This paper presents a subsystem-aware Stackelberg game framework for optimal power control in periodically switched cyber-physical systems (PSCPS) operating over wireless networks under jamming attacks. To account for subsystem switching dynamics, a switching Kalman filter is employed for mode-dependent state estimation, with corresponding error covariance matrices derived for each mode. The impact of switching is quantified using a subsystem-importance-based weighting factor, obtained by analyzing trace variations in the covariance matrices. This factor is integrated into the defender’s utility function to enable adaptive power control. First, a subsystem-aware Stackelberg game model without power constraints is formulated, and the equilibrium strategies of the defender (leader) and the attacker (follower) are obtained via the convex optimization. The framework is then extended to the power-constrained scenario, where feasibility issues arise. By analyzing the boundary and extreme points of the solution space, closed-form equilibrium strategies are derived. Simulation results show that the proposed approach enhances the resilience of PSCPS against jamming attacks in wireless communication environments.

Distributed optimization is growing increasingly important in large-scale complex systems for its flexibility and efficiency. However, the pressing challenge in distributed scenarios is that the cost functions are unknown and communication resources are limited. In response to the dual challenges of inaccessible gradients and limited communication resources, a distributed zero-order gradient scheme with a local prediction mechanism is proposed. It is suitable for unbalanced directed networks and reduces communication overhead via sparsification strategies. The proposed algorithm employs an error feedback mechanism to compensate for corrupted information, thereby ensuring the effectiveness of the zero-order gradient scheme. Furthermore, the devised local prediction mechanism improves the convergence performance of the algorithm in the presence of inaccurate transmission information and gradient estimation bias. With appropriate selection of the step size and setting of the perturbation radius, the proposed algorithm can converge linearly to the optimal point. Finally, the validity of findings is verified by numerical simulations.

This paper presents a switched model predictive control (MPC) approach for switched systems subject to state/input constraints and switching constraints. The switching constraints, including dwell-time restrictions and path constraints, complicate the design of the control law and the switching law. To ensure constraint satisfaction, a switch-admissible control invariant (switch-ACI) set is constructed by incorporating dwell-time restrictions and admissible path constraints. Based on switch-ACI sets, necessary and sufficient conditions are established for the construction of the initial feasible region that ensures the existence of a control law and a switching law satisfying all system constraints. To mitigate the inherent computational complexity of mixed-integer programming (MIP), artificial final points are introduced to develop a computationally efficient switched MPC algorithm, in which the MIP problem is transformed into several standard quadratic programming problems. The persistent feasibility and closed-loop stability of the developed switched MPC are ensured, and the effectiveness of the proposed approach is validated through a vehicle lane-changing case study.

Reinforcement learning methods for manipulator trajectory generation often suffer from low sample efficiency and inadequate exploration. To address this issue, an efficient expert guidance mechanism with dynamic movement primitives is incorporated into reinforcement learning. In the mechanism, the expert model is built to provide suggested actions and guided rewards for the agent. It gradually improves the performance of the agent through a learning strategy of multiple stages, combined with expert demonstration and self-exploration. The proposed method enhances the performance of actor–critic-based reinforcement learning algorithms, which is verified in simulation, and further validated in a sweet pepper harvesting experiment.

This article investigates the problem of almost sure convergence for nonhomogeneous switching Markov chains with absorbing states. Two types of graphs are used to describe the switching walks of state components in the Markov chain under deterministic transfer-restricted switching and arbitrary switching. The transfer-restricted switching among nonhomogeneous Markov chains is characterized by a switching directed graph, while the stochastic transitions within each Markov chain are depicted by state transition component graphs. The reachability relationship between nonabsorbing and absorbing state components is then analyzed by using the Lyapunov function constructed from the state space of Markov chains. Building on the two types of graphs, cycle-dependent switching strategies for almost sure convergence to the target absorbing state are established. Furthermore, necessary and sufficient conditions for almost sure convergence to the common absorbing state under arbitrary switching are derived, which utilize the properties of absorbing Markov chains and state transition component graphs with self-loops. Finally, the effectiveness of the proposed method is validated through two examples.

Robots operating in agricultural environments require a robust, fast perception system to accurately identify picking points. This paper proposed a lightweight method for detecting sweet pepper peduncles, which uses an active illumination camera system and DUTU2-Net+to achieve efficient and accurate peduncle detection. The camera system is used to overcome the influence of ambient light through flash and no-flash (FNF) image pairs, achieving more robust color detection and quickly locating the peduncle’s region of interest (ROI). The improved DUTU2-Net+is used accurately for peduncle ROI detection. It uses an encoder with depthwise separable convolution (DSC), dilated convolution, and a feature enhancement structure with a triple attention module (TAM) to reduce the computational load and parameters while ensuring detection accuracy. Experimental results show that the proposed method can effectively identify the position of the peduncle. The DUTU2-Net+model achieves an average absolute error of 0.002, a maximum F1score of 0.992, a frame rate of 36.3 FPS, and a model size of 6.9 MB. The source code is available at https://gitee.com/rosdx/detection-for-harvesting.git.

This paper investigates a finite-time prescribed performance adaptive control (FTPPAC) scheme for a class of full-state dependent switched uncertain nonlinear systems in the non-strict feedback form. Firstly, a model transformation is introduced to handle unknown control coefficients and unknown nonlinear functions, after which a state observer is designed to estimate the unknown state of the transformed system. Subsequently, the backstepping method and adaptive control are employed to ensure that the tracking error satisfies the pre-designed performance indices. Compared with the traditional design of prescribed performance control (PPC) for switched systems, a novel switching-dependent prescribed performance bound (SDPPB) is presented, which is specifically tailored to satisfy the requirements and characteristics of different subsystems. This design approach allows for the adjustment of the initial value of SDPPB at the switching instants based on the tracking error and subsystem change. Finally, by applying the average dwell time switching and multiple Lyapunov functions method, sufficient conditions for finite-time stability of the closed-loop system are derived. The effectiveness of the proposed control scheme is demonstrated through two simulations. Note to Practitioners—Finite-time stability analysis and quantitative evaluation of control performance are hot topics in the field of control, which play a crucial role in engineering practice. This paper proposes the finite-time prescribed performance adaptive control scheme for a class of switched uncertain nonlinear systems, which can be applied to motion tracking control of servo mechanisms, command tracking of aero-engine and attitude control of spacecraft. It is worth noting that unknown nonlinear dynamics, unknown control coefficients and system state performance constraint problems often exist in practical systems. Therefore, the model transformation is presented to address unknown control coefficients and unknown nonlinear functions. The state observer is used to estimate the unknown state of the transformed system. By designing switching-dependent prescribed performance bound, whose initial value can vary with the subsystem and tracking error, and by utilizing adaptive control method that remains continuous at the switching instant, we ensure that the tracking error satisfies the pre-designed performance indices and that all signals of the closed-loop system are bounded in finite time. In view of the growing demand for transient and steady-state performance in modern industrial production, this control scheme holds promise for practical problem-solving.

This article delves into the security control challenges posed by networked switched systems (NSSs) in the face of deception attacks and denial-of-service (DoS) attacks. It takes an active perspective and proposes an enhanced two-tier Stackelberg game approach to influence the actions of the deception attacker, controller, and DoS attacker. By formulating the cost functions for these three participants, it seeks to derive the optimal solution and the interplay between their respective optimal strategies. Meanwhile, two complicated situations are taken into account: The asynchronous behavior between the system and controller is caused by deception attacks tampering with switching signals; The open-loop operation of the system is caused by DoS attacks blocking the output of the controller. In the joint design of the two-tier multiattacker Stackelberg game approach and average dwell time technique, the system's mean square exponential stability is guaranteed while revealing the quantitative relationship between the deception attack, DoS attack, and asynchronous switching behaviors. Finally, a simulation result of a switched RLC circuit confirms the validity of the obtained active security control methodology.

This article investigates the resilient control strategies of networked switched systems (NSSs) against denial-of-service (DoS) attacks and external disturbance. In the network layer, both the defender and the attacker allocate energy over multiple channels. Considering the impact of switching characteristic in the physical layer on the network layer, a dynamic regulating factor is proposed to adjust the total energy of the defender. To optimize the signal-to-interference-noise ratio and energy consumption simultaneously at each player's side, a multiobjective game problem is formulated. Furthermore, a nondominated sorting genetic algorithm framework is employed, incorporating the knee point selection mechanism to attain the Pareto-Nash equilibrium, based on which the optimal defense strategy can be derived to achieve resilience against DoS attacks. In the physical-layer, taking the dynamic packet loss caused by DoS attacks and external disturbance into account, an minimax controller containing control inputs and the switching signal is designed to guarantee the optimal performance for NSSs through the dynamic game-theoretic approach. Finally, the networked continuous stirred tank reactor system is provided to verify the effectiveness of the proposed method.

The aging population and diminishing working population in agriculture motivate the development of autonomous harvesting robots. Although autonomous harvesting is expanding rapidly, the commercial application of sweet pepper harvesting robots still faces challenges. This paper presents the development of a sweet pepper harvesting robot and reports its experimental verification, which mainly includes end-effector design, visual perception, and grasping pose control. The end-effector adopts electrical control, mainly composed of a servo-electric two-finger parallel clamping module, a swing-cutting module, and a fruit recovery device. Equipped with a tactile sensor array, it can accurately sense the sweet pepper peduncle position and the end-effector state (harvesting failure) to complete the precise cutting. An end-effector grasping pose control algorithm of the manipulator is proposed, which can control the end-effector to grasp along the direction of the fruit peduncle and perpendicular to the tangent direction of the picking point by estimating the pose of the sweet pepper peduncle. Finally, the robot and proposed method were verified in a plant factory. The experimental findings demonstrate that the developed harvesting robot can complete robust detection of fruit peduncles and non-destructive picking of sweet pepper, with an average picking time of about 15 seconds.

This article studies the stability problem of discrete-time switched positive linear systems (SPLSs) with marginally stable subsystems. Based on the weak common linear copositive Lyapunov function (weak CLCLF) approach, the switching property and the state component property are combined to ensure the asymptotic stability of SPLSs under three types of switching signals. First, considering the transfer-restricted switching signal described by the switching digraph, novel cycle-dependent joint path conditions are proposed in combination with state component digraphs. Second, under the time interval sequence, two types of path conditions are constructed for designing switching schemes. Third, necessary and sufficient conditions for the asymptotic stability of SPLSs under arbitrary switching are established. Finally, three examples are provided to illustrate the effectiveness of the proposed method.

This article investigates the bumpless transfer (BT) control problem of switched systems with unmeasured states. Note that the control input signal of the switched system jumps when switching occurs, which may adversely affect the performance of switched systems. By introducing a modified state observer and an auxiliary continuous control signal, a novel description of the BT performance for the system is presented. Moreover, a compensation-based dynamic BT controller and a bump-dependent switching law are designed to solve the BT control problem. As a result, the developed control method ensures the stability of the closed-loop system while maintaining BT performance. The validation of the results can be provided by applying the proposed BT control method to an example of an aero-engine control system.

This article presents a novel control protocol for a class of switched large-scale nonlinear systems subject to prescribed performance constraints and bumpless transfer constraints. The prescribed performance constraints enforce the output tracking error to converge to a predefined residual set, with a predefined maximum overshoot and minimum convergence rate. Meanwhile, the bumpless transfer constraints restrict the bump of the control input when switching between two adjacent modes. By incorporating the modification signals into the prescribed performance functions (PPFs), control-bump PPFs (CPPFs) are provided to proactively reduce the performance bounds to a reasonable level. The modification signals are associated with control bumps and generated by an auxiliary switched positive system. Furthermore, a model of control bump is developed to enhance the dynamic response, and switched disturbance observers are employed to estimate external disturbances. In the framework of dynamic surface control and multiple Lyapunov stability theory, a prescribed performance bumpless transfer control (PPBTC) approach is proposed to guarantee the prescribed performance and bumpless transfer performance. Finally, a practical example of the proposed control scheme is presented to demonstrate its validity and applicability.

This article investigates the resilient control problem of networked switched systems against denial-of-service attack. The resilient control strategies include the security game of the network layer and the optimal control for the physical layer. First, an$\boldsymbol{N}$-coalition noncooperative game is utilized to characterize the network security game between an intrusion detection system and the attacker, where their strategy sets are obtained from the Nash equilibrium. Then, by the dynamic programming principles, the optimization strategy is developed to obtain the optimal controller gains for the subsystems. Meanwhile, for ensuring the optimality of the switched system, a genetic algorithm is adopted to search for the optimal switching signal. Finally, the proposed methods are applied to a networked continuous stirred tank reactors system, which demonstrates its effectiveness.

This article investigates the finite-time stability (FTS) of switched nonlinear systems (SNSs) with state jumps. Our main concerns are twofold. One is that the SNS only consists of FTS subsystems. The other is that the SNS has both FTS subsystems and exponentially stable (ES) subsystems. Due to the switches and state jumps, the main challenge of the study comes from the estimation of settling time and the synthesis of different Lyapunov functions for FTS/ES subsystems. Based on the analytical-inductive method and the initial-value-dependent dwell-time approach, several FTS criteria for the SNSs are established. Then, we provide the estimation of settling time which is explicitly dependent on the dwell time and initial value. Our results can be applied to the finite-time control problem of switched systems with/without state jumps, even when some subsystems can only be exponentially stabilized through linear feedback control and others are finite-time stabilized by discontinuous/nonlinear controls. Finally, three examples are given to illustrate the proposed results. The finite-time stabilization of a continuous stirred tank reactor and the finite-time consensus of a multiagent system are both adopted as potential applications.

This article considers the stochastic stability of uncertain positive Markov jump fuzzy systems (PMJFSs). The uncertainties consist of partially unknown membership functions and generally uncertain transition rates (GUTRs). In the GUTR model, each transition rate (TR) is completely unknown or only its estimate value is known. The coexistence of the partially unknown membership functions and GUTRs makes the control problem difficult to handle. Based on the T-S fuzzy scheme, a switching-type controller with input constraint which parallels as the structure of the uncertain fuzzy model is applied to ensure the positivity and stochastic stability of such systems. A linear decoupling strategy is proposed to tackle the coupling problems when designing the controller and further derives the desired controller parameters. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.

This article investigates the exponential almost sure stability (EAS-stability) of switching Markov jump systems (SMJSs). The considered SMJSs are ruled by Markov jump switching and deterministic switching simultaneously. The deterministic switching follows the persistent dwell time (PDT) switching which is more general than the dwell time (DT) switching and the average DT (ADT) switching. To tackle the difficulties caused by PDT switching, a new method is constructed by restricting the deterministic switching frequency into a suitable range. First, the detailed parameter analyzing is presented because the general measure parameter is unable to describe the dynamic characteristic of SMJSs with PDT switching. Second, the switching rule is established on account of the restricted switching frequency. Two sufficient conditions are further proposed to ensure the EAS-stability of SMJSs with all stable subsystems and SMJSs containing unstable subsystems, respectively. A numerical example and a fault-prone system model are given to illustrate the effectiveness of the proposed strategies.

The positive stochastic stabilization issue is investigated for Markov jump nonlinear systems. The nonlinear systems in the absence and presence of uncertainties are considered and modeled into Markov jump fuzzy systems with known and partially known membership functions, respectively. For the former, the parallel distributed compensator (PDC) controllers are designed to realize the positive stochastic stabilization. Besides, an additional criterion is derived to reduce the number of linear inequalities. For the latter, namely, some elements of membership functions are unavailable, and the PDC strategy is infeasible. Then, a switching controller, whose structure is parallel to that of fuzzy model, is constructed to achieve the corresponding positivity and stability. All the conditions are transformed to standard form of linear programming problem, which is solved with the help of linprog toolbox. Finally, the feasibility of the obtained results is illustrated by a pest's structured population model.

This paper is concerned with the problem of output feedback adaptive control for switched systems with unmodeled dynamics and relative degree p. Firstly, for the former p states, based on the local filters, an adaptive controller is designed by using a backstepping recursive design method and a common coordinate transformation for all subsystems. Second, a common Lyapunov function is constructed to establish the boundedness of all signals in the resulting closed-loop switched systems under arbitrary switching signals. Finally, it is shown that a system output can track the desired trajectory.

This paper is concerned with the problem of stabilization for positive switched delay systems. A hysteresis switching law is designed to stabilize positive switched systems with a time delay. On one hand, such switching laws can eliminate the chattering resulting from state-dependent switching laws. On the other hand, a constant time delay may sometimes lead to the instability of positive switched systems. Sufficient conditions on the existence of hysteresis switching laws are presented by making use of linear programming approach. The validity of the proposed approaches is illustrated by a numerical example.

In this paper, an integral sliding mode control approach is employed to analyze the mean-square exponential stability for a class of uncertain switched nonlinear stochastic systems under asynchronous switching signals. Firstly, an integral sliding surface is designed and a sufficient condition for its existence are presented to guarantee the mean-square exponential stability of switched systems in the sliding motion. Then variable controllers are designed such that switched systems remain in the sliding motion from the initial time instant. Due to the asynchronous switching between system modes and the corresponding controllers, the results presented allow the closed-loop systems to be unstable in the mismatched periods. Finally, a numerical example is presented to illustrate the effectiveness of the proposed results.

This paper is concerned with the problems of passivity and passification for a class of uncertain switched systems subject to stochastic disturbance and time-varying delay. The passivity property is adopted to analyze the influence of the external disturbance on such systems to achieve prescribed attenuation levels. Based on average dwell time approach, free-weighting matrix method, and Jensen's integral inequality, delay-dependent sufficient conditions are obtained in terms of linear matrix inequalities, which ensure the uncertain switched stochastic time-delay system to be robustly mean-square exponentially stable and stochastically passive. Then, the switched passive controllers are synthesized by linearization techniques. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the error dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.