The control of unmanned aerial vehicle quadrotor is challenging because of non‐linearities, coupling and disturbance. Here, a novel control method which includes reinforcement learning (RL) component and robust component is proposed. In this method, the RL component only relies on collected data instead of modelling to handle coupling and disturbance from aerodynamics and model. To ensure safety during training and improve training speed, the robust component is used to reduce the disturbance .The stability of the system with our controller is proven by Lyapunov method. The results of the simulation exhibit advanced performance of our controller.

Here, a novel control method including reinforcement learning (RL) component and robust component is proposed. The RL component is used to handle disturbance from aerodynamics and model. The robust component is used to ensure safety during training and improve training speed.image

This paper designs a finite‐time convergence protocol and an event‐triggered control protocol based on Zero‐Gradient‐Sum (ZGS) algorithm under stochastic switching undirected topology, respectively, which greatly expands the theory of continuous‐time distributed optimization algorithms. With finite‐time stability and Lyapunov stability analysis, it is illustrated that the proposed method can finite‐time converge to the optimal solution of distributed unconstrained convex optimization problem and overcome the disturbances of the switching communication networks. In addition, the event‐triggered mechanism can effectively reduce the network burden and communication cost as well as avoid Zeno behaviour. Finally, two numerical simulations verify the advantages and effectiveness of these methods.

This paper designs a finite‐time convergence protocol and an event‐triggered control protocol based on Zero‐Gradient‐Sum (ZGS) algorithm under stochastic switching undirected topology, respectively, which greatly expands the theory of continuous‐time distributed optimization algorithms. With finite‐time stability and Lyapunov stability analysis, it is illustrated that the proposed method can converge to the optimal solution of distributed unconstrained convex optimization problem and overcome the disturbances of the switching communication networks.image

This work proposes the framework of dual dynamic triggers to deal with the communication problem of remote control under the event‐triggered scheme for network control systems (NCSs). Compared with the existing NCSs, a local controller is set up at the plant site, which provides the local control input for the plant as a remote controller. The remote controller and the plant measurement have their individual dynamic triggers. Thus it is allowed that the local controller and the remote controller permit asynchronous communication, which is governed by different event conditions. It is proven that the proposed control strategy can drive the closed‐loop system asymptotically to equilibrium. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method.

To deal with the communication problem of remote control under the event‐ triggered scheme for network control systems, this work proposes the framework of dual dynamic triggers. Compared with the existing networked control systems, a local controller is set up at the plant site, which provides the local control input for the plant as a remote controller. The remote controller and the plant measurement have their individual dynamic triggers. Thus it is allowed that the local controller and the remote controller permit asynchronous communication, which is governed by different event conditions. It is proven that the proposed control strategy can drive the closed‐loop system asymptotically to equilibrium.image

In collaborative haptic training systems, a novice operator is interfaced with an expert operator and cooperatively performs some task on a real/virtual environment. Most control architectures for collaborative haptic training systems do not consider the switching task dominance together with investigating overall stability in the presence of nonlinear dynamics and uncertainty. In this paper, a theoretical framework is presented for switching task dominance in collaborative haptic training systems based on supervision and intervention of the expert operator. To that effect, the novice operator performs the operation with as little as possible interference haptic signals in the normal operational conditions. On the other hand, the expert operator is able to intervene the operation to guide the novice operator when it is necessary. The most challenging part of controller design for such systems is to provide the mentioned supervisory framework in a way that the stability of interaction between the operators and the system is ensured with acceptable task performance in various operational conditions. This work offers a variable‐gain dual robust control scheme to address the above problem. The key idea is that the tracking gain of each controller is adjusted in real‐time to switch the task authorities. It is verified that the input‐to‐state stability property is satisfied for each subsystem. Then, the overall stability is proved by leveraging the small gain theorem. Finally, the functionality and performance of the suggested control architecture is demonstrated through simulation and experimental studies.

This work offers a variable‐gain dual robust control scheme to address the issue of supervision and real‐time intervention of an expert operator in dual user haptic training systems. The key idea is that the tracking gain of each controller is adjusted in real‐time to reverse the task authorities. The input‐to‐state stability property is investigated for each subsystem and the overall stability is proved by using the small gain theorem.image

The regulation and stabilization of a system's output to achieve desired performance and ensure system reliability require the use of different controllers, but selecting appropriate control parameters presents a challenge in ensuring robustness and stability. Proportional–integral–derivative (PID) controllers are popularly used due to their simplicity and effectiveness in addressing these challenges. In this paper, a simple, effective, and efficient, novel graphical technique is proposed to design PID and its variants (PI/PD) controllers, which addresses the challenges in selecting appropriate control parameters. The method involves creating three equispaced vectors for controller parameters , and obtaining a 3D (2D in PI/PD) Cartesian grid of these vectors. All nodes in the grid provide several possible controllers, and integral time squared error (ITSE) is calculated for each controller from the closed‐loop step response of the system. The obtained ITSE is plotted in a 4D (PID) or 3D (PI/PD) graph, and controller parameters corresponding to the minimum value of ITSE are identified. Furthermore, the proposed graphical technique aids in choosing the lower and upper bounds (LB and UB) if the controller is designed using optimization techniques. The better selection of LB and UB reduces the search space resulting in lesser execution time and fewer iterations. To validate the proposed graphical technique, we designed various controllers for widely‐used brush‐less DC and switch reluctance motors in electric vehicles. Additionally, by choosing the LB and UB with the proposed technique, controllers are also designed using three optimization techniques: particle swarm optimization, black widow optimization algorithm, and honey badger algorithm. The obtained controllers using the graphical technique outperformed the optimization techniques in terms of time and frequency domain specifications, and the proposed selection of lower and upper bounds resulted in improved performance in terms of iterations and execution time.

In this paper, a novel graphical technique is presented for designing proportional–integral–derivative (PID) and its variant (PI/PD) controllers. In this method, three equispaced vectors are initially created for controller parameters (, , ), and then a 3D (2D in PI/PD) Cartesian grid of these vectors is obtained.image