Suboptimal control synthesis for state and input delayed quadratic systems

In this article a suboptimal linear-state feedback controller for multi-delay quadratic system is investigated. Optimal state and input coefficients resulting from the expansion over a hybrid basis of block pulse and Legendre polynomials are first obtained by formulating a nonlinear programming problem. Afterwards, suboptimal control gains are found by solving a least square problem constructed with optimal coefficients of the open loop study. A sufficient condition for the exponential stability of the closed loop is obtained from generalized Grönwall–Bellman lemma. The Van de Vusse chemical reactor case is handled allowing to validate the proposed technique.

Event-triggered formation control of n-link networked stochastic robotic manipulators

This article proposes an event-triggered control framework to satisfy the tracking formation performance for a group of uncertain non-linear n-link robotic manipulators. The robotic manipulators are configured as a multi-agent system and they communicate over a directed graph (digraph). Furthermore, the non-linear robotic manipulator-multi-agent systems are subject to stochastic environmental loads. By introducing extra virtual controllers in the final step of the backstepping design, a total number of n event-triggering mechanisms are introduced independently for each link of all the robotic manipulator agents to update the control inputs in a fully distributed manner. More precisely, the actuator of each link of a particular agent is capable of being updated independent of other link actuator updates. A rigorous proof of the convergence of all the closed-loop signals in probability is then given and the Zeno phenomenon is excluded for the control event-triggered architectures. The simulation experiments finally quantify the effectiveness of proposed approach in terms of reducing the number of control updates and handling the stochastic environmental loads.

Adaptive sliding mode observer–based integral sliding mode model-free torque control for elastomer series elastic actuator–based manipulator

In this brief, an adaptive nonsingular terminal sliding mode observer–based adaptive integral terminal sliding mode model-free control is proposed for the trajectory tracking control of the output torque of elastomer series elastic actuator–based manipulator. Considering the tip load and its external disturbance, an elastomer series elastic actuator–based manipulator model is established. In order to realize the output torque tracking control of elastomer series elastic actuator–based manipulator, by using the characteristics of elastomer series elastic actuator, the output torque control is transformed into position control. Based on the idea of model-free control, an ultra-local model is applied to approximate the dynamic of the manipulator, and all the model information is considered as an unknown lumped disturbance. The adaptive nonsingular terminal sliding mode observer is designed to estimate the lumped disturbance, and the absolute value of the tracking error is introduced into the sliding surface to make the selection of parameters more flexible. Then, on the basis of adaptive nonsingular terminal sliding mode observer, the adaptive integral terminal sliding mode model-free control is proposed under model-free control framework. The design and analysis of both observer and controller do not rely on accurate model information. Finally, the performance of the proposed method is verified by simulation results.

Disturbance observer–based fixed-time robust control for constrained air-breathing hypersonic vehicle

This article studies the fixed-time robust control problem for the longitudinal dynamics of hypersonic vehicles in the presence of parametric uncertainties, external disturbances and input constraints. First, the dynamic model is transformed into two fourth-order integral chain subsystems by feedback linearization technology. Four novel fast integrating sliding surfaces are designed for each subsystem to guarantee the fixed time convergence of the errors and the derivatives. The double power reaching law is investigated to accelerate the convergence of sliding surfaces. Furthermore, the fixed-time disturbance observer technique is applied to estimate the lumped disturbance precisely. A novel fixed-time anti-saturation auxiliary system is designed to tackle the saturation caused by constraints of actuators. Then the semi-global uniform boundedness of the closed-loop system in a fixed time is proved by Lyapunov’s stability theory. Finally, comparison simulation experiments with the existing higher order sliding mode control method are carried out to verify the proposed method’s effectiveness and superiority.

Intelligent train control for cooperative train formation: A deep reinforcement learning approach

Considering the intelligent train control problem in long-term evolution for metro system, a new train-to-train communication-based train control system is proposed, where the cooperative train formation technology is introduced for realizing a more flexible train operation mode. To break the limitation of centralized train control, a pre-exploration-based two-stage deep Q-learning algorithm is adopted in the cooperative train formation, which is one of the first intelligent approaches for urban railway formation control. In addition, a comfort-considered algorithm is given, where optimization measures are taken for providing superior passenger experience. The simulation results illustrate that the optimized algorithm has a smoother jerk curve during the train control process, and the passenger comfort can be improved. Furthermore, the proposed algorithm can effectively accomplish the train control task in the multi-train tracking scenarios, and meet the control requirements of the cooperative formation system.

An efficient intelligent decision method for bionic motion unmanned system

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

A novel non-singular terminal sliding mode control combined with integral sliding surface for perturbed quadrotor

In this article, a new dynamic non-singular terminal sliding mode control technique for a quadrotor system subjected to external disturbances is evaluated. The offered control approach is based on non-singular terminal sliding mode controller combined with proportional–integral–derivative sliding surface to improve the performance. The proposed controller is formulated using the Lyapunov theory which ensured the existence of the sliding mode surfaces in finite time. Furthermore, the chattering problem, caused by the switching position and attitude laws, has been reduced using the proposed controller. Moreover, a high-precision performance trajectory tracking can be obtained. The problem of the disturbances is addressed using the suggested controller. Simulation results show the feasibility and efficiency of the non-singular terminal sliding mode control-proportional–integral–derivative proposed approach.

Kinematic control of a cable-driven snake-like manipulator for deep-water based on fuzzy PID controller

The traditional deep-water manipulators have several problems to work in confined spaces, such as large volume, complex structure, and inability. To solve these problems, a novel cable-driven snake-like manipulator robot for deep-water is proposed. In this study, the structure design of the cable-driven snake-like manipulator robot is first introduced. Then, we establish the kinematics model of the proposed cable-driven snake-like manipulator robot, which includes three parts: motor-cable kinematics, cable-joint kinematics, and joint-end kinematics. Especially, a tip-following algorithm (Supplemental Material) is presented to fit the confined and complicated underwater scenarios. Furthermore, a kinematics control strategy based on fuzzy PID controller is presented to reduce the tracking error caused by transmission mechanism, and the simulation of the cable-driven snake-like manipulator is carried out based on the MATLAB. The results demonstrate that the tracking error is less than 0.04 mm, which shows the proposed control strategy is effective.

Generalized regression neural network modeling based on inverse Duhem operator and adaptive sliding mode control for hysteresis in piezoelectric actuators

A dynamic generalized regression neural network model based on inverse Duhem operator is proposed to characterize the rate-dependent hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural network can only model the system with one-to-one mapping. An inverse Duhem operator is proposed to extract the dynamic property of the hysteresis. Moreover, it can transform the multi-valued mapping of the hysteresis into a one-to-one mapping to suit the input of neural network. In order to compensate the effect of the hysteresis in piezoelectric actuator, the adaptive sliding mode controller with a feedforward hysteresis compensator is developed for the tracking control of the piezoelectric actuator. Experimental results demonstrate superior tracking performance, which validate the practicability and effectiveness of the presented approach.

Stability analysis of the anti-lock braking system with time delay

A time delay exists between driver input and vehicle braking state response during the working process of the anti-lock braking system (ABS), and the braking performance of vehicles will be further reduced due to the delay of controllers. This paper investigates a systematic method of stability analysis for time delay ABS, and the analysis focuses on the stability and critical delay algorithm of ABS with delay time. Firstly, the dynamic structure and modelling process of ABS are briefly introduced, and PD control algorithm is adopted to improve the control performance. Then, dynamic models of ABS with time delay are derived, and the full delay stability interval and critical time delay algorithm of ABS are deduced by using the generalized Sturm criterion method. Finally, the validity of the critical delay algorithm by the proposed method and the stability and accuracy of ABS with time delay, different road conditions, vehicle speeds and control parameters are illustrated by numerical simulations, and the results show that the critical time delay algorithm of ABS can be verified under different conditions.