Periodically intermittent cruise control of heavy haul train with uncertain parameters

With the objective to promote the efficiency of heavy haul transportation, this paper investigates the efficiently intermittent cruise control problem for heavy haul trains. Based on the concept of periodically intermittent control, the closed-loop form of the error dynamic state-space model for heavy haul trains is given considering uncertain parameters, which is different from existing heavy haul train control methods in that the control forces are only provided in part of the running period. To facilitate the controller design, a set of linear matrix inequalities (LMIs) are presented as the sufficient conditions for the existence of the periodically intermittent controller, which guarantees both the speed tracking error and the relative coupler displacements are exponentially stable at the equilibrium state. Simulation results indicate that the proposed control scheme can significantly improve the control efficiency without sacrificing too much on speed tracking performance.     

Finite-time performance guaranteed event-triggered adaptive control for nonlinear systems with unknown control direction

This paper studies the issue of finite-time performance guaranteed event-triggered (ET) adaptive neural tracking control for strict-feedback nonlinear systems with unknown control direction. A novel finite-time performance function is first constructed to describe the prescribed tracking performance, and then a new lemma is given to show the differentiability and boundedness of the performance function, which is important for the verification of the closed-loop system stability. Furthermore, with the help of the error transformation technique, the origin constrained tracking error is transformed into an equivalent unconstrained one. By utilizing the first-order sliding mode differentiator, the issue of “explosion of complexity” caused by the backstepping design is adequately addressed. Subsequently, an ingenious adaptive updated law is given to co-design the controller and the ET mechanism by the combination of the Nussbaum-type function, thus effectively handling the influences of the measurement error resulted from the ET mechanism and the challenge of the controller design caused by the unknown control direction. The presented event-triggered control scheme can not only guarantee the prescribed tracking performance, but also alleviate the communication burden simultaneously. Finally, numerical and practical examples are provided to demonstrate the validity of the proposed control strategy.     

Prescribed performance control for nonlinear parameter-varying systems with an application to turbofan engine

In this paper, we investigated the problem of the prescribed performance control for the turbofan engine described by nonlinear parameter-varying (NPV) systems formulation. NPV systems for describing the turbofan engine are set up. Compared with the existing dynamic linear models and nonlinear models, the turbofan engine NPV model can show its dynamics time-varying features and nonlinearity. Meanwhile, based on the turbofan engine NPV system, a prescribed performance tracking controller is designed by error mapping function, and a class of state-and-parameter-dependent (SAPD) control synthesis conditions are formulated. These SAPD conditions can be effectively solved by sum-of-squares technique, and steady-state and transient performance of tracking error can be ensured. Finally, simulation results on the turbofan engine have been given to verify the feasibility and effectiveness of the prescribed performance tracking control scheme.     

Fuzzy adaptive output-feedback tracking control for nonlinear strict-feedback systems in prescribed finite time

The current paper addresses the fuzzy adaptive tracking control via output feedback for single-input single-output (SISO) nonlinear systems in strict-feedback form. Under the situation of system states being unavailable, the system output is used to set up the state observer to estimate the real system states. Furthermore, the estimation states are employed to design controller. During the control design process, fuzzy logic systems (FLSs) are used to model the unknown nonlinearities. A novel observer-based finite-time tracking control scheme is proposed via fuzzy adaptive backstepping and barrier Lyapunov function approach. The suggested fuzzy adaptive output feedback controller can force the output tracking error to meet the pre-specified accuracy in a fixed time. Meanwhile, all the closed-loop variables are bounded. Compared to some existing finite-time output feedback control schemes, the developed control strategy guarantees that the settling time and the error accuracy are independent of the uncertainties and can be specified by the designer. At last, the effectiveness and feasibility of the proposed control scheme are demonstrated by two simulation examples.     

Varying Zonotopic tube RMPC with switching logic for lateral path tracking of autonomous vehicle

Model mismatch caused by strong nonlinearity and other factors will severely impact the lateral path tracking control in Autonomous Vehicles (AVs) under extreme conditions. Previous studies have focused on guaranteeing robust stability under possible uncertainty realizations through Tube-based Robust Model Predictive Control (TRMPC). However, three deficiencies in TRMPC applications are revealed: unknown disturbance set, simple rigid tube, and excessive conservatism. In this paper, a novel scheme named Varying Zonotopic TRMPC with Switching Logic (SVTMPC) is developed to overcome these limitations. Firstly, a zero steady-state error dynamic model is established, and a new update mechanism of the nominal state is devised to determine the unknown internal disturbance set of the AV system. Secondly, zonotopic representation of all defined sets is used to construct the prediction model, as well as a flexible tube with varying cross-sections is naturally designed to overcome excessive conservation and non-solution of Quadratic Programming (QP). Finally, a switching logic between conservative and radical strategies improves tracking performance under conventional conditions without compromising robust stability. Numerical simulation through three scenarios shows that the SVTMPC controller can comprehensively improve robust stability and adaptability compared with MPC and TRMPC. Hardware-in-the-Loop (HIL) experiment verifies the effectiveness and real-time of the SVTMPC controller.     

Predictor-based optimal robust guaranteed cost control for uncertain nonlinear systems with completely tracking errors constraint

In this paper, a novel composite controller is proposed to achieve the prescribed performance of completely tracking errors for a class of uncertain nonlinear systems. The proposed controller contains a feedforward controller and a feedback controller. The feedforward controller is constructed by incorporating the prescribed performance function (PPF) and a state predictor into the neural dynamic surface approach to guarantee the transient and steady-state responses of completely tracking errors within prescribed boundaries. Different from the traditional adaptive laws which are commonly updated by the system tracking error, the state predictor uses the prediction error to update the neural network (NN) weights such that a smooth and fast approximation for the unknown nonlinearity can be obtained without incurring high-frequency oscillations. Since the uncertainties existing in the system may influence the prescribed performance of tracking error and the estimation accuracy of NN, an optimal robust guaranteed cost control (ORGCC) is designed as the feedback controller to make the closed-loop system robustly stable and further guarantee that the system cost function is not more than a specified upper bound. The stabilities of the whole closed-loop control system is certified by the Lyapunov theory. Simulation and experimental results based on a servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

Improved order-reduction method for cooperative tracking control of time-delayed multi-spacecraft network

This article investigates the order-reduction method for multi-spacecraft cooperative tracking control problems considering non-uniform time delays. The tracking error system is constructed as a linear time-varying (LTV) system since the orbit of the reference point is an ellipse. To facilitate the controller design, a model transformation method is proposed to transform the LTV system into a linear time-invariant (LTI) system with norm-bounded uncertainties. By using the sliding-mode control (SMC) technique, a delay-dependent cooperative tracking controller is designed to guarantee multiple followers to track the leader. Then, an order-reduction method is proposed to reduce the order of sufficient conditions in the form of linear matrix inequalities (LMIs), which make sure that the tracking error system is asymptotically stable. A numerical example is finally provided to illustrate the effectiveness of the designed controller and the improved performance of the order-reduction method.     

Fuzzy adaptive event-triggered control for uncertain nonlinear system with prescribed performance: A combinational measurement approach

This paper addresses the adaptive fuzzy event-triggered control (ETC) problem for a class of nonlinear uncertain systems with unknown nonlinear functions. A novel ETC approach that exhibits a combinational triggering (CT) behavior is proposed to update the controller and fuzzy weight vectors, achieving the non-periodic control input signals for nonlinear systems. A CT-based fuzzy adaptive observer is firstly constructed to estimate the unmeasurable states. Based on this, an output feedback ETC is proposed following the backstepping and error transformation methods, which ensures the prescribed dynamic tracking (PDT) performance. The PDT performance indicates that the transient bounds, over-shooting and ultimate values of tracking errors are fully determined by the control parameters and functions chosen by users. The closed-loop stability is guaranteed under the framework of impulsive dynamic system. Besides, the Zeno phenomenon is circumvented. The theoretical analysis indicates that the proposed scheme guarantees control performance while considerably reducing the communication resource utilization and controller updating frequency. Finally, the numerical simulations are conducted to verify the theoretical findings.     

Multilayer-neural-network observer with compensator and command-filter-based adaptive backstepping tracking control of switched nonlinear systems

This study presents an output backstepping control architecture based on command filter via Multilayer-Neural-Network Pre-Observer with compensator to realise the reference signal tracking of an arbitrarily switching nonlinear systems with nonseperated parameter. First, a multilayer neural network pre-observer is designed before backstepping procedures to servo reconstruct the system states which can not be obtained directly. The pre-observer has superior performance in neutralizing the states abrupt chattering caused by the arbitrarily switching parameter entered in the nonlinear structure. Next, observer error compensation mechanism is designed to compensate the state estimation and shrink the approximation error domain further. Then, the backstepping controller with compensation signal based on command filter is presented to realise the stable reference signal tracking. Last, the proposed control scheme guarantees the states of the closed-loop system bounded. This mechanism makes up the shortcoming of the traditional state observer and give more flexibility in reconstructing the systems states timely, then overcomes the obstacle of the arbitrarily switching parameterized system. Furthermore, compared with the existing traditional uniform robust uncertain controller, the developed backstepping control method combining with the pre-observer not only guarantees the states servo reconstruction and servo control of the switched system, but also improves the tracking performance. Finally, a low-velocity servo turnable switched system is extensively simulated to demonstrate the effectiveness of the developed controller.     

Event-triggered adaptive fault-tolerant control for nonlinear systems fusing static and dynamic information

In this paper, a novel event-triggered adaptive fault-tolerant control scheme is proposed for a class of nonlinear systems with unknown actuator faults. Multiplicative faults and additive faults are taken into account simultaneously, both of which may vary with time. Different from existing results, our controller fuses static reliability information and dynamic online information, which is helpful to enhance the fault-tolerant capability. With the aid of an event-triggering mechanism, an actuator switching strategy and a bound estimation approach, the communication burden is significantly reduced and the impacts of the actuator faults as well as the network-induced error are effectively compensated for. Moreover, by employing the prescribed performance control technique, the system tracking error can converge to a predefined arbitrarily small residual set with prescribed convergence rate and maximum overshoot, which implies that the proposed scheme is able to ensure rapid and accurate tracking. Simulation results are presented to illustrate the effectiveness of the proposed scheme.     

Event-triggered dynamic output feedback tracking control for large-scale interconnected systems with disturbances

This paper is concerned with the event-triggered dynamic output feedback tracking control for large-scale interconnected systems with disturbances. For each node, a novel event-triggered mechanism is driven by local relative output tracking error to determine whether the signal will be transmitted. A two-step optimization is applied for dynamic output feedback controller design which guarantees robust stability of the system with an optimal H_∞ disturbance attenuation level. Finally, a simulation example of master-slave multiple vehicles is given to illustrate the effectiveness of the proposed scheme.     

Event-triggered adaptive target tracking control for an underactuated autonomous underwater vehicle with actuator faults

In this paper, the target tracking control problem is investigated for an underactuated autonomous underwater vehicle (AUV) in the presence of actuator faults and external disturbances based on event-triggered mechanism. Firstly, the five degrees-of-freedom kinematic and dynamic models are constructed for an underactuated AUV, where the backstepping method is introduced as the major control framework. Then, radial basis function neural network (RBFNN) and adaptive control method are made full use of estimating and compensating the influences of uncertain information and actuator faults. Besides, the relative threshold event-triggered strategy is integrated into the tracking control to further reduce communication burden from the controller to the actuator. Moreover, through Lyapunov analysis, it is proved that the designed controllers guarantee that the tracking error variables of the underactuated AUV are uniformly ultimately bounded and can converge to a small neighborhood of the origin. Finally, the effectiveness and reasonableness of the designed tracking controllers are illustrated by comparative simulations.     

Global practical tracking for nonlinear systems with uncertain dead-zone input via output feedback

In this paper, global practical tracking is investigated via output feedback for a class of uncertain nonlinear systems subject to unknown dead-zone input. The nonlinear systems under consideration allow more general growth restriction, where the growth rate includes unknown constant and output polynomial function. Without the precise priori knowledge of dead-zone characteristic, an input-driven observer is designed by introducing a novel dynamic gain. Based on non-separation principle, a universal adaptive output feedback controller is proposed by combining dynamic high-gain scaling approach with backstepping method. The controller proposed guarantees that the closed-loop output can track any smooth and bounded reference signal by any small pre-given tracking error, while all closed-loop signals are globally bounded. Finally, simulation examples are given to illustrate the effectiveness of our dynamic output feedback control scheme.     

Integral predictor based prescribed performance control for multi-motor driving servo systems

An integral predictor-based dynamic surface control scheme is developed with prescribed performance (IPPDSC) for multi-motor driving servo systems in this paper. By employing a novel finite-time performance function and an improved error transformation, the tracking error is limited within a prescribed zone in any preset time without having the overrun and the singularity problem. Furthermore, integral state predictors are designed to update neural network weights to handle high-frequency oscillations under large adaptive gains. Different from the existing approaches, an integral term of prediction error is introduced to eliminate the steady-state error and avoid chattering. In addition, a synchronization controller based on the mean relative coupling structure is proposed to solve the coupling problem between synchronization and tracking. Finally, simulation and experimental results are presented to demonstrate the effectiveness of the designed approach.     

Robust control of a system with a pneumatic spring

Recently, series elasticity has been realized using pneumatics in human-robot interaction systems. Pneumatic circuits provide not only a flexible power transmission, but also the elastic element in a series elastic actuator (SEA). Pneumatic series elastic systems involve more than twice the number of parameters that influence system behaviors in comparison with rigid robotic systems. In this study, a position controller that eliminates the need of identifying a system model by employing the time delay estimation (TDE) technique is proposed for pneumatic SEA systems. The TDE technique is effective in compensating for system dynamics and all uncertainties involved in system behaviors without imposing computation load. TDE error is cancelled out through a learning way, which improves control performance and leads to asymptotic stability. A simulation study demonstrates the robustness of the proposed controllers against uncertainties imposed on the motor system as well as uncertainties on the end-effector. The simulation shows the efficacy of the learning compensation for TDE error.     

Trajectory tracking of flexible joint manipulators actuated by DC-motors under random disturbances

For a class of flexible joint manipulators actuated by DC-motors, the problem of modeling and trajectory tracking control under random disturbances is considered in this paper. How to describe random disturbances and introduce them to the system is the key for modeling and control. According to the relative motion and the equivalent circuit, the effect of random disturbances can be regarded as torque or voltage disturbed by colored noises. Thus, a random model is constructed. By using the vectorial backstepping and the technique of separating out the noise from coupled terms, a state feedback tracking controller is designed such that the state of closed-loop system has an asymptotic gain in the 2nd moment and the mean square of tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters. The effectiveness of the proposed scheme is demonstrated by the simulation results for a two-link robot.     

Distributed fault-tolerant tracking control for heterogeneous nonlinear multi-agent systems under sampled intermittent communications

This paper studies the distributed fault-tolerant control (FTC) problem for heterogeneous nonlinear multi-agent systems (MASs) under sampled intermittent communications. First, in order to estimate the state of leader under sampled intermittent communications, the distributed intermittent observer for each follower is constructed. By using the tool from switching system theory, the estimation error converges to zero exponentially if the communication rate is larger than a threshold value even under the impact of sampled intermittent communications. Then, by applying model reference adaptive tracking technique, a robust FTC protocol is developed to track the distributed intermittent observer. Two algorithms are presented to choose the feedback gain of the distributed intermittent observer and the tracking feedback gain of the fault-tolerant tracking controller. It is proved that the global consensus tracking error is bounded under the developed distributed control protocol. Finally, an example with the coupled pendulums is provided to verify the efficiency of the designed method.     

Adaptive fuzzy output feedback controller design for a HAGC system with input saturation and output error constraints

The comprehensive effect of external disturbance, measurement delay, unmeasurable states and input saturation makes the difficulties and challenges for a HAGC system. In this paper, an adaptive fuzzy output feedback control scheme is designed for a HAGC system under the simultaneous consideration of those factors. At the first place, by state transformation technique, the dynamic model of a HAGC system is simply expressed as a strict feedback form, where measurement delay is converted into input delay. Then, an auxiliary system is employed to compensate for the effect of input delay. Furthermore, an asymmetric barrier Lyapunov function (BLF) is constructed to ensure the output error constraint requirement of thickness error and the fuzzy observer is established to solve unmeasurable states, unknown nonlinear functions at the same time. With the aid of backstepping method, adaptive fuzzy controller is developed to assure that the closed-loop system is semi-globally boundedness and the output error of thickness error doesn’t violate its constraint. At the end, compared simulations are carried out to verify the efficiency of the proposed control scheme.     

Additive-state-decomposition-based model predictive tracking control for PMSM servo system with multiple disturbances

This paper presents an additive-state-decomposition-based model predictive tracking control and disturbance rejection method for a permanent magnet synchronous motor (PMSM) servo system subject to unknown parameter perturbations, unmodeled dynamics, and time-varying load torque. The basic idea of this method is to equivalently decompose the original system into a primary system for handling the tracking control subproblem and a secondary system for dealing with the robust stabilization subproblem. A model predictive controller is designed for the primary system to achieve high-accuracy tracking of the reference speed. As for the secondary system, a novel high-order generalized extended state observer (HGESO) is constructed to estimate the multiple disturbances simultaneously, and a state feedback control law incorporating a disturbance compensator is developed to eliminate the adverse effect of the multiple disturbances on the system output. By combining the control inputs of the two subsystems together, the control objectives of the original system can be achieved. Both the stability criterion and design procedure of the closed-loop control system are developed. Finally, hardware-in-the-loop-based comparative experiments are conducted to demonstrate that the proposed method effectively suppresses the influence of the multiple disturbances on motor speed tracking accuracy and that the control system has both satisfactory dynamic performance and robustness.     

High precision tracking control for a sequence of way-points using dynamic unicycles

This paper investigates the high precision way-point tracking control problems for dynamic unicycles. Firstly, a simplified electrical motor is used to provide the torques in unicycle’s dynamic model. Then, in order to improve the tracking precision, a kinematic controller and a dynamic controller are proposed to drive the unicycle to its given way-points, respectively. A group of parameter conditions is developed for the tracking system to avoid the singularity. Comparing with the other tracking methods, our proposed tracking controllers can effectively drive the dynamic unicycle to its targets with high precision. The stability and performance of proposed tracking method are strictly analyzed in both kinematic and dynamic levels. In final, numerical simulations are provided to verify the effectiveness and advantages of proposed approaches.