Transactions of the Institute of Measurement and Control

This paper is concerned with the dynamic event-triggered H_∞ filtering for networked systems with stochastic cyber attacks and quantization. First, a new dynamic event-triggered scheme is employed to save limited system resources by using a new triggering error, which includes the conventional one. Second, the filtering error system is formulated under the dynamic event-triggered scheme, quantization and stochastic cyber attacks in a unified framework, and independent Bernoulli distributed variables are used to describe the randomly occurring cyber attacks. Third, a new sufficient condition is established to guarantee the filtering error system is mean-square asymptotically stable and achieves a prescribed H_∞ performance. Under the condition, a new filter design method is given by applying a reversible linear transformation. Finally, a mechanical system with two masses and two springs is given to demonstrate the proposed method is more effective.

In this paper, a fast fault detection scheme is developed for a class of nonlinear interconnected systems with output measurements. First, through combining an adaptive high gain observer with the deterministic learning theory, the system states and unknown dynamics are estimated simultaneously. However, large value of gain may let the estimator becomes noise sensitive. Thus, the observer structure is modified to avoid this issue. Second, by reusing the estimated knowledge which is stored in the constant radial basis function (RBF) neural networks, a bank of dynamic estimators are constructed. Then, the average L 1 norms of residuals are generated. The smallest residual principle is considered for decision-making in the detection phase. Third, the fault detection conditions and detection time are analyzed under the influence of observer gain. A simulation example is utilized to illustrate the effectiveness of this scheme.

This paper considers the problem of randomly varying trial lengths of a class of two-dimensional (2D) linear systems represented by Roesser model, and two new iterative learning control (ILC) schemes are proposed. One is a conventional P-type control rule with a modified tracking error; the other is an ILC law with an iteration-average operator. Both learning control schemes are developed by a 2D stochastic variable to describe varying trial lengths. Under the Bernoulli distribution assumption and the initial state conditions, the convergence analysis is performed rigorously in the probability sense. Finally, illustrative examples have been provided to verify the theoretical results.

The boundary control problem of fractional ordinary differential equations coupled with a time fractional reaction–advection–diffusion equation with delay is studied in this paper. To ensure the asymptotic stability of the system we studied, a state feedback boundary controller is proposed. By backstepping method, we transform the fractional coupled system into a chosen target system under a controller. Furthermore, we obtain the existence and uniqueness of the state solution of the considered system. A Lyapunov functional is constructed to show the asymptotic stability of the fractional coupled system by the special fractional Halanay inequality. The asymptotic stability criterion of the fractional coupled system is described by Linear Matrix Inequalities (LIMs). Which can be easily solved and verified. Finally, the applicability of our theoretical results is showed by a numerical simulation.

For the speed-tracking control of permanent magnet synchronous motor (PMSM) systems, fast dynamic response and high reference speed require a large transient current to provide sufficient electromagnetic torque. However, the excessive transient current may damage the drive circuit and threaten the system safety. Besides, it is also important to pay attention to the influence of multiple disturbances including system parameter uncertainties and unknown load torque variation. To solve these problems, we propose a nonlinear speed-tracking control approach for uncertain PMSM systems. More specifically, based on the cascade control structure and by introducing auxiliary smooth function, we first develop a nonlinear current-constrained controller (CCC) to guarantee the speed tracking, disturbance rejection, and overcurrent protection simultaneously. In order to improve the robustness of the desired performances, we further implement extended state observer (ESO) techniques to propose an ESO-based CCC (ESO-CCC). Finally, comparative experiments are implemented in a 5.5 kW PMSM platform to verify the performance of the proposed two controllers.

The ideal fuel rail pressure is a significant component to keep the high-pressure common rail (HPCR) system work stably. The influence of time-varying fuel injection disturbance of the HPCR system is neither fully considered nor well dealt with in traditional rail pressure control approaches. To this end, the rail pressure tracking problem of an HPCR system is investigated in this paper using a generalized proportional integral observer (GPIO)-based composite control algorithm. A nonlinear model of the HPCR system is first established based on fluid dynamics and mechanics laws. Then, a GPIO is utilized to observe the time-varying fuel injection disturbance. Based on the design of a nonlinear sliding surface using the disturbance estimation, a GPIO-based nonsingular terminal sliding mode control (NTSMC) method is proposed to achieve a better rail pressure tracking control performance and a stronger robustness. Finally, a group of simulations in the MATLAB/Simulink environment and experiments in a more realistic Advanced Modeling Environment for performing Simulation of engineering systems (AMESim) environment are conducted, the results of which demonstrate the advantage and effectiveness of the proposed method.

In flow measurement and control industrial application, installing a flow conditioner is an effective way to rectify irregular and unstable flow to stable flow state within a short flow distance. To effectively rectify the eccentric jet flow caused by valve regulation in pipeline, the convergence flow conditioner with convergent orifice angles has been innovatively designed and investigated. However, the effect of structural parameters (such as flow conditioner thickness and flow area) on improving unstable eccentric jet flow rectification was still not elaborated. Thus, the effect of structural parameters of the convergence flow conditioner on rectifying eccentric jet flow was examined in this paper. The structural parameters of the flow conditioner thickness and the flow area were considered under the fixed convergence angle of 12°. The experimental system of monitoring pressures upstream and downstream of the convergence flow conditioner along the pipeline was developed and corresponding numerical simulation was employed. The flow coefficient and resistance coefficient of a convergence flow conditioner under various valve openings were accessed, and monitored pressures along the pipeline were also discussed for numerical simulation verification. The velocity and streamline distributions downstream the convergence flow conditioners were comparatively analyzed. In addition, the axial velocities on various downstream cross-sections were extracted to evaluate the velocity uniformity. A dimensionless parameter of velocity deviation ratio was introduced to quantify the rectification effect of eccentric jet flow evolving in the downstream pipeline. Results showed that the convergence flow conditioner with the thickness of 6 mm and the flow area of 50% owned a better effect on rectifying the valve-induced eccentric jet flow, that is, a shorter flow length was required for changing the valve-induced eccentric unstable flow to uniform stable flow by installing a convergence flow conditioner with above structural parameters.

For the attitude control system of a quadrotor unmanned aerial vehicle (UAV), an integral backstepping active disturbance rejection control strategy is proposed to solve the tracking and disturbance rejection problems in flight control. To solve the problem of buffeting near the origin of traditional nonlinear functions, this paper designs a function newfal ( · ) with better smoothness near the origin. Second, to improve the observation performance of the extended state observer and the anti-disturbance performance of the controller, an improved expanded state observer (IESO) based on error is designed according to the working principle of expanded state observer (ESO) and the tracking law of state variables. Then, to improve the tracking accuracy of the controller, the integral term is introduced into the backstepping method and uses the idea of recursion to design the nonlinear backstepping integral control law. Combining the disturbance estimation and compensation functions of active disturbance rejection control (ADRC), an integral backstepping active disturbance rejection control is designed and applied to the attitude angle control channel of quadrotor UAV. Finally, experimental results indicate that the designed control strategy can realize high-precision tracking of the attitude angle of a quadrotor UAV, achieve fast dynamic response, and improve the anti-interference.

This work addresses the problem of synchronizing pathological changes in a patient with coronary artery obstruction to the corresponding nominal coronary artery system (CAS) model. The CAS model is characterized by nonlinear terms, so the synchronization problem is transformed into an equivalent time-varying delay T-S fuzzy framework using the sector of nonlinearity approach. New bilinear matrix inequalities (BMIs) conditions for robust stability analysis and dynamic output feedback gain synthesis are derived based on a less conservative condition for stability assessment of T-S fuzzy systems. A simple change of coordinate is used to solve the cross terms of the bilinearities, allowing the problem to be formulated in terms of linear matrix inequalities (LMIs) and solved using standard semi-definite programming. The effectiveness of the controller design approach is demonstrated through extensive simulations under diverse performance criterion that the controller can achieve.

In this paper, a distributed formation tracking control problem is investigated for underactuated surface vessels (USVs). The uncertainties induced by model uncertainties and external disturbances are assumed to be unknown. An event-trigged disturbance observer (ETDO) is proposed to provide the estimations of the uncertainties, and an event-triggered mechanism is used to determine when measured velocity information must be sent to the observer. An event-trigged controller (ETC) is designed based on a backstepping technique, dynamic surface control, and the observer. Stability analysis of distributed formation control system is given to prove all signals are bounded. Simulations demonstrate the proposed control strategy.

This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.

In this article, a prescribed-time state-feedback stabilization design strategy is proposed for a class of p-norm stochastic nonlinear strict feedback systems. In previous work on prescribed-time stabilization of stochastic systems, only stochastic nonlinear systems with fractional power less than or equal to one are considered. To overcome this problem, we improve the existing method and discuss the issue of prescribed-time stabilization of stochastic nonlinear systems with fractional power is arbitrary positive odd rational number. First, a prescribed-time controller is designed by combining the Lyapunov function with adding a power integrator technique. It should be pointed out that the homogeneous domination approach is adopted when dealing with the nonlinear terms of the system. Then, according to the stochastic prescribed-time stability theorem, it is proved that the designed controller can ensure the closed-loop system is prescribed-time mean-square stable. Finally, three simulation examples are given to investigate the validity of the presented method, in which the last one is an electromechanical system example.

Aiming at the oscillation in the electric vehicle powertrain systems, a control strategy based on the structural singular value μ is proposed. To solve the problem of robustness under parameter uncertainty, the traditional μ -synthesis needs to obtain all-pass characteristics through the weight function in the nominal system, which reduces the perturbation suppression ability of the system. Unlike the previous use of μ -analysis as a system performance criterion, this paper starts from the essence of parameter uncertainty problems and designs controllers based on the relationship between parameter uncertainty and nominal systems, using structural singular value μ as a performance constraint. The result shows that the μ -analysis not only takes into account the robustness and disturbance rejection ability but also reduces the controller order. In addition, the oscillation suppression effect in the presence of transmission backlash is verified, and the μ -analysis can suppress the oscillation better than the μ -synthesis.

In this paper, we focus on the rendezvous problem of discrete-time multi-agent systems. Each agent is equipped with the same sensing, computing, and motion-control capabilities to achieve rendezvous based on the neighbors’ states. First, a convex hull combination algorithm (CHCA) is designed, in which each agent solves a convex problem composed of perceived neighbors in the sensing region and chooses an optimal control strategy to move to the next position with guaranteed connectivity under low-density network topologies. Second, the relative neighborhood graph is incorporated into the CHCA (RNCHCA) as the constraint set to adapt to the high-density network topologies. The convergence and connectivity of the proposed algorithms are proved based on the geometric concept and case analyses. Finally, a large number of simulation results show that under the initially connected topologies with different densities, the RNCHCA can achieve a higher rendezvous speed than that achieved by the traditional circumcenter algorithm, particularly under high-density network topologies.

The robust non-singular fast terminal sliding mode (NFTSM) controller is adopted in this work to solve the problem of tracking trajectory of a mobile manipulator (MM) suffering from uncertainties. The NFTSM method has the capability to ensure convergence rate and to provide good tracking accuracy and robustness against external perturbations and parameter uncertainties (total disturbances). However, the NFTSM controller needs high discontinuous gain to reject the effect of strong disturbances, which results in vibrations in the steady-state and chattering in the control law. To solve these issues, an extended state observer–based NFTSM technique is proposed for n -degree of freedom (DoF) coupling MM. The proposed method is designed to approximate and compensate in real-time the uncertainties. It also ensures robustness against total disturbances, good convergence rate, and good tracking accuracy. The stability of the proposed control is also verified based on the Lyapunov theory. Experimental works are conducted on a 5-degree-of-freedom (5-DoF) MM where the results obtained demonstrate the effectiveness of the developed technique and prove the stability of the closed-loop system.

To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.