generalization error for TL‐based RNN (TL‐RNN) is first derived to demonstrate the generalization capability on the target process. The theoretical error bound that depends on model capacity and the discrepancy between source and target domains is then utilized to guide the development of pretrained models for improved model transferability. Subsequently, the TL‐RNN model is utilized as the prediction model in model predictive controller (MPC) for the target process. Finally, the simulation study of chemical reactors via Aspen Plus Dynamics is used to demonstrate the benefits of transfer learning. [ABSTRACT FROM AUTHOR]

The shaking table has been used extensively in the structure test field to verify the structure's performance against various vibrations, for example earthquakes. In order to replicate the vibrations, which are measured by the acceleration signal, the model of the shaking table should be thoroughly constructed to design the controller. However, parametric uncertainty and strong nonlinearity, such as the nonlinear friction, make it an obstacle to obtaining an accurate model. A neural network‐based controller, specifically a long short‐term memory neural network‐based neural network controller, is designed in this paper to address this issue. The nonlinear systems are estimated by the neural network's universal approximation characteristics, and a long short‐term memory neural network is utilized to optimize the time‐series‐related errors. Furthermore, a robust sliding mode controller is utilized to compensate for the residual error of the neural network and other uncertainties. The semi‐global asymptotic stability of the controller is proved by Lyapunov analysis. Comparative experimental results indicate the superiority of the proposed controller. [ABSTRACT FROM AUTHOR]

In this article, committed to extending the robust integral of the sign of the error (RISE) feedback control to the working condition of output feedback, a novel output feedback controller with a continuously bounded control input which combines the adaptive control and integral robust feedback will be proposed for trajectory tracking of a family of nonlinear systems subject to modeling uncertainties. A novel adaptive state observer (ASO) with disturbance rejection performance is creatively constructed to derive real‐time estimation of the unmeasured state signals. Moreover, a projection‐type adaption law is integrated to handle parameter uncertainties and an integral robust term is employed to deal with external disturbances. It is shown that asymptotic estimation performance and meanwhile asymptotic tracking result can eventually be derived. Simulation validations are implemented to demonstrate the high tracking performance of the presented controller. Notably, the synthesized control algorithm can be readily extended to the Euler–Lagrange systems. Typically, it can be extended to practical electromechanical equipment such as three‐dimensional vector forming robots to improve the real‐time forming accuracy. [ABSTRACT FROM AUTHOR]

This work investigates simultaneous prescribed performance tracking control and mismatched disturbance rejection problems for a class of strict‐feedback nonlinear systems. A novel control scheme combining prescribed performance control, disturbance observer technique, and backstepping method is proposed. The disturbance estimations are introduced into the design of virtual control law design in each step to compensate the mismatched disturbances. To further improve the control performance, a prescribed performance function characterizing the error convergence rate, maximum overshoot, and steady‐state error is used to construct the composite controller. The proposed controller guarantees transient and steady‐state performance specifications of tracking error and provides much better disturbance attenuation ability simultaneously. Rigorous stability analysis for the closed‐loop system is established by direct Lyapunov function method. It is shown that all the states in the resulting closed‐loop system are stable, and the tracking error evolves within the prescribed performance boundaries and asymptotically converges to zero even in the presence of mismatched external disturbances. Finally, theoretical results are illustrated and demonstrated by two simulation examples. [ABSTRACT FROM AUTHOR]

A new method is presented for evaluating the performance of a nonlinear control allocation system within a linear control loop. To that end, a worst‐case gain analysis problem is formulated that can be readily solved by means of well‐established methods from robustness analysis using integral quadratic constraints (IQCs). It exploits the fact that control allocation systems are in general memoryless mappings that can be bounded by IQCs. A data‐driven approach is used to find an optimal bound of the input/output mapping of the control allocation. Additionally, an iterative procedure based on local IQCs is introduced to determine meaningful sampling limits for less conservative yet accurate results. The effectiveness of the proposed data‐driven performance analysis is shown at the example of an actively controlled flexible wing in a wind tunnel. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of asymptotic output tracking control with prescribed transient performance for a class of nonlinear systems in the presence of unknown dynamics. By fusing the funnel control approach and the tool of barrier Lyapunov function, a smooth adaptive tracking control strategy is developed in a recursive manner. In the proposed design, a new barrier Lyapunov function is adopted to eliminate the effects of unknown nonlinear dynamics, ensuring the prescribed transient behavior of the output tracking. The distinctive characteristic of the proposed method is that the designed control algorithm can guarantee not only prescribed output tracking performance but also global asymptotic stability without incorporating any prior knowledge of nonlinear dynamics or even corresponding bounding functions. Three illustrative examples are provided to testify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an observer‐based controller is designed to control a class of discrete‐time switched nonlinear systems with state delay and external disturbances. Based on the passivity concept, the average dwell time method, and auxiliary matrices, less‐conservative sufficient conditions are derived to make the closed‐loop system finite‐time bounded and passive in the presence of disturbances. In contrast to existing papers in this field, this paper deals with discrete‐time nonlinear switched systems and especially with an observer‐based controller. The developed conditions are presented in the form of linear matrix inequalities. Finally, computer simulations (numerical and practical examples) are provided to illustrate the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

The design of optimal fault‐tolerant control with actuator and sensor faults is investigated. The system dynamics and faults are considered as a class of unknown nonlinear discrete‐time systems when the data‐driven equivalent model is formulated by a multi‐input fuzzy rule emulated network (MiFREN). The multi‐gradient learning law is developed with the proposed fault‐detection algorithm to enchant the performance of MiFREN. By employing only pieces of information from the equivalent model, the proposed controller is designed and the closed‐loop performance is analyzed through the rigorous theorem. Simulation systems and comparison results are provided to validate the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a new robust control strategy is proposed to regulate the output voltage of a quasi‐resonant converter. The proposed approach completely eliminates the switching losses and the electromagnetic interferences due to high current changing rates. The circuit structure is simple and affordable and does not have many auxiliary switches. The regulated output voltage of a buck converter must be robust to input voltage variations. Moreover, the controller must be robust to the circuit parameter uncertainties. In this paper, a new control strategy with nonlinear structure is used with the advantage of robustness, and turn‐on and turn‐off zero current switching. The robustness of the converter to the input voltage variations and the parameter uncertainties is investigated. The new proposed control strategy can be implemented using different controller types. Therefore, a comparison between the proposed nonlinear controller and a conventional PI controller, which is most widely used in practice, is performed. The simulation results show the efficiency and robustness of the proposed method. Finally, the simulation results are verified by the experimental tests. [ABSTRACT FROM AUTHOR]

Aiming at reducing the influence caused by data injection attacks, an asynchronous event‐triggered robust H‐infinity control (AERHC) is proposed for the stability of nonlinear ICPSs suffering from data injection attacks. A robust H‐infinity memory dynamic feedback control technique is designed to guarantee asymptotic stability of the closed‐loop system under a Lyapunov functional and the robust H∞ stability criterion. Furthermore, a memory dynamic feedback controller which has five controller gains different from each other for mitigating the conservative of nonlinear ICPSs and for resisting data injection attacks in ICPSs. Meanwhile, an asynchronous event‐triggered strategy is adopted to reduce the data communication frequency among nonlinear ICPSs components, and it effectively handle the influence of coupling problem between the measurement channel and the control channel. Finally, simulation tests are conducted by using MATLAB to control the robustness of the motor angle position of electromechanical system by using AERHC, which verify that the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the fixed‐time stabilization problem for a class of high‐order nonlinear systems with monotone degrees and output constraints. Firstly, a new fixed‐time stability criterion is established, which is less conservative in the explicit and delicate selection of Lyapunov functions. Then, by choosing a novel tan‐type barrier Lyapunov function (BLF) and using the generalized adding a power integrator (AAPI) technique, a fixed‐time stabilization controller is explicitly constructed while achieving the requirement of state constraints. The novelty of the proposed control strategy can tackle simultaneously the fixed‐time stabilization of the involved systems with and without output constraints, without changing the controller structure. A simulation example is given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, the distributed finite‐time control problem for the class of systems interconnected over an undirected graph is considered. First, the concepts of well‐posedness, finite‐time boundedness, and contractiveness for the considered systems are given. Then, a sufficient condition is developed to ensure that the plant is well‐posed, finite‐time bounded, and contractive, where the results are stated by terms of linear matrix inequalities (LMIs). For the control synthesis, we construct a distributed dynamic output‐feedback controller inheriting the structure of the plant such that the resulting closed‐loop system is finite‐time bounded and contractive. Since the analysis condition of the plant used for the closed‐loop system is presented by some nonlinear inequalities in given finite‐time interval, we further derive a sufficient condition in terms of LMIs with some fixed parameter for the existence of an appropriate output‐feedback controller such that the closed‐loop system is contractive. Finally, numerical simulations are provided to show the validity of the proposed results. [ABSTRACT FROM AUTHOR]

This paper presents a robust hybrid fault‐tolerant optimal predictive control (HFTPC), for an industrial robot arm under hybrid (electric and pneumatic) actuator faults and/or varying time‐delays. Based on the error dynamics, estimated states, and a predictive controller, a new state feedback control law is proposed and implemented for the reformulation of the optimal control problem of a nonlinear faulty hybrid actuator system based on predictive control via linear matrix inequalities (LMIs). First, a robust MPC scheme is performed in which the future control sequence is used to compensate the varying time‐delays. Then, a robust stable hybrid fault tolerant predivtive control is implemented to handle actuators faults to effect robust trajectory tracking. In fact, the stability of hybrid systems based on the proposed control scheme is a very sensitive criterion. Therefore, stability conditions are required for controlling the industrial arm under faulty hybrid (electric and pneumatic) actuator, based on the Lyapunov‐Krasovskii (L‐K) theory, less conservative stable conditions in terms of LMIs are given and used to ensure the asymptotically robust stability of closed‐loop constrained system that dependent delay‐range. To highlight the robustness and effectiveness of the proposed approach, a simulation study of an industrial robot arm example is proved, where the results showed the prompt and the accuracy of the proposed scheme. [ABSTRACT FROM AUTHOR]

Research is increasingly shifting from the design and control of single robots toward collaborative methods for multirobot systems. Particularly interesting are heterogeneous systems since the combination of different capabilities can tackle tasks that are difficult, if not impossible, to perform with a single robot. In this article we propose a scheme for the control of a novel heterogeneous multirobot system designed for bricklaying where the use of a single robot is not feasible due to the weight of the payloads to be managed. The proposed solution is based on a crane which cooperates with a lightweight rigid robot. The correct cooperation between the two robotic subunits poses a series of control challenges that must be studied in the context of cooperative manipulation of an object. In this article, we first derive the mathematical model of this novel robotic system during the positioning of the block. Then a control law is proposed. The proposed law is based on an ad hoc inverse dynamic control which takes advantage of the overactuation of the resulting connected system to unload the robotic arm from the weight of the block. A physical CAD‐based simulator of the system is used to show the effectiveness of the proposed control law and to show that the proposed architecture allows to position precisely large and heavy blocks while using a relatively small rigid robotic manipulator. [ABSTRACT FROM AUTHOR]

In this paper, a subdivision strategy for the adjoining cell mapping method to solve the global optimal control problems for multi‐input‐multi‐output systems is proposed. The subdivision strategy constructs the discrete optimal control table first by a rough discretization of the state space, which can identify uncontrollable regions. Then the optimal feedback control from the selected initial state is instituted. The rough cells where the feedback control trajectory crosses are subdivided into smaller cells and the finer discrete optimal control table is obtained after the second search. The further discretization of the target cell and control inputs can further enhance the terminate control precision. The calculation can also take the advantage of GPU parallel technology that will greatly enhance the computation efficiency. Two examples illustrate the feasibility and validity of the subdivision strategy to solve optimal control problems for multi‐input‐multi‐output systems. The proposed method offers an efficient alternative numerical way to obtain the approximate optimal control solution with global optimum for nonlinear multi‐input‐multi‐output systems. [ABSTRACT FROM AUTHOR]

In this paper, a sensorless speed tracking control scheme is proposed for Permanent Magnet Synchronous Motor (PMSM) with unmodeled electromotive forces. Specifically, one observer is developed to estimate the angular velocity of PMSM and the estimated value is injected into a novel nonlinear feedback controller. The unmodeled electromotive forces are not estimated. The influences of the unmodeled electromotive forces on the speed tracking performance are overcome by constructing corresponding nonlinear damping terms in the controller. Under certain assumptions, the rigorous proof of the stability of the closed‐loop system is completed with Lyapunov theory. All the dynamic signals are bounded. Furthermore, the estimation error and the tracking error can converge to an arbitrarily small value by choosing the control parameters appropriately large. Finally, the simulation results of constant and time‐vary speed tracking illustrated the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The global stabilization problem for a more extensive class of switched nonlinear systems is studied, in which control vector fields are allowed to vanish at some point, even for all the subsystems at the same time. The main work of this article is to design a control vector fields‐dependent switching law and a bounded switching controller to stabilize the switched nonlinear systems. First, a sufficient condition ensuring the global stabilization of such a class of switched nonlinear systems is developed by the dual design of controllers and a switching law. In some special cases, the designed switching law can rule out Zeno behavior. Then, as a verification of the proposed control design method, the global stabilization of switched strict‐feedback nonlinear systems with gk(x) vanishing at some points is obtained by the codesign of the control vector fields‐dependent switching law and the bounded switching controller. Finally, a switched RCL circuit system example is also provided to demonstrate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper presents a non‐linear controller based in synergetic controller (SC) that used for Wind and PV systems to reduce the chattering phenomenon, tracks rapidly and maintain the Maximum Power Point (MPP) under different environmental and load conditions. To approve the effectiveness of the proposed approach, comparative analysis of MPPT controller built using Perturb and observation for PV system and torque vector for Wind power system, with MPPT controller implemented using synergetic control in the both renewable sources in the hybrid system. The results show that SC MPPT is robust and maintain the power balancing with meeting multiple objectives of standalone hybrid systems such as rapidity of response, robustness under the external variations and no chattering problem as compared with the other controllers. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed adaptive dynamic programming scheme to investigate the optimal tracking control problem for finite‐horizon non‐linear interconnected systems with constraint inputs under aperiodic sampling. A N‐player nonzero‐sum differential game system is constructed with the presented non‐linear interconnected system and the tracking error system by introducing the augment vectors. To address the problems of constrained‐input and finite‐horizon control, a non‐quadratic utility function and a finite‐horizon cost function are utilized which will arise in the time‐varying Hamilton–Jacobi (HJ) equation. Then, a periodic event‐triggered scheme is designed to realize aperiodic sampling, where the consumption of communication resources is reduced and the Zeno behavior is avoided. Under the designed periodic event‐triggered scheme, the time‐varying HJ equation is almost impossible to get an analytical solution due to its hybrid properties and non‐linearity. Therefore, the critic neural networks are used to estimate the optimal solution of the HJ equation, and the weight update law is constructed to guarantee the uniformly ultimate bounded of approximated errors. Further, the hybrid nonzero‐sum differential game is confirmed to be uniformly ultimate bounded by using the Lyapunov theory. Finally, the obtained distributed PET control strategy is successfully applied to dispose the missile‐target intercepter problem. [ABSTRACT FROM AUTHOR]

In this article, we study the anti‐swing control of the Pendubot which is a 2‐link planar robot with a single actuator driving the first joint, whose control objective is to stabilize the Pendubot to the downward equilibrium point (DEP) with the two links in the downward position for all its initial states with the exception of a set of Lebesgue measure zero. To achieve this control objective, with respect to the angle of the first joint, we present a proportional‐derivative (PD) controller using a damper and a linear spring with positive stiffness, and a sinusoidal‐derivative (SD) controller using a damper and a nonlinear spring (force being sinusoidal function of the displacement) allowing even negative stiffness. We prove that the control objective is achieved if some conditions on the stiffness of linear or nonlinear spring are satisfied. We present analytical solutions to the optimal design of the derivative gain and the stiffness which minimizes the real parts of the dominant poles of the linearized model of the corresponding closed‐loop systems around the DEP for all Pendubots in terms of their five physical parameters without any constraint. Our discussion shows that the SD controller is superior to the PD controller. We provide simulation results for a Pendubot to show that the presented SD controller can stabilize the Pendubot to the DEP faster than the presented PD controller. [ABSTRACT FROM AUTHOR]

Differential‐algebraic equations (DAEs) can model constrained dynamical systems and processes from practical engineering. Therefore, research on nonlinear optimal control problems of DAEs is of theoretical significance for optimal control of constrained systems, which can generate reference trajectories and control inputs for online control strategies. In terms of the numerical solution of this type of problem, research on indirect numerical methods is still insufficient and less research focuses on symplectic‐preserving methods. In this article, a symplectic indirect approach is proposed for optimal control problems subject to index‐1 DAEs. Necessary conditions of the optimal control problem constitute a Hamiltonian boundary value problem (HBVP) and there exists a symplectic structure in the Hamiltonian system. In the proposed approach, based on specified properties of generating functions, discrete equations can preserve the symplectic structure of the Hamiltonian system. In the iterative solution, the Jacobian matrices of the discrete equations are sparse and symmetric, which are very significant to save memory and improve efficiency in practical computation. In numerical examples, the proposed approach can provide highly accurate state variables and control inputs with fewer iterations. More accurate cost functional can be obtained. Problems from the chemistry process also can be solved effectively, it verifies the problem‐solving ability of the proposed approach. [ABSTRACT FROM AUTHOR]

To balance the performance and implementability of classical predefined performance control algorithm when its hard constraint is violated, this article investigates a switching‐based prescribed‐performance‐like control (PPLC) approach for nonlinear teleoperation systems. To achieve the objective, a switching mechanism is introduced into the classical controller design and based on this, a nonlinear proportional plus auxiliary compensation controller is designed, where a switched filter control technique is employed. By modeling the resulting closed‐loop system as a special switched delayed system and using the piecewise Lyapunov‐Krasovskii functional method, the state‐independent input‐to‐output stabilty of closed‐loop system is established, where a feasible range of the "initial" values of piecewise continuous performance function is given to achieve a tradeoff between the closed‐loop stability and overshoot. It is shown that the developed switching‐based PPLC approach obtains better performance than the classical predefined performance control algorithms. Simulations on a pair of 3‐DOF planar arms illustrate the approach. [ABSTRACT FROM AUTHOR]

This paper applies the state derivative feedback method and proportional plus derivative state feedback method to design the fuzzy controllers for the nonlinear singular systems with external noises. The Takagi‐Sugeno fuzzy modeling technology is employed in this paper to represent the nonlinear singular systems with external noise. Considering the singular Takagi‐Sugeno fuzzy model, the passivity constraint is chosen to treat the external noises for the controlled systems. By using the parallel distributed compensation method, the fuzzy controller is designed to satisfy the Lyapunov stability conditions and passivity constraints simultaneously. These sufficient conditions can be effectively solved in terms of linear matrix inequalities, which can be solved via the convex optimization technique. At last, some numerical examples are provided to verify the applicability and effectivity of the proposed fuzzy controller design methods. [ABSTRACT FROM AUTHOR]

This article presents a novel framework for the output regulation of rational nonlinear systems subject to input saturation, where the controller structure is composed by an internal model generator in series with an output feedback stabilizing stage. In order to address the effects of control saturation, we propose the use of an anti‐windup compensation loop into both internal model and stabilizing control stages. Given prior knowledge of the system zero‐error steady‐state condition and a proper internal model, we cast the regulation error dynamics in a differential‐algebraic form, which leads to matrix inequalities conditions that ensure closed‐loop stability and exponential transient performance. Optimization problems are then proposed to simultaneously compute the stabilizing controller and the anti‐windup gains such that a domain of attraction estimate is maximized. [ABSTRACT FROM AUTHOR]

This paper studies resilient coordinated control over networks with hybrid dynamics and malicious agents. In a hybrid multi‐agent system, continuous‐time and discrete‐time agents concurrently exist and communicate through local interaction. We introduce the notion of heterogeneous robustness to capture the topological structure and facilitate convergence analysis of hybrid agents over multiple subnetworks, where the exact number and identities of malicious agents are not known. A hybrid resilient strategy is first designed to ensure group consensus of the heterogeneously robust network admitting completely distributed implementation. We then develop a scaled consensus protocol which allows different clusters within each subnetwork, providing further flexibility over the resilient control tasks. Finally, some numerical examples are worked out to illustrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

Practical control problems are always subject to plant state and/or input constraints, which make designing an effective controller a challenging task. This paper introduces a novel virtual control approach to handling the presence of hard constraints in control systems by utilizing virtual mechanisms in the form of nonlinear springs and dampers. The augmented virtual mechanisms are to assist in better shaping the closed‐loop responses, especially when operating near the constrained boundary. A linear quadratic regulator based model predictive control method is utilized to develop stabilizing controllers that not only achieve desired system performance, but also meet the imposed hard constraints. The basic idea is to dramatically increase control penalty by way of tuning the spring and damper effect when the constrained state/input response is close to its hard constraint. The proposed method is applied to a balancing ball problem to demonstrate its applicability and effectiveness, and the simulation results validate the proposed concept. [ABSTRACT FROM AUTHOR]

The negative aspects of overusing fossil fuels for producing electricity attract the attention of engineers society to use renewable energies. Power electronic converters play an important role in the transmission and usage of renewable energy in the industry. Boost converters are a major part of devices in which convert renewable energies into usable energy for various industries or appliances. In this article, to control a boost converter with parametric uncertainty, a new robust‐optimal controller is designed. The proposed control method is designed based on merging two conventional control theory: the robust control theory and linear quadratic regulator technique. This method consists of two nested loops. The inner loop composed of a state feedback controller which works as active damping to improve the transient state. The outer loop consists of a PI controller to track the desired voltage and reject disturbance. The coefficients of both controllers are computed by a robust‐optimal control technique. To evaluate the proposed control method, a well‐tuned single‐loop PI controller and a quantitative feedback theory based PID controller are compared with the proposed method. As shown by simulations, the proposed method has a more reliable performance in dealing with parameters uncertainties such as load changes and input voltage drops. [ABSTRACT FROM AUTHOR]

Summary: This article is concerned with the design of the observer and controller to perform fault estimation (FE) and fault tolerant control for the discrete‐time nonlinear stochastic systems with multiple time‐varying state delays and intermittent sensor and actuator faults. In comparison with the present research, the method addressed in this article is more flexible and feasible. A novel adaptive descriptor observer is developed to obtain the error dynamics and then realize n‐step FE. Then, an active observer‐based controller is constructed to stabilize the closed‐loop system. Moreover, an augmented closed‐loop system is acquired and a series of delay‐dependent sufficient conditions are collected by the delay‐dependent Lyapunov function utilizing linear matrix inequalities. The method is less conservative compared with the ones of the existence. At last, a simulation example is presented to demonstrate the advantages and effectiveness of the approach proposed in the article. [ABSTRACT FROM AUTHOR]

Summary: This article is concerned with the adaptive output‐feedback control of switched nonstrict feedback nonlinear systems. By introducing a novel error surface, an adaptive control strategy is proposed for the general case where the nonlinear functions and the control gain functions are unknown, and the states are unmeasurable. The considered switched nonlinear system contains unknown actuator failures, which are modeled as both loss of effectiveness and lock‐in‐place. In order to improve the transient performance in the presence of unknown actuator failures, the prescribed performance approach is used. The "explosion of complexity" problem is avoided through using low‐pass filters. The stability of the closed‐loop system under arbitrary switching is shown using Lyapunov stability theory, based on which, the tracking error is shown to converge to a small residual set with the prescribed performance bounds. The advantages of the proposed technique are verified through simulations of two numerical and practical examples. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the stability analysis and control design of a class of nonlinear positive Markovian jump systems with randomly occurring actuator faults and saturation. It is assumed that the actuator faults of each subsystem are varying and governed by a Markovian process. The nonlinear term is located in a sector. First, sufficient conditions for stochastic stability of the underlying systems are established using a stochastic copositive Lyapunov function. Then, a family of reliable L1‐gain controller is proposed for nonlinear positive Markovian jump systems with actuator faults and saturation in terms of a matrix decomposition technique. Under the designed controllers, the closed‐loop systems are positive and stochastically stable with an L1‐gain performance. An optimization method is presented to estimate the maximum domain of attraction. Furthermore, the obtained results are developed for general Markovian jump systems. Finally, numerical examples are given to illustrate the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the problem of using sampled‐data state/output feedback to semiglobally stabilize a class of uncertain nonlinear systems whose linearization around the origin is neither controllable nor observable. For any arbitrarily large bound of initial states, by employing homogeneous domination approach and a homogeneous version of Gronwall‐Bellman inequality, a sampled‐data state feedback controller with appropriate sampling period and scaling gain is constructed to semiglobally stabilize the system. In the case when not all states are available, a reduced‐order sampled‐data observer is constructed to provide estimates for the control law, which can guarantee semiglobal stability of the closed‐loop system with carefully selected sampling period and scaling gain. [ABSTRACT FROM AUTHOR]

A filtered adaptive constrained sampled‐data controller for uncertain multivariable nonlinear systems in the presence of various constraints is synthesized in this paper. A piecewise constant adaptive law drives that estimation error dynamics to zero at each sampling time instant yields adaptive parameters. The filtered control scheme consists of two components. Based on an estimation/cancellation strategy, a disturbance rejection control law is designed to compensate the nonlinear uncertainties within the bandwidth of low‐pass filters, whereas a constraint violation avoidance control law is designed to solve an online constrained optimization problem. Although a reduced sampling time helps to minimize the estimation error caused by the neglect of unknowns, the resulting aggressive signals put more restrictions on the control law. Greater sacrifice of tracking performance is required to satisfy the constraints. The constraints violation avoidance control law is in favor of a larger sampling time. Sufficient conditions are given to guarantee the stability of the closed‐loop system with the sampled‐data controller, where the input/output signals are held constant over the sampling period. Numerical examples are provided to validate the theoretical results, comparisons between the constrained sampled‐data controller and unconstrained adaptive controller with the implementation of different sampling times are carried out. [ABSTRACT FROM AUTHOR]

A boiler‐turbine unit is a primary module for coal‐fired power plants, and an effective automatic control system is needed for the boiler‐turbine unit to track the load changes with the drum water level kept within an acceptable range. The aim of this paper is to develop a nonlinear tracking controller for the Bell‐Åström boiler‐turbine unit. A Takagi‐Sugeno fuzzy control system is introduced for the nonlinear modeling of the Bell‐Åström boiler‐turbine unit. Based on the Takagi‐Sugeno fuzzy models, a nonlinear tracking controller is developed, and the proposed control law is comprised of a state‐feedforward term and a state‐feedback term. The stability of the closed‐loop control system is analyzed on the basis of Lyapunov stability theory via the linear matrix inequality approach and Schur complement. Moreover, model uncertainties are also considered, and it is proved that with the proposed control law the tracking error converges to zero. To assess the performance of the proposed nonlinear state‐feedback state‐feedforward control strategy, a nonlinear model predictive control strategy and a linear strategy are presented as comparisons. The effectiveness and the advantages of the proposed nonlinear state‐feedback state‐feedforward control strategy are demonstrated by simulations. [ABSTRACT FROM AUTHOR]

In this work, we consider the problem of controlling a single‐phase on‐board battery electric vehicle (BEV) charger with vehicle‐to‐grid (V2G) technology. The BEV charger consists of a bidirectional ac‐dc power converter connected to the single‐phase power grid, followed by a bidirectional dc‐dc power converter interfacing an EV battery pack. The main control objectives are fourfold: (i) Unitary Power Factor (UPF) in grid‐side; (ii) tight dc‐bus voltage regulation; (iii) safety battery charge and battery discharge during the grid‐to‐vehicle (G2V) mode and V2G mode, respectively; and (iv) asymptotic stability of the closed loop system. After an accurate system modelling, a nonlinear controller is designed using a backstepping design technique. The point is that the battery inner voltage is not accessible to measurement. Therefore, a nonlinear observer is invoked in order to estimate all non‐measured variables making the solution cheaper and noiseless. It is shown using a formal analysis and numerical simulations, that the proposed output feedback controller (combining a nonlinear controller and a nonlinear observer) meets all control objectives. [ABSTRACT FROM AUTHOR]

Recently, there have been a series of results regarding two time optimal control problems for a class of linear and nonlinear systems ‐ one is to keep the system states within certain bound for the longest time during feedback disruption and the other is to derive the system states to near the origin as fast as possible after feedback recovery, both under bounded control inputs. These are called maximal and minimal time optimal control problems, respectively. In the existing results, a bang‐bang controller has been commonly suggested as the actual implementation of the optimal controller. In this paper, we suggest a modified version of the bang‐bang controller which can also serve as an approximate optimal controller. Our proposed controller provides the (near) optimal performance with (i) possible reduction of a number of switchings; (ii) possible reduction of control input magnitude. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel decentralized filtering adaptive constrained tracking control framework for uncertain interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, a piecewise constant adaptive law will generate total uncertainty estimates by solving the error dynamics between the host system and decentralized state predictor with the neglection of unknowns, whereas a decentralized filtering control law is designed to compensate both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. In the control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low‐pass filters, while the trade‐off between tracking and constraints violation avoidance is formulated as a numerical constrained optimization problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. The uniform performance bounds are derived for the system states and control inputs as compared to the corresponding signals of a bounded closed‐loop reference system, which assumes partial cancelation of uncertainties within the bandwidth of the control signal. Compared with model predictive control (MPC) and unconstrained controller, the proposed control architecture is capable of solving the tracking control problems for interconnected nonlinear systems subject to constraints and uncertainties. [ABSTRACT FROM AUTHOR]

In this study, the robust fuzzy observer-based control scheme for the dynamic positioning of marine vessels is presented. The proposed controller assumes that the environmental disturbances are unknown but bounded with known upper limit. First, the authors apply the Takagi–Sugeno fuzzy model to approximate the ship model. Using formulated fuzzy model, the robust fuzzy observer is developed to approximate the unknown states. Then, by employing optimal $H^\infty $H∞ disturbance attenuation technique, the effects of the environmental disturbances can be attenuated to least value. Finally, the linear matrix inequality technique is hired for solving the optimal robust problem. Model ship is used for simulation, and results are given to prove the validity of the presented controller. [ABSTRACT FROM AUTHOR]

This study mainly investigates the path planning and collision avoidance problem of autonomous agricultural machineries in the complex rural road. First, the grid model is used to build the global map of machineries and the particle swarm optimisation algorithm is used to search the global path. A non-linear dynamic inertia weight is introduced to avoid the local lock-up problem of traditional particle swarm optimisation. Then the artificial potential energy method for addressing the local collision problem is used, which is the sum of the attraction of the target point and the repulsion of the obstacles and the road boundary force. To solve the problem of the slip and roll of autonomous machinery caused by complex roads, a machinery dynamic model considering road curvature and topographic inclination is established, and the model predictive control algorithm is used to track the planned path. The simulation results show that proposed method can make the autonomous agricultural machinery driving safely and smoothly. [ABSTRACT FROM AUTHOR]

Summary: The paper proposes a novel control design for nonlinear systems with multiple uncertainties and nonlinear measurement. The output linearization is utilized to handle the nonlinearities in system dynamics and measurement. Firstly, the integrator chain for nonlinear systems with multiple uncertainties is analyzed. Based on the fundamental integrator chain form, the equivalent total effect of multiple uncertainties is summarized as total disturbance. By timely estimating and compensating for the total disturbance, an active disturbance rejection control design to handle both multiple uncertainties and nonlinear measurement is proposed. Moreover, the transient performance of the corresponding closed‐loop system is rigorously studied, which theoretically reveals the high consistence of the tracking performance despite various multiple uncertainties. [ABSTRACT FROM AUTHOR]

This work addresses the problem of two unmanned aerial vehicles (UAVs) transporting a slung‐load on a plane. The vehicles presented are a simplified version of the classical planar vertical take‐off and landing (PVTOL). The proposed solution implements a decentralized control scheme based on the concept of passivity where there is no explicit communication between agents. This is accomplished by taking advantage of the physical connection between the agents and the load. In contrast with similar control algorithms based on force control, like impedance filters, the objective is to drive the system into a state of minimal energy. In addition to this, the presented control strategy differs from other passivity based methods, like an interconnection and damping assignment passivity‐based control, in that the method can also be used to accommodate more complex control strategies that do not necessarily depend on the energy of the system. [ABSTRACT FROM AUTHOR]