Nowadays, devices generate copious quantities of high-speed data streams due to Internet of Things (IoT) applications. For the most part, cloud computing platforms handle and manage all of these data and requests. However, for certain applications, the data transmission delay that comes with transferring data from edge devices to the cloud could be unbearable. When there are a lot of devices connected to the internet, the public network actually becomes a bottleneck for data transfer. In this setting, power management, data storage, resource management, and service management all necessitate more robust infrastructure and complex processes. More efficient use of network and cloud resources is achievable with fog computing's “intelligent gateway” capability. Planning and managing resources is one of the most important factors affecting system performance (especially latency) in a fog-cloud environment. Planning in an environment with fog and clouds is an NP-hard problem. This paper delves into the optimisation difficulty of longevity for data-intensive job scheduling in fog and cloud-based IoT systems. The issue is initially expressed as an optimisation model for integer linear programming (ILP). Next, we provide a heuristic algorithm known as DLJSF (Data-Locality Aware Job Scheduling in Fog-Cloud) that is based on the suggested formulation. The results of the tests showed that the performance of the proposed algorithm is close to the results by an average of 87 %. Also, on average, it is 99.16 % better than the LP results obtained from the optimal solution obtained from the solver obtained from the solution that the data is processed locally. To check the efficiency of the simulation solution, it was repeated for tasks with different entry rates and data with different sizes. According to the obtained documents, the data transfer approach can be valuable and the proposed algorithm has not lost its performance in different conditions. 

DLJSF: Data-Locality Aware Job Scheduling IoT tasks in fog-cloud computing environments

This paper addresses the sliding-mode surface-based adaptive optimal nonzero-sum games control problem for continuous-time nonlinear systems with input constraints. By constructing a value function associated with the sliding-mode surface, the original nonzero-sum games problem can be transformed into constructing multiple optimal control policies. Then, an adaptive dynamic programming method under identifier-critic networks is introduced to deal with the Hamilton–Jacobi equation, where the identifier networks are applied to approximate the unknown dynamics. A remarkable feature is that the critic updating laws are designed through experience replay and gradient descent methodologies, such that the persistence of excitation condition can be excluded. Based on the Lyapunov stability theory, all signals of the close-loop system are verified to be uniformly ultimately bounded. Finally, simulation results are given to illustrate the effectiveness of the proposed optimal nonzero-sum games scheme. 

Sliding-mode surface-based adaptive optimal nonzero-sum games for saturated nonlinear multi-player systems with identifier-critic networks

In this paper, the problem of resilient distributed secure consensus control for uncertain networked agent systems under hybrid denial-of-service (DoS) attacks is investigated. The current situation is that there exists a cyber layer connecting the network control units and a physical layer with specific physical links in the networked agent systems. Both the communication networks connecting the two layers and within the cyber layer may be attacked by DoS maliciously. These two attack scenarios have different impacts on the networked system. The former focuses on updating the control inputs timely, while the latter influences the connection weight of the cyber communication topology. Firstly, a distributed security consensus framework is proposed for the case that updates of signals are destroyed by DoS attacks between two layers, in which an acknowledgment (ACK)-based attack detection and a recovery mechanism are introduced, and a self-triggered based distributed control protocol is designed. On the premise of avoiding Zeno behavior, the relationship between trigger intervals and DoS attack characteristics is revealed. Secondly, corresponding asynchronous switching topology method is developed for secure consensus of the networked systems when DoS attacks are launched within the cyber layer. In addition, we found that large signals jump during switching and triggering will generate pulses and affect system stability. Therefore, a saturation function is introduced to constrain the fluctuation range of the signal. Finally, the effectiveness of the design scheme is verified by the simulation results of multi-robot systems. 

Resilient distributed secure consensus control for uncertain networked agent systems under hybrid DoS attacks

This paper studies the sliding-mode surface (SMS)-based approximate optimal control issue for a class of nonlinear multiplayer Stackelberg-Nash games (MSNGs). First, considering different roles of the players in MSNGs, a hierarchical decision-making process is expressed as designing different cost functions for the leader and the followers. Therein, the leader makes its decision preferentially with consideration of all followers, while each follower responds optimally to leader’s strategy simultaneously via Nash games. Then, based on the sliding mode control technology, a novel robust control policy is proposed to solve the coupling Hamilton–Jacobi equations of nonlinear MSNGs. Subsequently, SMS-based critic network is utilized to approximate optimal control inputs for all players. Based on the Lyapunov stability theory, it is strictly proven that all signals of the closed-loop nonlinear systems are uniformly ultimately bounded. Finally, effectiveness of the proposed method is demonstrated via a simulation example. 

Sliding-mode surface-based approximate optimal control for nonlinear multiplayer Stackelberg-Nash games via adaptive dynamic programming

This paper addresses the event‐triggered stabilization control for multi‐input and multi‐output (MIMO) nonlinear systems. To handle the “explosion of complexity”, the dynamic surface control (DSC) technology is introduced to the proposed control scheme. In addition, the event‐driven idea is adopted to mitigate the data transmission pressure in sensor‐to‐controller and controller‐to‐actuator channels. Considering that the non‐derivable virtual controllers are commonly constructed in the event‐triggered control schemes aiming at saving dual‐channel resources, in this paper, a hybrid event‐triggering mechanism with multiple event detectors is developed to obtain differentiable virtual control signals, thereby providing a suitable application environment for the DSC technology. Meanwhile, an output‐based adaptive threshold is designed to obtain more communication benefits compared with the fixed threshold strategy. Finally, all signals in the closed‐loop system are ensured to be bounded under a standard Lyapunov stability analysis framework, and the effectiveness of the proposed control scheme is validated by a simulation example.

Adaptive dynamic surface control of MIMO nonlinear systems: A hybrid event triggering mechanism

Purpose This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances. Design/methodology/approach A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function. Findings Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward. Originality/value The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism. 

Hierarchical sliding mode-based adaptive fuzzy control for uncertain switched under-actuated nonlinear systems with input saturation and dead-zone

Aiming at the problem of parameter uncertainty and external interference in the trajectory tracking of quadrotor unmanned aerial vehicle (UAV), a discrete-time fast terminal sliding mode control (FTSMC) scheme is proposed in this paper. Firstly, the position model of the UAV is derived based on the dynamic equation, and the discrete-time model of the UAV is obtained and analyzed by Euler discretization technology. Then, by introducing a new discrete-time fast terminal sliding mode surface, and designing the fast sliding mode terminal control rate based on equivalent control, the UAV can track the desired trajectory with small steady-state error. Finally, in the experimental part, the feasibility and superiority of this scheme are verified by comparing with the traditional PID position control method.

Trajectory tracking method of UAV based on discrete-time terminal sliding mode

In order to solve the problem of autonomous positioning of UAV indoors, this paper designs a indoor autonomous positioning system of UAV based on rotatable visual and cascade controller. The system consists of a vision positioning module, an operation processing unit and a flight control module. The said vision positioning module includes a servo, a camera and a laser. The operation processing unit controls the rotation of the monocular camera so that the target object appears in the center of the field of view, and transmits the corresponding rotation angle to the flight control module. After processing the altitude information and servo rotation information, the flight control module calculates the position of the UAV and compares it to the ideal position. At the same time, it transmits the position error into the cascade position controller, which makes the UAV fly with a certain speed and accuracy for correction. Finally, we designed an experiment of area positioning and high precision seeding based on a quadrotor platform to verify the feasibility of the proposed scheme.

Indoor UAV Positioning System Based on Rotational Vision and Cascade Controller

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Hierarchical sliding mode-based adaptive fuzzy control for uncertain switched under-actuated nonlinear systems with input saturation and dead-zone

Trajectory tracking method of UAV based on discrete-time terminal sliding mode

Indoor UAV Positioning System Based on Rotational Vision and Cascade Controller