Safety stabilization of switched systems with unstable subsystems

This paper is concerned with the problem of safety stabilization for switched systems where the solvability of the problem under study for individual subsystems is not assumed. A new state-dependent switching strategy with guaranteed dwell-time for switched systems is constructed, and a sufficient condition for absence of Zeno behavior is derived. Also, a novel switched control design method is proposed to simultaneously guarantee the safety of the switched closed-loop system and stabilize the system based on the union of a common barrier function and a single Lyapunov function, which effectively handles the conflict between safety and stability objectives. Finally, two examples are presented to demonstrate the effectiveness of the proposed design approach.     

Dynamic output feedback stabilization of deterministic finite automata via the semi‑tensor product of matrices approach

This paper deals with the dynamic output feedback stabilization problem of deterministic finite automata (DFA). The static form of this problem is defined and solved in previous studies via a set of equivalent conditions. In this paper, the dynamic output feedback (DOF) stabilization of DFAs is defined in which the controller is supposed to be another DFA. The DFA controller will be designed to stabilize the equilibrium point of the main DFA through a set of proposed equivalent conditions. It has been proven that the design problem of DOF stabilization is more feasible than the static output feedback (SOF) stabilization. Three simulation examples are provided to illustrate the results of this paper in more details. The first example considers an instance DFA and develops SOF and DOF controllers for it. The example explains the concepts of the DOF controller and how it will be implemented in the closed-loop DFA. In the second example, a special DFA is provided in which the DOF stabilization is feasible, whereas the SOF stabilization is not. The final example compares the feasibility performance of the SOF and DOF stabilizations through applying them to one hundred random-generated DFAs. The results reveal the superiority of the DOF stabilization.     

Identification of FIR systems under difference-driven scheduled quantized observations

In networked system identification, how to effectively use communication resources and improve convergence speed is the focus of attention. However, there is an inherent contradiction between the two tasks. In this paper, the event-driven communication is used to save communication resources for the identification of finite impulse response systems, and the input design is carried out to meet the requirements of convergence speed. First, a difference-driven communication is proposed. Then, the performance of the communication mechanism is analyzed, and the calculation method of its communication rate is given. After that, according to the communication rate and the convergence rate of the identification algorithm, the input design problem is transformed into a constrained optimization problem, and the algorithm for finding the optimal solution is given. In addition, considering the case that the output is quantized by multiple thresholds, the way to calculate its communication rate is given and the influence of threshold number on communication rate is discussed. Finally, the effectiveness of the algorithm is verified by simulation.     

Quantum‑enhanced reinforcement learning for control: a preliminary study

Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained great achievements in biomedicine, Internet of Things (IoT), logistics, robotic control, etc. However, there are still many challenges for engineering applications, such as how to speed up the learning process, how to balance the trade-off between exploration and exploitation. Quantum technology, which can solve complex problems faster than classical methods, especially in supercomputers, provides us a new paradigm to overcome these challenges in reinforcement learning. In this paper, a quantum-enhanced reinforcement learning is pictured for optimal control. In this algorithm, the states and actions of reinforcement learning are quantized by quantum technology. And then, a probability amplification method, which can effectively avoid the trade-off between exploration and exploitation via quantized technology, is presented. Finally, the optimal control policy is learnt during the process of reinforcement learning. The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym. The preliminary study results validate that, compared with Q-learning, this quantized reinforcement learning method has better control performance without considering the trade-off between exploration and exploitation. The learning performance of this new algorithm is stable with different learning rates from 0.01 to 0.10, which means it is promising to be employed in unknown dynamics systems.     

Adaptive feedback control for nonlinear triangular systems subject to uncertain asymmetric dead-zone input

In this paper, an adaptive control strategy is proposed to investigate the issue of uncertain dead-zone input for nonlinear triangular systems with unknown nonlinearities. The considered system has no precise priori knowledge about the dead-zone feature and growth rate of nonlinearity. Firstly, a dynamic gain is introduced to deal with the unknown growth rate, and the dead-zone characteristic is processed by the adaptive estimation approach without constructing the dead-zone inverse. Then, by virtue of hyperbolic functions and sign functions, a new adaptive state feedback controller is proposed to guarantee the global boundedness of all signals in the closed-loop system. Moreover, the uncertain dead-zone input problem for nonlinear upper-triangular systems is solved by the similar control strategy. Finally, two simulation examples are given to verify the effectiveness of the control scheme.     

Extended state observer‑based pressure control for pneumatic actuator servo systems

A pneumatic actuator is a fast and economical tool that converts compressed air into mechanical motion. In this paper, an extended state observer (ESO)-based sliding mode controller (SMC) is developed to adjust the air pressure of the actuator for accurate position control. Specifcally, an impedance control module is established to produce desired air pressure based on the relationship between forces and desired positions. Then, the ESO-based SMC is implemented to adjust the air pressure to the required level despite the presence of system uncertainties and disturbances. As a result, the position of the actuator is controlled to a setpoint through the regulation of pressure. The performance of ESO-based SMC is compared with that of a classic active disturbance rejection controller (ADRC) and a SMC. Simulation results demonstrate that the ESO-based SMC shows comparable performance to ADRC in terms of precise pressure control. In addition, it requires the least control efort necessary to excite valves among the three controllers. The stability of ESO-based SMC is theoretically justifed through Lyapunov approach.     

Sailboat navigation control system based on spiking neural networks

In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks (SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally, we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.     

Distributed stochastic mirror descent algorithm for resource allocation problem

In this paper, we consider a distributed resource allocation problem of minimizing a global convex function formed by a sum of local convex functions with coupling constraints. Based on neighbor communication and stochastic gradient, a distributed stochastic mirror descent algorithm is designed for the distributed resource allocation problem. Sublinear convergence to an optimal solution of the proposed algorithm is given when the second moments of the gradient noises are summable. A numerical example is also given to illustrate the eff ectiveness of the proposed algorithm.     

Bipartite consensus for nonlinear time-delay multiagent systems via time-varying gain control method

The bipartite consensus problem is addressed for a class of nonlinear time-delay multiagent systems in this paper. Therein, the uncertain nonlinear dynamics of all agents satisfy a Lipschitz growth condition with unknown constants, and part of the state information cannot be measured. In this case, a time-varying gain compensator is constructed, which only utilizes the output information of the follower and its neighbors. Subsequently, a distributed output feedback control protocol is proposed on the basis of the compensator. According to Lyapunov stability theory, it is proved that the bipartite consensus can be guaranteed by means of the designed control protocol. Different from the existing literature, this paper studies the leader–follower consensus problem under a weaker connectivity condition, i.e., the signed directed graph is structurally balanced and contains a directed spanning tree. Two simulation examples are carried out to show the feasibility of the proposed control strategy     

A hybrid genetic algorithm for the electric vehicle routing problem with time windows

Driven by the newlegislation on greenhouse gas emissions, carriers began to use electric vehicles (EVs) for logistics transportation. This paper addresses an electric vehicle routing problem with time windows (EVRPTW). The electricity consumption of EVs is expressed by the battery state-of-charge (SoC). To make it more realistic, we take into account the terrain grades of roads, which affect the travel process of EVs. Within our work, the battery SoC dynamics of EVs are used to describe this situation. We aim to minimize the total electricity consumption while serving a set of customers. To tackle this problem, we formulate the problem as a mixed integer programming model. Furthermore, we develop a hybrid genetic algorithm (GA) that combines the 2-opt algorithm with GA. In simulation results, by the comparison of the simulated annealing (SA) algorithm and GA, the proposed approach indicates that it can provide better solutions in a short time.     

Output feedback adaptive super twisting sliding mode control for quadrotor UAVs

In this paper, a sliding mode control with adaptive gain combined with a high-order sliding mode observer to solve the tracking problem for a quadrotor UAV is addressed, in presence of bounded external disturbances and parametric uncertainties. The high order sliding mode observer is designed for estimating the linear and angular speed in order to implement the proposed scheme. Furthermore, a Lyapunov function is introduced to design the controller with the adaptation law, whereas an analysis of finite time convergence towards to zero is provided, where sufficient conditions are obtained. Regarding previous works from literature, one important advantage of proposed strategy is that the gains of control are parameterized in terms of only one adaptive parameter, which reduces the control effort by avoiding gain overestimation. Numerical simulations for tracking control of the quadrotor are given to show the performance of proposed adaptive control–observer scheme.     

Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach

The path planning of autonomous mobile robots (PPoAMR) is a very complex multi-constraint problem. The main goal is to find the shortest collision-free path from the starting point to the target point. By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered. This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR, which includes the fitness value calculation method and the prior knowledge particle swarm optimization (PKPSO) algorithm. The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient. The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy, which improves the local optima evasion capability. In addition, the quintic polynomial trajectory optimization approach is devised to generate a smooth path. Finally, some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.     

On the leader-following exponential consensus of discrete-time linear multi-agent systems over jointly connected switching networks

The leader-following asymptotic consensus problem for general discrete-time linear multi-agent systems over jointly connected switching networks was solved about a decade ago. Recently, the leader-following exponential consensus was further established using the so-called Krasovskii–LaSalle theorem for a class of discrete-time linear switched systems. But this method involves some advanced concepts such as the weak zero-state detectability of some limiting system. In this paper, we offer a simpler solution to the leader-following exponential consensus problem for general discrete-time linear multi-agent systems over jointly connected switching networks. After converting the solvability of the problem to the establishment of the exponential stability for a class of discrete-time linear switched systems, we first show that this class of linear switched systems is uniformly completely observable. Then, we further conclude that the uniform complete observability for this class of linear switched systems implies the exponential stability for the same class of linear switched systems, thus leading to the solution of the leader-following exponential consensus problem. Moreover, our approach also gives rise to an explicit characterization of the exponential convergence rate of the leader-following consensus problem.     

Input-to-state stability and gain computation for networked control systems

This paper studies the stability problem for networked control systems. A general result, called network gain theorem, is introduced to determine the input-to-state stability (ISS) for interconnected nonlinear systems. We show how this result generalises the previously known small gain theorem and cyclic small gain theorem for ISS. For the case of linear networked systems, a complete characterisation of the stability condition is provided, together with two distributed algorithms for computing the network gain: the classical Jacobi iterations and a message-passing algorithm. For the case of nonlinear networked systems, characterisation of the ISS condition can be done using M-functions, and Jacobi iterations can be used to compute the network gain.     

Discrete-time sliding mode control with power rate exponential reaching law of a pneumatic artificial muscle system

This paper develops a discrete-time sliding mode controller with a power rate exponential reaching law approach to enhance the performance of a pneumatic artificial muscle system in both reaching time and chattering reduction. The proposed method dynamically adapts to the variation of the switching function, which is based on an exponential term and a power rate term of the sliding surface. Thus, the controlled system can achieve high tracking performance while still obtain chattering-free control. Moreover, the effectiveness of the proposed method is validated through multiple experimental tests, focused on a dual pneumatic artificial muscle system. Finally, experimental results show the effectiveness of the proposed approach in this paper.     

Mu-based trajectory tracking control for a quad-rotor UAV

In this paper, the design and application of a robust mu-synthesis-based controller for quad-rotor trajectory tracking are presented. The proposed design approach guarantees robust performance over a weakly nonlinear range of operation of the quad-rotor, which is a practical range that suits various applications. The controller considers different structured and unstructured uncertainties, such as unmodeled dynamics and perturbation in the parameters. The controller also provides robustness against external disturbances such as wind gusts and wind turbulence. The proposed controller is fixed and linear; therefore, it has a very low computational cost. Moreover, the controller meets all design specifications without tuning. To validate this control strategy, the proposed approach is compared to a linear quadratic regulator (LQR) controller using a high- fidelity quad-rotor simulation environment. In addition, the experimental results presented show the validity of the proposed control strategy.     

Fast adaptive regression-basedmodel predictive control

Model predictive control (MPC) is an optimal control method that predicts the future states of the system being controlled and estimates the optimal control inputs that drive the predicted states to the required reference. The computations of the MPC are performed at pre-determined sample instances over a finite time horizon. The number of sample instances and the horizon length determine the performance of the MPC and its computational cost. A long horizon with a large sample count allows the MPC to better estimate the inputs when the states have rapid changes over time, which results in better performance but at the expense of high computational cost. However, this long horizon is not always necessary, especially for slowly-varying states. In this case, a short horizon with less sample count is preferable as the same MPC performance can be obtained but at a fraction of the computational cost. In this paper,we propose an adaptive regression-based MPC that predicts the bestminimum horizon length and the sample count from several features extracted from the time-varying changes of the states. The proposed technique builds a synthetic dataset using the system model and utilizes the dataset to train a support vector regressor that performs the prediction. The proposed technique is experimentally compared with several state-of-the-art techniques on both linear and non-linear models. The proposed technique shows a superior reduction in computational time with a reduction of about 35–65% compared with the other techniques without introducing a noticeable loss in performance.     

Robust non-aggressive three-axis attitude control of spacecraft: dynamic sliding mode approach

Conventional sliding mode control (SMC) has been extensively applied in controlling spacecrafts because of its appealing characteristics such as robustness and a simple design procedure. Several methods such as second-order sliding modes and discontinuous controllers are applied for the SMC implementation. However, the main problems of these methods are convergence and error tracking in a finite amount of time. This paper combines an improved dynamic sliding mode controller and model predictive controller for spacecrafts to solve the chattering phenomenon in traditional sliding mode control. To this aim, this paper develops dynamic sliding mode control for spacecraft’s applications to omit the chattering issue. The proposed approach shows robust attitude tracking by a set of reaction wheels and stabilizes the spacecraft subject to disturbances and uncertainties. The proposed method improves the performance of the SMC for spacecraft by avoiding chattering. A set of simulation results are provided that show the advantages and improvements of this approach (in some sense) compared to SMC approaches.     

Design of semi-tensor product-based kernel function for SVM nonlinear classification

The kernel function method in support vector machine (SVM) is an excellent tool for nonlinear classification. How to design a kernel function is difficult for an SVM nonlinear classification problem, even for the polynomial kernel function. In this paper, we propose a new kind of polynomial kernel functions, called semi-tensor product kernel (STP-kernel), for an SVM nonlinear classification problem by semi-tensor product of matrix (STP) theory. We have shown the existence of the STP-kernel function and verified that it is just a polynomial kernel. In addition, we have shown the existence of the reproducing kernel Hilbert space (RKHS) associated with the STP-kernel function. Compared to the existing methods, it is much easier to construct the nonlinear feature mapping for an SVM nonlinear classification problem via an STP operator.     

Distributed regularized online optimization using forward–backward splitting

This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes. Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time. A distributed forward–backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted. For both cases, we show that the regret upper bounds scale as O( √T ), where T is the time horizon. In particular, those rates match the centralized counterpart. Finally, we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.