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Zero voltage switching (ZVS) of power semiconductor devices is an effective way to improve the efficiency and power density of the power electronic converters with extremely high switching frequency. This article aims to analyze and realize the true full-range ZVS operation of a totem-pole power factor correction circuit. The available ZVS control strategies are investigated, and the conditions for the realization of full-range ZVS operation are analyzed. It is revealed that the existed control strategies did not consider the influence of the dead-time, resulting in the failure of ZVS operation in some input voltage ranges. To address this issue, two feasible retrofitted control strategies, namely the turn-off current optimization control and the adaptive dead-time control, are proposed. The merits and drawbacks of the two control strategies are carefully analyzed and compared. It is found that the adaptive dead-time control can not only achieve the true full range ZVS but also significantly reduce the loss of the freewheeling diodes. The design consideration of key parameters is discussed. Finally, the feasibility of the proposed control and the correctness of the theoretical analyses are verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

This article focuses on the fast finite-time tracking consensus for uncertain nonlinear multiagent systems. By gracefully combining the hierarchical decomposition, adding a barrier power integrator and disturbance compensation techniques, a new adaptive fast finite-time controller is proposed. Compared with the existing works, the proposed method has several distinguishing features: 1) It can achieve fast finite-time convergence with full state constraints; 2) it can deal with some fully unknown nonlinearities and state-dependent disturbance. Moreover, the nonlinearities can be related with the states of all the followers; 3) the input of the leader is not required to be zero, meaning a broad class of references signals can be generated; and 4) the controller is computationally simply and ready to be implemented. No fuzzy logic/neural networks are needed. Applications of the proposed method on multiple servo motors are also studied. [ABSTRACT FROM AUTHOR]

Unexpected faults that occur in the sensors of the single-phase grid-connected converter (SGC) may deteriorate the control performance ofthe grid current and dc-link voltage and pose risks to the connected distributed generation system. This article proposes an unknown input sliding mode observer (UI-SMO) based sensor fault identification and remediation strategy. Therein, an UI-SMO is established that integrates the observations of the grid current and dc-link voltage with the reconstruction of the unknown input (i.e., load condition of the SGC). Such an observer-based analytical redundancy generates the residuals that indicate the abnormalities of the sensors for fault identification. An adaptive fault-tolerant control (AFTC) strategy for the SGC is developed, where an automated control system reconfiguration with unknown input adaptation is seamlessly implemented by substituting the data reported by the faulty sensors with their observed values. Extensive simulation and experimental studies validate the effectiveness of the proposed UI-SMO-based sensor fault identification and AFTC strategy of the SGC. [ABSTRACT FROM AUTHOR]

In this article, for robotic systems with uncertain dynamics and time-varying asymmetric output constraints, we present a composite learning finite-time control scheme with salient features benefited from two design steps. In the first step, unlike existing composite adaptive/learning control algorithms, which result in either exponential convergence or finite-time convergence but exhibit a potential singularity issue, a modified nonsingular terminal sliding mode-based composite learning controller is adopted such that both tracking error and parameter estimation error converge to zero in finite time without singularity. The unknown parameter learning law is constructed by using online historical data and regressor extension, which gives a benefit of relaxing the typically required stringent persistent excitation with a much weaker excitation condition termed interval excitation. In the second step, a universal time-varying asymmetric barrier function (UTABF) is adopted to directly constrain the system output rather than the most commonly used barrier Lyapunov function, which indirectly converts the output constraints into the conservative tracking error constraints. Moreover, the UTABF can handle both constrained and unconstrained cases uniformly without the need for changing the control structure. Both theoretical analysis and experiments results on an industrial manipulator confirm the benefits and effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this article, a robust disturbance rejection controller is designed for quadrotor attitude tracking within dynamic safety operational envelopes. A scalable constraint technique based on the unified barrier function is developed to transform the attitude restricted within the dynamic safety envelope into a new unconstrained state variables. The closed-form state stabilization control input based on the operational safety rules derived from backstepping method with dynamic surface control can simultaneously guarantee the safety and asymptotic stability of underlying system. A sliding mode disturbance observer is used to estimate and compensate for unknown time-varying perturbations with minor chattering and rapid convergence. Finally, numerical simulations and platform experiments are conducted to verify the effectiveness of the proposed dynamic geofencing attitude control. [ABSTRACT FROM AUTHOR]

Human intelligence, cognition, and skills can be applied to robots to accomplish challenging tasks through cooperative control between humans and robots. In this article, we propose two control frameworks for the human teleoperation of multiple robots under undirected and directed graphs. The two control laws are considered for two cases: 1) when both the relative positions and velocities of the robots are available, and 2) when only the relative positions are available. In this article, we first propose a controller that integrates adaptive neural networks, task-space synchronization, and robust control to address several practical issues related to uncertainty in robotic systems, (e.g., model inaccuracy, time delay, and disturbance). Second, another control algorithm using only the relative position information is addressed by using distributed observers on the remote side. As a result, the system is proven to be stable, and the tracking errors of position and velocity are uniformly and ultimately bounded. Finally, the effectiveness of the proposed control algorithms is verified by experimental demonstration. [ABSTRACT FROM AUTHOR]

Considering a class of nonlinear systems subject to actuator saturation and uncertain control input, this article explored the control capability of an extended-state observer-based active disturbance rejection controller in the presence of disturbances and uncertainties. First, the actuator saturation is handled with the convex hull in the extended-state observer-based control scheme, and an ellipsoid invariant set inside the domain of attraction for control systems is obtained using the Lyapunov method. Second, the enlargement problem of the invariant set is also studied, which illustrates that the convex hull of the ellipsoid invariant sets is also an enlargement invariant set. Finally, comprehensive comparisons with existing methods on a brushless dc motor demonstrate the stronger capability of the designed controller on disturbance rejection and actuator saturation. The results show that the disturbance offset and performance index (integrated time and absolute error and integral absolute error) are about half of the classical proportional–integral–derivative(PID) and double-integral sliding-mode control, or the disturbance rejection capability is about twice in this case. [ABSTRACT FROM AUTHOR]

In this article, we consider adaptive output feedback tracking for a class of nonlinear systems with uncertain parameters, multiplicative and additive sensor uncertainties, and external disturbances. A state observer and an adaptive controller are constructed based on gain scaling technique by which a dynamic gain is designed to compensate for the effects of the sensor uncertainties. The adaptive controller is linear-like and, therefore, is simple and easy to implement. It is shown that with this proposed scheme, all states of the closed-loop system are bounded and the real tracking error can converge to an arbitrarily small residual set. Simulation and experiment are presented to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

If one cannot obtain the accurate prior information of the controlled object in advance, it will take much effort to get good dynamic performance. To this end, this article proposes an online estimation method to deal with disturbances and uncertainties for a class of systems with uncertain control gain. Based on the extended-state observer scheme, the estimation problem of disturbance and uncertain control gain is transformed to the convex optimization problem and solved by the sign-projected gradient flow, then the estimation can be applied to compensate disturbance and design controller. Moreover, the exponential and ultimate boundedness is obtained for the derived closed-loop system, together with rigorous theoretical analysis and proof. Finally, both the numerical simulations and the experiment on a brushless dc motor demonstrate that the proposed design has a better tracking performance and stronger robustness to deal with disturbances and uncertainties compared with the existing methods. [ABSTRACT FROM AUTHOR]

In this article, a butterfly-like Prandtl–Ishlinskii (PI) hysteresis model and a novel neural network based adaptive implicit inverse control scheme to describe and control the butterfly-like hysteresis are proposed. The main contributions are: 1) a butterfly-like PI model is developed for the purpose of predicting the hysteresis effects and the model is feasible for controller design; 2) an implicit inverse control scheme especially for mitigating the butterfly-like hysteresis is implemented, which avoids the construction of the direct inverse of the butterfly-like PI model; 3) an adaptive implicit inverse control approach, which integrates the neural network and the implicit inverse technique into the output-feedback control is developed for eliminating the butterfly-like hysteresis and an arbitrarily small $L_{\infty }$ norm of tracking error is achieved. The proposed modeling and control methods are validated experimentally via the dielectric elastomer actuator based motion control platform. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

This article presents an adaptive control framework for dynamically reconfigurable battery (DRB) systems based on the deep reinforcement learning method. The proposed adaptive control framework relies on deep Q-network to learn the DRB system operations. By utilizing its model-free nature, the proposed framework can significantly reduce the complexity of building experiences or expert models for DRB systems as well as improve battery operating time by ensuring cell balancing. Extensive simulation and experimental study has been carried out with data gathered from a real-world DRB testbed, and the results show the effectiveness and efficiency of the proposed control framework. [ABSTRACT FROM AUTHOR]

An adaptive robot control approach that can achieve an accurate approximation of the complex robot dynamics without knowledge unlearning in the case of multiple sequential tasks is proposed in this article. A locally weighted learning (LWL) model with automatic structure growth according to the size of the learning domain is introduced to approximate unknown robot dynamics, and a composite learning technique with regressor extension is applied to improve parameter convergence. The LWL ensures that learning in one area of the learning domain does not cause unlearning in another area, and the composite learning theoretically guarantees the identification performance of the LWL model. By the exploitation of both stored data in memory and instantaneous data, parameter convergence in the LWL model is achieved under a more achievable interval-excitation condition than the stringent persistent-excitation condition. This further enhances the trajectory tracking performance for multiple sequential tasks, which is generally not achievable by existing approximation-based adaptive robot control approaches. Experimental studies have verified the superiority of the proposed approach over prevalent approaches. [ABSTRACT FROM AUTHOR]

This article presents a saturated adaptive-law-based backstepping control method for a class of uncertain nonlinear systems with external dynamic disturbances. In this approach, a novel adaptive backstepping control method with a new command filter is primarily proposed to design the feedback controllers. In order to address the system unknown nonlinearities caused by external dynamic disturbances, a method named “saturated adaptive law approach” is used to estimate the system unknown nonlinearities online. Meanwhile, a filter called “dual command filter” is applied to estimate the derivative of certain signal such that the saturated adaptive law approach can be well implemented. With the help of Lyapunov stability criteria, it is proved that the target signal can be tracked by the output of the system with small error. Finally, our control method is applied to a quadrotor hover system, and two sets of comparison experiments are given to show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article addresses a novel adaptive compensation strategy for a flexible manipulator in the presence of unmodeled system dynamics and an infinite number of time-varying actuator faults. Differing from the existing results, the unmodeled system dynamics are represented by a nonlinear time-varying delay function, which is tackled with the aid of neural network technique and Lyapunov–Krasovskii function. By introducing the projection technique and a piecewise Lyapunov function, a fault-tolerant control method is proposed, the uniformly ultimate boundedness of the closed-loop system is ensured, and the number of actuator faults is allowed to be infinite. Simulation and physical experiment results show that the manipulator can track the desired angle and the vibration can be suppressed effectively under the proposed scheme for the system with uncertainties and actuator faults. [ABSTRACT FROM AUTHOR]

In this article, the geometric adaptive robust hierarchical control strategy is proposed for underactuated quadrotor, in which the negative effects of nonlinearities, uncertainties, and coupling are thoroughly considered and effectively attenuated. For handling nonlinearities, geometric control is adopted, i.e., the attitude error is directly defined on tangent space of rotation group, thus better attitude tracking performance can be achieved. To deal with uncertainties, a comprehensive dynamic model is established with compound damping terms representing the main aerodynamic effects and all inertia parameters including mass, inertia tensor, and center of gravity. Then, integrated direct/indirect adaptive robust control is developed with adaptive compensation of aerodynamic damping and complete inertia, which is beneficial to improve tracking performance. As for the coupling, hierarchical control architecture is utilized to relax the strong coupling between attitude and translational subsystems. Moreover, the stability of the overall closed-loop hierarchical system, together with transient performance and final tracking accuracy, has been rigorously analyzed without time-scale separation assumption. Finally, comparative experiments demonstrate the better performance of the proposed method in terms of tracking accuracy and robustness. [ABSTRACT FROM AUTHOR]

An industrial integrated energy system gathers a variety of production capacity and energy-related units, which also involves the production, transformation, and consumption of cold, heat, power, gas, etc. The combined cooling, heating, and power (CCHP) system built upon the cascade utilization of energy plays a significant role in improving the utilization rate of multiple energy resources. In this article, a controlled hierarchical relevance vector machine is modeled for identifying the scheduling event in the by-product gas system of a steel industry, which can effectively overcome the disturbance triggered by the changing of operational modes (OMs). Considering the multiple time scales (MTSs) property and the changing of seasons and load conditions of the CCHP, a unified modeling approach is proposed to describe MTSs and multiple OMs of the CCHP by using fuzzy singular perturbation here. Furthermore, an extended $Q$ -learning approach by incorporating a hierarchical interval type-2 fuzzy system is designed for dynamically choosing the optimal controller’s parameter for the proposed unified model along with the changing of OMs. The performance of the proposed method can be experimentally validated by a series of industrial application cases to highlight the necessity of modeling and self-adaptive parameter of the controller. [ABSTRACT FROM AUTHOR]

This article studies the problem of obstacle-avoidance tracking control for a class of uncertain nonlinear robot systems in multiple-dynamic-obstacles environment. The main challenge focuses on how to simultaneously ensure tracking performance and obstacle avoidance. The existing collision-avoidance tracking control schemes cannot guarantee the tracking performance inside the obstacle detection region, since the use of additive Lyapunov-barrier function (LBF) generates the dynamic mismatching. To overcome this difficulty, a novel integral-multiplicative LBF is constructed. The adaptive mechanism is designed to compensate for the mismatching uncertainties. By incorporating the barrier function into backstepping procedure, a secure adaptive tracking control scheme is proposed. Compared with the existing results, the proposed control scheme can simultaneously achieve the obstacle avoidance and tracking performance regardless of being inside the obstacle sensing region and unknown nonlinear uncertainties. [ABSTRACT FROM AUTHOR]

In this article, we propose a simple, practical, and intuitive approach to improve the performance of a conventional controller in uncertain environments using deep reinforcement learning while maintaining safe operation. Our approach is motivated by the observation that conventional controllers in industrial motion control value robustness over adaptivity to deal with different operating conditions and are suboptimal as a consequence. Reinforcement learning, on the other hand, can optimize a control signal directly from input–output data and thus adapts to operational conditions but lacks safety guarantees, impeding its use in industrial environments. To realize adaptive control using reinforcement learning in such conditions, we follow a residual learning methodology, where a reinforcement learning algorithm learns corrective adaptations to a base controller’s output to increase optimality. We investigate how constraining the residual agent’s actions enables to leverage the base controller’s robustness to guarantee safe operation. We detail the algorithmic design and propose to constrain the residual actions relative to the base controller to increase the method’s robustness. Building on Lyapunov stability theory, we prove stability for a broad class of mechatronic closed-loop systems. We validate our method experimentally on a slider crank setup and investigate how the constraints affect the safety during learning and optimality after convergence. [ABSTRACT FROM AUTHOR]

The article proposes an adaptive robust controller with synchronization compensation for the Maglev wind turbine yaw system to achieve stable nacelle suspension and pitching suppression. First, two-degree-of-freedom suspension model is constructed with consideration of magnetic coupling and pitching interference, and then deduces a two-terminal suspension control model based on the inverse system. Second, a nominal suspension controller is designed based on the composite error; and then an adaptive synchronization controller is proposed to adjust coupling parameters based on reconstructed synchronous function; an adaptive tracking controller is used to online update dominant control input parameters. The robust controller is to compensate for the influence of the approximation errors during the parameters convergence process. It is proved that the proposed strategy can guarantee the globally uniformly ultimately bounded of all signals, and suspension tracking error and synchronization error can converge to the desired smaller neighborhood of zero. Finally, simulation and experimental results show that the system with the proposed strategy has the faster dynamic response, smaller steady error and synchronization error, and stronger robustness than other two strategies. [ABSTRACT FROM AUTHOR]

In order to improve the motion accuracy and robustness of the clutch mechanical leg for driving robot (CMLDR), and to improve the shift performance of the driving robot vehicle (DRV) for the engagement operation of CMLDR, a neural network adaptive robust control method of shift process for CMLDR with clutch transmission torque compensation is proposed. First, the dynamics model of the driving robot is established considering an external interference force and dynamic modeling error of the mechanical leg. Second, the upper clutch controller including a two-parameter shift module, a switch controller, and a finite-time linear quadratic regulator of the clutch transmission torque is designed. Third, the estimator of clutch engagement torque through a Kalman filter in the process of shift is constructed. Then, the neural network adaptive robust (NNAR) controller of the mechanical leg is designed. Finally, the proof of stability analysis for NNAR controller is conducted. Experiment and simulation results show that the designed controller of the mechanical leg has a strong antiinterference ability. And the DRV accurately tracks the target speed after compensating for the transmission torque. [ABSTRACT FROM AUTHOR]

This article proposes a proportional-integral (PI) event-triggered control strategy for networked nonlinear systems subject to unmatched uncertainties. The influences of the unmatched uncertainties and external disturbances on the output channel are attenuated by an extended-state-observer-based output feedback controller through a state transformation. The outputs of the controller are transmitted in an event-triggered fashion by comparing the difference between the current sampling error and its integration with respect to time. We prove the stability of the closed-loop event-triggered control system by regulating the trajectories of the observation and tracking errors into a compact set. Further, the relationships between the control performance and designed parameters are investigated quantitatively. In addition, Zeno-freeness is guaranteed by proving the existence of a nonzero minimum inter-event time. Finally, the effectiveness of the proposed PI-type event-triggered control strategy in reducing signal transmission rate is validated through numerical examples and various experiments. [ABSTRACT FROM AUTHOR]

In the fully mechanized mining face, proper chain tension of chain transmission system is of great significance in reducing the failure rate and improving the service life of scraper conveyor. However, the disturbance caused by random load and variable stiffness makes it very difficult to control chain tension. An adaptive tension control scheme combining a neural command filtering backstepping algorithm and online identification is proposed in this article based on a novel tension model. First of all, the proposed tension model is represented by a linear regression part and a nonlinear mapping part, which effectively reflects the chain transmission characteristics and avoids the full measurement of each chain ring. Then, Levant's differentiator and state observer are used to expand input variables for identification algorithm and neural networks estimation. To avoid the adverse effect of random load on parameter adaption, stiffness online identification algorithm is employed in improving the tension tracking performance. To compensate for nonlinear mapping part and uncertain nonlinearity caused by random load, neural adaptive estimator is effectively integrated with the robust integral term in backstepping design. Moreover, Lyapunov stability of the proposed controller is guaranteed. Finally, comparative experimental results of six controllers are completed to verify the effectiveness of the proposed controller in the static and random load mode. [ABSTRACT FROM AUTHOR]

The flotation industrial process is a strong nonlinear and coupling multivariable cascade process with a structure of device and operational layers. To address its operational control problem, this article describes nonlinear dynamic characteristics and disturbances in both device and operational layers as the previous sample unmodeled dynamics and its change rate and proposes a compensation-signal-based dual-rate operational feedback adaptive decoupling control approach. First, a device layer controller consisting of a controller-driven model, a proportional–integral controller, an unmodeled dynamics compensator (UDC), and an unmodeled dynamics change rate compensator (UDCRC) is designed. Then, the device layer closed-loop system and the operational layer system are unified in the same time scale, and hence, a controlled plant model is obtained. A system identification algorithm is proposed to obtain the parameters of this model. With these parameters, an operational layer controller consisting of a controller-driven model, a proportional–integral–derivative controller, a feedback decoupling controller, an UDC, and an UDCRC is designed. Finally, convergence proofs of the proposed identification algorithm and the closed-loop system stability analysis are given, and emulation experiments in a hardware-in-the-loop system are used to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this article, the adaptive attitude-tracking problem of a rigid body is investigated, where the input and output are transmitted via a network. To reduce the communication burden in a network, a quantizer is introduced in both uplink and downlink communication channels. An adaptive backstepping-based control scheme is developed for a class of multiple-input and multiple-output (MIMO) rigid body systems. The proposed control algorithm can overcome the difficulty to proceed with the recursive design of virtual controls with quantized output vector, and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector output quantization and input quantization. It is shown that all closed-loop signals are ensured uniformly bounded and the tracking errors converge to a compact set containing the origin. Experiments on a 2 degrees-of-freedom helicopter system illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The independent metering system decouples the meter-in and meter-out flows of the chamber, giving extra control flexibility and can reducing energy consumption while keeping motion control accuracy. The control performance of the independent metering system is significantly determined by the valve characteristics.Valve nonlinearities, such as deadband, nonlinear flow gain, and leakage, may restrict the control accuracy. To address the issue and improve the system control precision, the nonlinearities of the valve need to be further modeled. In this article, a nonlinear valve flow model is proposed to accurately depict the relationships between the flow rate, pressure drop, and input voltage. The valve deadband is compensated by the static inverse function, while a certain polynomial projection function is used to mimic the nonlinear flow curve outside the deadband. To deal with uncertainties and nonlinearities in the independent metering system, an adaptive robust control approach is synthesized. The least-squares estimation law is adopted to online adapt the valve parameter and other system parameters. Comparative experiments were carried out to illustrate the effectiveness of the proposed approach. The results show that the valve flow nonlinearities are further captured by the proposed model, and the independent metering system performed a higher level control precision. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive sliding-mode control (ASMC) with a modified reduced-order proportional integral observer (MROPIO) to provide speed control for a permanent magnet synchronous motor against unknown disturbances, such as system parameter uncertainties and external load torque variation. The ASMC method, based on a new sliding-mode reaching law, improves chattering in the control signal and reduces the time required for the system trajectory to reach the sliding-mode surface. The MROPIO estimates unknown disturbances and provides feed-forward compensation for the ASMC, thus overcoming incorrect estimations of unknown disturbances during speed jumps can cause overshoots in the speed response. All the proposed methods were implemented on an industrial motor drive. The experimental results confirm the validity of the proposed method for practical applications. [ABSTRACT FROM AUTHOR]

In this article, a pneumatic artificial muscle (PAM) based on a metal hydride (MH) is considered for a compact compliant actuator. It is suitable for broad applications of the human–robot interaction (HRI). To address the problem of the HRI represented by a varying environment, a compliant control is introduced. In fact, the bottlenecks of improving the performance in the compliant control of the PAM actuator are: an inherent nonlinear dynamics of a PAM, the parametric and nonlinear uncertainties influenced by a varying environment, and an additional high dimension introduced by an MH employed as a driving force for the PAM. We propose a learning-based adaptive robust control (LARC) framework to tackle these challenges. A Bayesian learning technique deals with the parameter adaptation for the adaptive control. The effectiveness of the LARC has been examined in extensive experiments of tracking control. [ABSTRACT FROM AUTHOR]

Most recent studies on adaptive hydraulic tracking control focus on the trajectory tracking performance while the parameter convergence property is often unsatisfying. This article proposes a composite learning adaptive position tracking controller with improved parameter convergence for electro-hydraulic servo systems. In the composite learning, a prediction error is formulated to exploit input–output memory data, and parameter estimates are driven simultaneously by tracking and prediction errors. Practical exponential stability of the closed-loop system, which implies the convergence of both the tracking and parameter estimation error, is established by a more realizable interval-excitation condition than the stringent persistent–excitation condition. Therefore, superior trajectory tracking is obtained compared with the classical adaptive hydraulic control. Besides, the initial fluid control volumes of hydraulic systems are assumed to be unknown a priori, which enhances the generality of the proposed control approach. The abovementioned two properties are generally not achievable in prevalent approaches to adaptive hydraulic control. Moreover, noisy acceleration signals and the time derivatives of pressure signals are not needed in the proposed approach, which improves its robustness against measurement noise. Extensive experimental results verify its superiority over currently available ones. [ABSTRACT FROM AUTHOR]

Cable-driven parallel mechanism has been widely studied due to its advantages of fast response and large workspace. The structural uncertainties in the system often introduce internal disturbance and unavoidably influence its motion precision. Generally, to reduce the influence, robust control is adopted and the boundary of the disturbance is regarded as a fixed value given by rough estimation. This method often causes large vibration of the motion component, which certainly compromises the dynamic performance of the system. Therefore, to solve this problem, a variable boundary analytical scheme based on the decoupling of control law and internal disturbance is proposed in this article. Specifically, sufficient condition for the solvability of disturbance boundary is proved by the boundedness of the structural matrix error. Then, the disturbance boundary is modeled analytically, so that its particular value can be determined with respect to the system status. Following that, a robust adaptive control algorithm based on the variable boundary of disturbance is developed. It is verified that the tracking error is globally uniformly bounded. The experimental results show that the proposed control method can effectively reduce the tracking errors and attenuate the tension chattering compared with the conventional robust adaptive control scheme with a roughly estimated boundary. [ABSTRACT FROM AUTHOR]

This study investigates the long-term instability problem of periodic-feedback-loop-based control schemes such as repetitive control, periodic adaptive disturbance observer (PADOB), and iterative learning control. The long-term instability problem is the one wherein a closed-loop system becomes unstable because a tracking error diverges with the increase in the number of iterations. A novel robust PADOB is proposed in this article to guarantee the long-term stability of the closed-loop system. The overall system eventually becomes stable because the proposed control scheme using an infinite impulse response (IIR) filter with an optimized lead time can stabilize the error propagation in the iteration domain. Compared to other methods such as zero-phase and IIR filters, the proposed control scheme is very practical because it allows selecting the cutoff frequency of the filter intuitively to stabilize the control system. The optimized lead time in the IIR filter is calculated considering the phase response of the IIR filter; it can improve adaptation speed using a high adaptation gain of the control system. Various simulation and experimental tests based on high-load motion systems that require high-precision positioning are performed to verify the theoretical analysis of the long-term instability problem and the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

For three-phase ac–dc converters, load disturbances, and parameter uncertainties cause large fluctuations of the dc-link voltage, even resulting in system instability. In order to cope with this problem, the disturbance observer-based finite-time control (DOFTC) is put forward in this article, where the finite-time disturbance observer is designed to fast and accurately estimate disturbances. Subsequently, the stability of the ac–dc converter closed-loop system is analyzed and theoretically proved. Finally, experimental results verify the effectiveness of the DOFTC strategy, where good dynamic and steady-state performance is achieved under load disturbance and parameter uncertainties. Compared with the enhanced state observer-based sliding mode control and adaptive control method, the proposed DOFTC method not only reduced the fluctuations of the dc-link voltage by at least 55% but also improved the transient response. [ABSTRACT FROM AUTHOR]

The spatial disturbance, which depends on position rather than time, is widespread in rotary machines. In this article, an improved control method named accurate cycle aligned repetitive control (ACARC) is proposed to deal with its suppression. In the ACARC, the data-storage technique is used to realize the spatial internal model and a design requirement is presented for the rejection of spatial disturbance with a better response. Further, the cycle offset, caused by the auxiliary stabilizing compensator, is significantly reduced by the combination of a spatial low-pass compensator and a time-advance element. Moreover, a cascaded structure of ACARCs is presented together with a detailed design procedure for disturbance with multiple components occurring in actual rotary systems. The effectiveness and superiority of the proposed control method are verified and compared with other methods by the simulations and experiments. [ABSTRACT FROM AUTHOR]

This article presents a robust passivity-based control (PBC) strategy to mitigate the instability effect of the dc–dc boost power converter supplying a constant power load and a constant voltage load in dc microgrid systems. The control robustness of the PBC against both line and load variation is significantly improved by adding the nonlinear disturbance observer (NDO). Using the disturbance estimation technique, this observer works in parallel with the PBC strategy to compensate the disturbances of the system through a feed-forward compensation channel. Based on the dissipation property, the PBC strategy ensures the system stability, whereas the NDO guarantees global trajectory tracking to the reference voltage during disturbances. This control strategy not only ensures large-signal stability of the system, but also provides a fastest recovery performance during disturbances of the system as compared with other PBC strategies. The hardware-in-loop and real-hardware experimental results are provided to verify the control robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The control problem of active suspension systems (ASSs) with uncertainties is considered in this article by formulating control goals as a series of equality constraint (i.e., soft constraint) and inequality constraint (i.e., hard constraint). Uncertainties are (possibly fast) time varying, bounded, and include large mismatched portions. However, bounds are unknown. The objective is to design an adaptive robust control driving the system to converge to soft constraints provided by the sky-hook model, and simultaneously, ensuring the system to meet hard constraints at every instant. The adaptive robust control design is implemented in four steps. First, we investigate a transformation technique for incorporating hard constraints into soft constraints and the model. Second, a nominal control is presented without considering uncertainty. Third, an orthogonal decomposition technique is proposed for dividing uncertainty into matched and mismatched portions, which creatively allocates the mismatched portion for later control design. Fourth, a continuous adaptive law is constructed to emulate a constant design parameter vector associated with uncertainty bounds. It is proved that the proposed control guarantees uniform boundedness and uniform ultimate boundedness of ASSs in the presence of uncertainties and hard constrains. Furthermore, experimental and simulation results on the 2-DOF ASS are presented to validate the effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]

This article presents a novel control strategy for a dual active bridge series resonant converter. The strategy seeks to ensure the stability of the converter over its entire dynamic range while enhances the transient response. Both properties allow the use of the converter in new applications, where fast dynamics are required, surpassing the performance of existing feedback loops. Starting from the generalized averaged model of the converter, we propose a nonlinear control strategy by means of Lyapunov's stability theory. After that, we derive a series of modifications in order to implement the strategy in a microcontroller or a DSP, including a sensorless method to tackle the lack of measurements of certain variables and an adaptive control law to deal with uncertain parameters in the model. The strategy is evaluated in simulations and experiments, employing a commercial converter, and comparing the results with other control policies. [ABSTRACT FROM AUTHOR]

To achieve the precision contouring performance, this article presents a contouring compensation approach for the biaxial motion system even under extreme contour tasks. Two permanent magnet linear synchronous motors are installed in the biaxial motion platform. First, the dynamics of the biaxial motion servo system with the uncertainties such as model parameter variations, additive disturbance, and nonlinear friction are introduced. Since Newton algorithm is provided to realize dynamic contouring error estimation, accurate estimation of the contouring error can be achieved, even for the high velocity and complex contour with sharp corners and large curves. Then, iterative learning control (ILC) is used to control the contouring error for contouring performance improvement. An adaptive PD-type learning law based on an asymmetric Gaussian-like function is applied to improve the performance of the system. Moreover, adaptive jerk control (AJC) integrated with adaptive feedback gain is designed to address the negative effect of the uncertainties and enhance the robustness. The adaptive feedback gain is dominated and tuned by a novel adaptation law without the need for prior bound knowledge of the uncertainties. Meanwhile, parameter estimation is employed to handle the model parameter variations. Finally, the experimental results are presented to validate the efficiency and the achievable contouring performance of the proposed Newton-ILC and AJC approach. [ABSTRACT FROM AUTHOR]

This article proposes a novel optimal tracking control scheme for permanent magnet synchronous motors (PMSMs) with partially unknown dynamics, saturation voltages, and disturbances in both speed and current dynamics. The strict-feedback nonlinear system is employed to present the PMSM model. Augmented feedforward control inputs are proposed to transform a speed and current tracking control problem of conventional cascade structures to an optimal control problem of a new structure. Consequently, the saturated adaptive optimal control law is designed for the problem. The optimal solution of Hamilton–Jacobi–Issac equation, which provides the value to the control law, is approximated by a simple online approximator. An integral reinforcement learning technique is used to tune the approximator without observing unknown dynamics. It is proven that the optimal value function, the control law, and the worst disturbance law converge to the near-optimal values. The simulation and experiment on a PMSM prototype model of a load drive application with a digital signal processing board TMS320F28379D of Texas Instrument are conducted to justify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this article, an adaptive path following the controller of a multijoint snake robot (MSR) based on the improved Serpenoid curve is proposed. The proposed controller can make the MSR follow the desired path. Compared with the traditional controller, this controller can make the position error possess fast convergence speed and high stability. The swing, the controller can estimate unknown friction coefficients, which improves the adaptive path following the ability of the MSR in an environment with unknown friction coefficients. First, the dynamic model without lateral force is established. Then, the control objectives of the controller are formulated. Third, the Serpenoid gait equation is improved, and the state-dependent time-varying amplitude is obtained. Fourth, the input–output control function of the system and the tracking function of the swing amplitude compensation are designed by the adaptive control method. The stability of the motion attitude angle variable errors and uniformly ultimately bounded stability of the tracking position are verified, respectively. Finally, the effectiveness and superiority of the proposed controller are verified by experiments. [ABSTRACT FROM AUTHOR]

Normally, achieving asymptotic convergence in the presence of unknown arbitrarily bounded external disturbance requires noncontinuous control signals. In this article, an adaptive backstepping controller for a class of incommensurate fractional-order nonlinear systems with parametric uncertainties and external time-varying disturbance is proposed to realize asymptotic perfect output tracking. Appropriate adaptive laws are designed to deal with the uncertainties and unknown bounded disturbance. In the designed adaptive backstepping control scheme, an auxiliary function is adopted to obtain a smooth control signal. It is theoretically shown that asymptotic output tracking to a given reference signal is achieved while ensuring global system stability in the sense that all the closed-loop signals are bounded. Simulation and experimental studies demonstrate the effectiveness of the proposed backstepping control scheme. [ABSTRACT FROM AUTHOR]

This article studies an adaptive fault-tolerant approach with a fixed-time sliding mode for trajectory tracking of uncertain robot manipulators with actuator effectiveness faults. An equivalent system with an adaptive technique is developed to remove the effects of uncertainties on tracking performance, whereas an appropriate system transformation is accomplished to eliminate the effects of the actuator effectiveness faults. A significant feature of this study is that the fixed-time stability is guaranteed theoretically and practically in the presence of uncertainties and actuator effectiveness faults. Moreover, the appearing advantages are that the proposed approach yields an improved tracking performance without utilizing the overestimated control gain and prior knowledge of a robot and, then, overcomes the singularity and algebraic loop problems completely. The effectiveness of the proposed approach for tracking systems is accomplished on simulation and experimental results from a qualitative and quantitative analysis. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive trajectory tracking control scheme for low-speed car-like vehicles with less efforts in tuning of the control gains. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is designed to enhance trajectory tracking while ensuring the commanded inputs within the input boundaries. The design of such adaptive control gains is also based on enhancing the convergence rate of tracking errors, especially for lateral deviation from the reference trajectory. It is further theoretically proven that, even under the influence of steering saturation, the proposed controller can make the closed-loop system approximately globally asymptotically stable at zero errors. Comparative MATLAB/Simulink simulations and experimental tests based on Quanser latest self-driving car have been conducted to verify the effectiveness of the proposed control scheme in accurate tracking without violating the input constraints. [ABSTRACT FROM AUTHOR]

For antiswing control of underactuated cranes, how to guarantee the converging speed of cranes through control design is essential but still remains unsolved. In this article, the adaptive antiswing control for underactuated gantry cranes with guaranteed transient performance under unmodeled dynamics and external disturbances is investigated. To sovle this problem, a set of filters are proposed to make the backstepping technique applicable for the control of crane systems. Then through variable transformation the position error and swing angel could be guaranteed converging to the origin with a given exponential speed. Hardware experiments are conducted to show that the proposed scheme achieves better control performance over existing methods, and it is illustrated that the proposed control scheme possesses strong robustness to unmodeled uncertainties and external disturbances. [ABSTRACT FROM AUTHOR]

In dual-motor servo systems, several factors seriously affect the tracking performance especially in high-speed and high-accuracy situations, which include machinery flexibilities, torque disturbance, unmodeled dynamics, and motor parameter differences. Given these factors, this article investigates the command filtered backstepping synchronization control (CFBSC) method for a servo system driven by two motors synchronously. Command filters are used to deal with the virtual control signals in backstepping design process to avoid the computational burden causing by repeated derivatives, and a compensation system is applied to reduce the tracking error. Adaptive control is used to compensate the torque disturbance and unmodeled dynamics. In addition, the speed and torque synchronization control signals are designed to guarantee high synchronization performance. The stability of the dual-motor servo system is proved. And the effectiveness of the proposed controller is validated through experiments. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) has been well recognized for its output tracking ability in systems that perform repetitive tasks, such as robot manipulators. In practice, however, the application of ILC remains challenging as it generally requires the repetition of the initial settings and such industrial manipulators do not provide measurements of actual joint positions. This article presents a practical and fully automated implementation of ILC for industrial robot manipulators while assuming complete model uncertainty and the unavailability of joint velocity measurements. We propose the employment of a dedicated controller tasked only with bringing the arm back to its initial position while reducing the joint position reinitialization error after every iteration. We show that the convergence of the position tracking error is bounded by the initial mismatch. Finally, we provide experimental justification of the proposed approach on a 4-DOF commercial robot manipulator while only using the measurements of relative joint positions and show that our fully automated process outperforms two ILC algorithms with thorough manual homing. [ABSTRACT FROM AUTHOR]

With the characteristics of module-level maximum power point tracking, module-level monitoring, and panel shut-off, single-phase cascaded H-bridge (CHB) inverter has outstanding advantages applied to household photovoltaic (PV) power generation occasion. However, unbalanced output powers among PV panels is an inherent problem of single-phase CHB PV inverter, which will make the H-bridges (HBs) with higher powers overmodulation, resulting in deteriorated grid current and even system instability. Aiming at this issue, this article proposes a power adaptive control strategy, which divides system operation into three modes, and calculates modulation waveforms by using different methods in different modes. By flexibly adjusting the transmission power of HBs in different modes, all the HBs are able to avoid overmodulation, and single-phase CHB PV grid-connected inverter can still operate normally even if the maximum powers among PV panels are seriously unbalanced. The effectiveness of the proposed control strategy is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Direct flux vector control has an unique advantage in facilitating flux-weakening operation due to the choice of controlled variables: stator flux linkage magnitude and torque producing current. However, the dynamics in stator flux oriented reference frame is heavily affected by the nonlinear cross-coupling between the two axes. This article presents a nonlinear transformation method to decouple the axes for a uniform bandwidth at all operating points. Respect to the literature, the proposed transformation takes magnetic saturation into account without approximation. Furthermore, the auxiliary-flux and auxiliary-current vectors are introduced to design a new adaptive evaluation of maximum torque per ampere and maximum torque per volt control laws, enabling to track the optimal control laws without the need for preprocessed lookup tables. The proposed scheme is experimentally validated on a 1.1 kW synchronous reluctance machine test bench. [ABSTRACT FROM AUTHOR]

Automatic manipulation of deformable objects is a challenging problem. Improper operations, such as excessive stretching and collision, are easy to cause damages to the deformable objects. Thus, not only the final configuration but also the trajectory of the deformation is supposed to be controlled during the process of interaction. In this article, a method to control the trajectory of the deformation in the unknown environment is proposed. We design an adaptive dynamic controller that adaptively estimates the deformation Jacobian matrix (DJM) online based on function approximation techniques (FAT), which approximates nonlinear functions with an arbitrary small error, avoiding modeling for compliant objects. Besides, we introduce a virtual force to improve the manipulability of the method. The stability of the proposed adaptive algorithm and the boundedness of internal signals are proved by Lyapunov theory whenever the approximation error is nonnegligible or negligible. Experiment results validate the efficiency of the algorithm proposed. [ABSTRACT FROM AUTHOR]

As one type of unmanned aerial vehicles, the quadrotor typically suffers from payload variations, system uncertainties, and environmental wind disturbances, which significantly deteriorate its attitude control performance. To provide high-speed, accurate, and robust attitude tracking performance for the quadrotor, an adaptive fast nonsingular terminal sliding mode (AFNTSM) controller is proposed in this article. The proposed AFNTSM controller combines the advantages of fast nonsingular terminal sliding mode (FNTSM), integral sliding mode, and adaptive estimation techniques, which are effective to achieve the desired tracking performance and suppress control signal chattering. Furthermore, unlike conventional methods, the adaptive estimation removes the requirements for the upper bound information of the disturbances. It is proved that the proposed AFNTSM can guarantee finite-time convergence and zero tracking error for the quadrotor attitude control. Finally, comparative study with the FNTSM control only and conventional sliding mode control is conducted through experiments and the results demonstrate that the proposed AFNTSM can achieve faster convergence and stronger robustness in line with theoretical analysis. [ABSTRACT FROM AUTHOR]