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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]

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]

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]

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]

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]

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]

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]

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]

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]

A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications. [ABSTRACT FROM AUTHOR]

This article studies synchronization control of a novel devised treadmill-based testing apparatus for wheeled planetary rover roaming at low speed. To offer satisfactory tracking performance and to ameliorate internal conflicts amongst individual treadmills during rover-treadmill interaction, a decentralized synchronization control strategy integrating an adaptive friction compensation scheme is proposed. To overcome the nonlinear friction effect at low-velocity scenarios, the LuGre model based friction compensation scheme is constructed with a dual-observer structure that can handle parametric uncertainties without recourse to the high-resolution encoder-required parameter identification. With the proposed control design, asymptotic stability of the closed-loop system is guaranteed with the tracking error and synchronization error converging to zero in presence of parametric uncertainties. Experimental results demonstrate the effectiveness of the proposed control strategy, which significantly improves the tracking performance in comparison with the existing proportional differential (PD)-type controller and synchronization controller without friction compensation. [ABSTRACT FROM AUTHOR]

In this article, we proposed a scheme of modeling an air heater system with a long airflow duct, predominantly available in many industrial plants to maintain the desired temperature by simultaneously befitting the transport delay, nonlinearities, model uncertainties, and nonuniform disturbances. At first, the nonlinear delayed air heater system is formulated by combining linear fuzzy system models utilizing fuzzy parallel distributed compensation (PDC) logic. Then, a newly developed L1 adaptive control strategy is modified by replacing its conventional state feedback controller with fuzzy PDC control logic to maintain the desired temperature by nullifying nonlinearities, transport delays, uncertainties, and disturbances. The novelty of the proposed work lies in the fact that the proposed fuzzy PDC law augmented L1 adaptive scheme explicitly deals with the delays and nonlinearities by means of fuzzy PDC logic as well as eliminates time-varying uncertainties and disturbances utilizing L1 adaptive control strategy. The results obtained from real-life laboratory-scale experiments suitably demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this article, existed Lyapunov-based control methods for stand-alone inverters either have a single control loop accompanied with steady-state errors or dual control loops at the sacrifice of negative definiteness of the derivative of the Lyapunov function. Besides, load-current sensors or observers are indispensable in these methods. Proposed Lyapunov-based control inherently has dual control loops that can rigorously guarantee the global large-signal stability of the system. Meanwhile, load disturbance is suppressed adaptively without any load-current sensors or observers. Stability analysis proves that the proposed method is valid both for a linear and nonlinear load. The proposed approach complies with the internal model principle, leading to the minimized steady-state error. An adaptive weighted Lyapunov function (V) is proposed to derive the dual-loop control law and adaptive law. The closed-loop system is inherently d–q decoupled. Three controller gains are tuned quantitatively via explicit formulas based on pole-placement strategy. Simulation and experimental results demonstrate that the proposed method has good steady-state and dynamic performance with great robustness against the parametric mismatch. [ABSTRACT FROM AUTHOR]

Cable-driven parallel robots (CDPRs) are a new type of parallel robots with a mobile platform driven by several cables. The flexibility of the structure not only helps them perform better than traditional parallel robots, but also brings about the issues of model uncertainty and multicable coordination. To handle these problems, a novel adaptive synchronization control (ASC) scheme is proposed to suppress the uncertainties of kinematics and dynamics simultaneously, regulate the coordination motion of multiple cables, and finish high-precision tracking tasks. In the proposed scheme, apart from tracking error, a new synchronization error is introduced to describe the coordination motion relationship between adjacent cables. According to these defined errors, the kinematic and dynamic adaptation laws are designed through the linear expressions of kinematic and dynamic models, respectively. Finally, the ASC scheme is presented as well as a tension distribution algorithm. The tracking experiments are implemented on a redundantly actuated 3-degree-of-freedom CDPR with four cables. The proposed ASC scheme can effectively complete the correction of the model parameters by using the feedback of errors and enhance the tracking accuracy of each cable and the synchronization accuracy between all the cables significantly, thereby ultimately improving the control performance of the mobile platform. [ABSTRACT FROM AUTHOR]

The pantograph–catenary system plays the crucial role in high-speed electric trains for electrical power collection, where continuity and smoothness of the contact between the pantograph and the catenary is one of the key factors ensuring high-quality power collection essential for safe and reliable train operation. In this article, a nonlinear and highly coupled model for the pantograph–catenary system is established, and an accelerated robust adaptive output feedback control method based on a high-gain observer is proposed to solve the contact force tracking control problem of the pantograph–catenary system using output information only. It is shown that, with the proposed method, the tracking error of the contact force is ensured to be ultimately uniformly bounded with an accelerated preassignable decay rate, resulting in smooth contact force and good-quality current collection. Both theoretical analysis and numerical simulation confirm the effectiveness and benefits of the method. [ABSTRACT FROM AUTHOR]

In this article, the issue of sliding mode control for nonlinear stochastic Markovian jump systems with uncertain time-varying delay is investigated. Considering the system state measurements and the state-dependent disturbances are not available for feedback purposes, an observer-based adaptive control strategy is proposed. Based on the decomposition of the input matrices, the state-space representation of the system is turned into a regular form with the aid of T–S fuzzy models first. Then, a fuzzy observer system is constructed, which could be transformed into two lower order subsystems. By choosing a common linear switching surface, on which it also obtains linear sliding mode dynamics in a simple form. Further, an adaptive controller is synthesized relying on the bounded system delay information to ensure the estimated states driven on the predefined sliding surface and remain the sliding motion. Also, the stochastic stability analysis of the sliding mode dynamics is undertaken with two types of transition rates, and an interesting result reveals that the stability for the dynamics with type of uncertain transition rates may cover the completely known type. Finally, a single-link robot arm model is provided to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents a nonmodel-based controller design for vehicle dynamic systems to improve lateral stability, where output tracking control and adaptive dynamic programming approaches are employed to track the desired yaw rate and, at the same time, mitigate the sideslip angle, roll angle, and roll rate of the vehicle. Moreover, different from some existing optimization methods in control allocation, the proposed control strategies, which distribute tire forces by learning, are only using the information of states, input, and reference signal instead of the knowledge of the vehicle system. The iterative process repeatedly uses the information about state and input to calculate the feedback gain. It can significantly reduce the learning time and computational burden. The effectiveness of the proposed controller design method is shown by CarSim simulations. [ABSTRACT FROM AUTHOR]

In this article, a seamless dynamics controller for wild-frequency active rectifiers in more electric aircraft (MEA) is presented. The novelty of the proposed control scheme resides in its ability to seamlessly regulate the output dc-bus voltage and input reactive power in the presence of rapid frequency changes. These smooth dynamics are achieved through a multivariable direct model reference adaptive control (MRAC) formulation, which allows the gains of the controller to adjust themselves to the wild-frequency operation of the MEA power generation. The validity and performance effectiveness of the proposed seamless dynamics control scheme is verified experimentally through a laboratory-scaled three-phase 1.5-kW 270-V SiC two-level voltage-source-converter using a variable-frequency programmable grid emulator. Moreover, a traditional proportional-integral controller is implemented experimentally with the same converter as a benchmark to highlight the merits of the proposed MRAC. [ABSTRACT FROM AUTHOR]

In this article, in order to solve the trajectory tracking control problem with the drive saturation (thrust overrun) for the 4500-m human occupied vehicle named “Deep-sea Warrior,” a model predictive adaptive constraint control strategy is put forward. The proposed control strategy mainly consists of two controllers. The first part is a kinematics controller designed by quantum-behaved particle swarm optimization model predictive control method. The second part is a dynamic controller designed by an adaptive algorithm. In order to study the effect of the ocean current disturbance on tracking controller, the ocean current is incorporated into the kinematics and dynamics model of the 4500-m human occupied vehicle. The thrusts of four degrees of freedom under the ocean current are calculated from designed controllers. Then, the thrusts are assigned to six thrusters on the 4500-m human occupied vehicle according to its thruster arrangement. An ocean current observer based on artificial fish proportional-integral control is designed for unknown currents. The simulation results of tracking control in three-dimensional underwater environment are given, which illustrates that the proposed control strategy can not only meet the hardware requirements (drive saturation) but also achieve a stable and efficient tracking control performance because of its constraint to speed and speed increment, the effect of the ocean current on kinematics and dynamics models and the dual feedback mechanism. [ABSTRACT FROM AUTHOR]

In this article, we study the control problem of the vehicle active suspension systems (ASSs) subject to actuator failure. An adaptive control scheme is presented to stabilize the vertical displacement of the car-body. Meanwhile, the ride comfort, road holding, and suspension space limitation can be guaranteed. In order to overcome the uncertainty, the neural network is developed to approximate the continuous function with the unknown car-body mass. Furthermore, to improve the transient regulation performance of ASSs when the actuator failure occurs, we propose a novel control scheme with the prescribed performance function to characterize the tracking error convergence rate and maximum overshoot in ASSs. Then, the stability of the proposed control algorithm can be proven based on the Lyapunov theorem. Finally, the comparative simulation results of two actuator failure types (i.e., the float fault and the loss of effectiveness fault) are given to demonstrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

It has been shown that the closed-loop transfer function representation of the unconstrained model predictive control (MPC) for single input single output systems is similar to a two-degree-of-freedom controller, which is capable of improving the tracking performance of positioning systems. Conventionally, the optimal parameters of the above-mentioned transfer function are result of a quite complicated MPC tuning. This paper proposes a new approach to obtain the parameters directly instead, where the input/output constraints are not considered. The method combines conventional feedback and adaptive feedforward techniques to minimize the tracking error as well as mitigate the influence of the load disturbance. Experiments on an ultra precision positioning system actuated by piezoelectric actuator show that the proposed method achieves much better tracking performance over a well-tuned conventional MPC. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive time-delay control method to handle the balance of a class of biped robots. Considering model uncertainties and unknown external disturbances, a high-dimensional integral Lyapunov function has been developed, then time-delay estimation technique (TDE) is utilized, and in order to compensate for the errors produced by TDE, an adaptive control law is designed. Based on the evolution of the error trajectories, the adaptive control law can automatically regulate the control gains without any threshold value. By the Lyapunov synthesis, the semiglobally uniformly ultimately boundness (UUB) of the closed-loop control system has been theoretically proved. We have conducted the experimental studies on biped robot to demonstrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body. The NNs are used to approximate the unknown mass of car body. It is commonly known that the stability and security of the ASSs will be weakened when the constraints are violated. Thus, the control problem of the time-varying vertical displacement and speed constraints for the quarter-car ASSs is a very important task because of the demand of the handing safety. The time-varying barrier Lyapunov functions are used to guarantee the constraints of the vertical displacement not violated, and it can prove the stability of the closed-loop system. Finally, a simulation example for the ASSs is employed to show the feasibility and rationality of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an advanced nonlinear controller for hydraulic system to achieve asymptotic tracking with various disturbances. To accomplish this study, a multilayer neural-networks (NNs) estimator is first developed to improve the compensation accuracy of model-based feedforward control terms, which can greatly reduce the uncompensated disturbance, then a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the multilayer NNs estimator to deal with the residual mismatched disturbance, in which the RISE feedback gain is adapted online to further decrease the high-gain feedback. At last, by considering the inwardness of matched disturbance in hydraulic systems, it is estimated by another multilayer NNs fully with the residual functional reconstruction inaccuracies handled by a novel adaptive term. As a result, theoretical analysis reveals that the proposed controller guarantees a semiglobal asymptotic stability. Extensively comparative experimental results verify the priority of the proposed control strategy, and a 0.2% dynamic tracking accuracy is achieved. [ABSTRACT FROM AUTHOR]

The hysteresis behavior of piezoelectric actuators is primarily responsible for the decrease of the precision and performance of the nanopositioning systems. To compensate for hysteresis, an inverse model is proposed in this paper as a segment similarity feedforward compensator. Furthermore, the inverse model is improved by using an adaptive method to eliminate the accumulated error of the similarity compensator. The proposed adaptive similarity compensator has the advantages of simple structure, less storage space, and fast calculation speed, and it is available for most engineering situations. Experiments are carried out with the proposed similarity compensator and adaptive similarity compensator. The experimental results show that the compensator can achieve good results and better performance, especially for high-frequency excitations. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive backstepping control scheme is presented to solve the position control problem for a gear transmission servosystem with unknown asymmetric sandwiched dead-zone nonlinearity. All the parameters involved in both the system and dead-zone models are allowed to be totally unknown. In contrast to the previous results where the effects of symmetric dead zone can be treated as additive disturbances in a transformed system, most of the unknown parameters of the transformed system are now time varying and dependent on the real-time value of relative displacement between the driving and driven gears. To deal with the above issue, new compensating terms in the quadratic form are introduced in the design of the virtual control signal for each recursive step. With the adaptive laws modified correspondingly, we can still show that all the closed-loop signals are ensured to be bounded and the output regulation error will converge to a compact set. Simulation results are provided to show the effectiveness of the proposed adaptive control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the practical use of discrete-time direct adaptive control in a high-precision inertially stabilized platform's turbulence isolation system for the purpose of enhancing isolation performance. Under low-frequency and low-velocity environments, the velocity-stabilized loop of the platform shows severe nonlinear characteristic; therefore, its isolation performance is limited. In previous research, we constructed a nonlinear model of the velocity-stabilized loop by using an improved Stribeck friction model, and also designed a feed-forward compensation strategy. Both have obtained outstanding performance during practical experiments. However, errors still exist as disturbance of the unmodeled part and environmental change causes the system's parameter to vary. To solve this, a novel discrete-time direct model reference adaptive control based on nonlinear friction compensation is introduced to the original proportional-integral control system. An improved projection algorithm and a recursive least-square algorithm with fading memory are respectively used to design the adaptive law. By using a turbulence observer to provide a reference signal, both types of controls are applied to the carrier turbulence isolation system. Results of the practical experiments prove that model reference adaptive control can further enhance the system's isolation ability. [ABSTRACT FROM AUTHOR]

This paper designs a simple model reference adaptive control based speed controller (MRAC-SC) for a surface-mounted permanent magnet synchronous motor drive. The proposed adaptive scheme is designed to track a reference model that ensures the desired exponential decay of a controlled speed error trajectory. Also, the proposed MRAC method includes an adaptive compensating control term and a stabilizing feedback control term. The former is proposed to compensate for uncertain model parameters (i.e., inertia, friction, and load torque) and the latter is constructed to asymptotically stabilize the error dynamics. The asymptotic stability of the closed loop is guaranteed with both control terms using the Lyapunov approach. The comparative studies between the proposed MRAC-SC, the nonadaptive model reference speed controller, and the conventional proportional–integral SC are performed to justify a fast transient response, a good tracking possibility, and robustness against the parameter uncertainties of the proposed MRAC technique. [ABSTRACT FROM AUTHOR]

This paper presents a speed estimation scheme by combining a second-order sliding-mode observer (SMO) with the model reference adaptive system (MRAS) in the sensorless-vector-controlled linear induction motor (LIM) drives for medium-low speed maglev applications. A state space-vector model of the LIM considering the dynamic end effects is rearranged in order to be represented by the form of the super-twisting algorithm (STA). Then, an STA-SMO-based stator current observer of the LIM with Popov's hyperstability theory is designed, which is used to replace the reference model of the MRAS-based scheme. Correspondingly, the actual stator current model is regarded as the adaptive model of the MRAS for speed estimation. Meanwhile, because the variation of stator resistance associated with stator temperature may lead to a large estimated error and even system instability, a parallel stator resistance on-line identification scheme is adopted in the proposed speed estimation scheme to reduce the speed error and improve the stability of the sensorless control system. Compared to the speed estimation schemes based on the Luerberger observer with MRAS and the single-manifold SMO, the proposed speed estimation scheme exhibits better estimated performance. The effectiveness and feasibility of the proposed speed estimation scheme have been verified by simulation and hardware-in-the-loop tests. [ABSTRACT FROM AUTHOR]

An adaptive controller is proposed for unknown nonlinear discrete-time systems when the control direction is unknown and unfixed. Two fuzzy rules emulated networks are utilized to establish a system dynamic model which provides the control-gain and the control direction. The control law is developed according to the control direction including the following advantages: first, the relation between input and output of controlled plant cannot be generally Lipschitz with the zero-change of control effort; and second, the control direction is unfixed for both positive and negative values. Theoretical expressions are manifested to guarantee the convergence of model and tracking performance. The practical-validation is conducted by two experimental systems: first, dc motor current control for the case of fixed control direction and Lipschitz condition, and second, robotic system with opened architecture for the case of unfixed control direction and non-Lipschitz condition at constant input. [ABSTRACT FROM PUBLISHER]

This paper presents an active disturbance rejection adaptive control scheme via full state feedback for motion control of hydraulic servo systems subjected to both parametric uncertainties and uncertain nonlinearities. The proposed controller is derived by effectively integrating adaptive control with extended state observer via backstepping method. The adaptive law is synthesized to handle parametric uncertainties and the remaining uncertainties are estimated by the extended state observer and then compensated in a feedforward way. The unique features of the proposed controller are that not only the matched uncertainties but also unmatched uncertainties are estimated by constructing two extended state observers, and the parameter adaptation law is driven by both tracking errors and state estimation errors. Since the majority of parametric uncertainties can be reduced by the parameter adaptation, the task of the extended state observer is much alleviated. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically achieves an asymptotic tracking performance in the presence of parametric uncertainties and constant disturbances. In addition, prescribed transient tracking performance and final tracking accuracy can also be guaranteed when existing time-variant uncertain nonlinearities. Comparative experimental results are obtained to verify the high tracking performance nature of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

In this paper, an adaptive interval type-2 fuzzy logic control scheme is proposed for high-performance permanent magnet synchronous machine drives. This strategy combines the power of type-2 fuzzy logic systems with the adaptive control theory to achieve accurate tracking and robustness to higher uncertainties. Unlike other controllers, the proposed strategy does not require electrical transducers and hence, no explicit currents loop regulation is needed, which yields a simplified control scheme. But, this limits the machine's operation range since it results in a higher energy consumption. Therefore, a modified reference frame is also proposed in this paper to decrease the machine's consumption. To better assess the performance of the new reference frame, comparison against its original counterpart is carried-out under the same conditions. Moreover, the stability of the closed-loop control scheme is guaranteed by a Lyapunov theorem. Simulation and experimental results for numerous situations highlight the effectiveness of the proposed controller in standstill, transient, and steady-state conditions. [ABSTRACT FROM PUBLISHER]

A brief overview on the model-based control and data-driven control methods is presented. The data-driven equivalent dynamic linearization, as a foundational analysis tool of data-driven control methods for discrete-time nonlinear systems, is introduced in detail with motivations and distinct features. The prototype model-free adaptive control schemes by using the dynamic linearization to an unknown nonlinear plant model, as well as the alternative model-free adaptive control methods by using the dynamic linearization to an unknown ideal nonlinear controller, are discussed. Furthermore, the extensions of the dynamic linearization to unknown nonlinear repetitive systems and the corresponding model-free adaptive iterative learning control methods are also overviewed and summarized. This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers. Finally, some perspectives on data-driven control methods in information-rich age are given. [ABSTRACT FROM AUTHOR]

A model-free robust adaptive controller is proposed for control of humanoid robots with flexible joints. The proposed controller uses a time-delay estimation technique to estimate and cancel nonlinear terms in robot dynamics including disturbance torques due to the joint flexibility, and assigns desired dynamics specified by a sliding variable. A gain-adaptation law is developed to dynamically update the gain of the proposed controller using the magnitude of the sliding variable and the gain itself. The gain-adaptation law uses a leakage term to prevent overestimation of the gain value, and offers stable and chattering-free control action. The effectiveness of the proposed adaptive controller is experimentally verified on a humanoid robot equipped with flexible joints. Tracking performances of the autotuned adaptive gain are better than those of the manually tuned constant gains. The proposed control algorithm is model-free, adaptive, robust, and highly accurate. [ABSTRACT FROM AUTHOR]

The micro/nano positioning system discussed in this paper includes a piezoelectric actuator (PEA) and flexure-hinge-based positioning mechanism. Due to the existence of the hysteretic nonlinearity in the PEA and the friction in the system, the accurate positioning of the piezo-actuated positioning system calls applicable control schemes for practical applications. To this end, an implementable adaptive controller is developed in the paper, where a minimized parameterization hysteresis model is employed to reduce the computational load. The formulated adaptive control law guarantees the global stability of the controlled positioning system, and the positioning error can approach to zero asymptotically. The advantages of the proposed method making on-line implementation feasible are that the traditional inversion of the hysteresis does not need to be constructed directly; the real values of the parameters of the positioning system neither need to be identified nor measured; only the parameters in the formulation of the controller are estimated online. Comparison with the feedforward plus proportional-integral feedback control scheme is conducted and experimental results show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper investigates the adaptive tracking-control problem for a class of nonlinear systems with uncertain parameters, disturbance, and unmodeled dynamics. A novel model reference adaptive control (MRAC) with external filter is proposed to not only stabilize the error system but also guarantee its transient performance. The proposed control strategy can mathematically ensure designable L\infty norm bounds of the difference between the original model’s and the reference model’s state and their control inputs as well. On the other hand, the reference model, which is deemed as a linear time-invariant (LTI) system, can be designed to run in a desired performance level by selecting a suitable filter in the control structure. Another improvement emphasizes that the proposed approach can attain the above performance without using a high-static adaptation gain, which avoids the possible negative effects in the context of robustness. Finally, simulation on a high-accuracy motion-system model driven by a direct-current (dc) motor is given to verify the proposed approach, and the effectiveness is shown based on the simulation curves and analysis. [ABSTRACT FROM PUBLISHER]

The compound active-clamp zero-voltage switching three-phase power factor correction (PFC) converter is used as the first stage converter of the heating power supply in the monocrystalline silicon growth furnace to achieve high efficiency and high power factor. Parameter uncertainty and variation are the main problems for the controller design of the converter. The conventional methods, including proportional–integral (PI) control, exact feedback linearization (EFL) control, etc., cannot deal with such problems. In this paper, we propose a model reference adaptive control for voltage outer loop to achieve adaptation for the load and capacitor variations, as well as a variable structure control with shrinking boundary layer for current inner loop to improve robustness to the parameters uncertainty. The simulation and experiment results show that the proposed controller improves the power factor and efficiency of the system, and it has better transient and steady-state performance with respect to the load variation, as compared with the traditional PI control method and the EFL control method. [ABSTRACT FROM PUBLISHER]

This paper concerns high-accuracy tracking control for hydraulic actuators with nonlinear friction compensation. Typically, LuGre model-based friction compensation has been widely employed in sundry industrial servomechanisms. However, due to the piecewise continuous property, it is difficult to be integrated with backstepping design, which needs the time derivation of the employed friction model. Hence, nonlinear model-based hydraulic control rarely sets foot in friction compensation with nondifferentiable friction models, such as LuGre model, Stribeck effects, although they can give excellent friction description and prediction. In this paper, a novel continuously differentiable nonlinear friction model is first derived by modifying the traditional piecewise continuous LuGre model, then an adaptive backstepping controller is proposed for precise tracking control of hydraulic systems to handle parametric uncertainties along with nonlinear friction compensation. In the formulated nonlinear hydraulic system model, friction parameters, servovalve null shift, and orifice-type internal leakage are all uniformly considered in the proposed controller. The controller theoretically guarantees asymptotic tracking performance in the presence of parametric uncertainties, and the robustness against unconsidered dynamics, as well as external disturbances, is also ensured via Lyapunov analysis. The effectiveness of the proposed controller is demonstrated via comparative experimental results. [ABSTRACT FROM PUBLISHER]

In this paper, we present the vibration control design for a Euler–Bernoulli beam with the boundary output constraint. To prevent the constraint violation, a novel barrier Lyapunov function is employed for the control design and stability analysis. This paper represents an important step in extending the barrier Lyapunov function theory to distributed parameter systems. Model-based barrier control is proposed to suppress the vibration of a flexible Euler–Bernoulli beam under the boundary output constraint. Then, adaptive control is designed to handle the system parametric uncertainties. The vibration suppression is well achieved without violation of the constraint. Numerical simulations are provided to illustrate the performance of the control system. [ABSTRACT FROM PUBLISHER]

A reconfiguration control scheme is proposed for a quadrotor helicopter with loss of control effectiveness via simple adaptive control and quantum logic. Some good control performances of the helicopter can be achieved by applying quadratic optimal control with scheduled stability degree when the system is in normal operation. A parallel feedforward compensator is introduced to fulfill the condition of positive realness, and thus, the simple adaptive controller can be utilized for the helicopter. Quantum logic is also applied to the quadrotor control system, which achieves the quantization of the disturbances patterns. A mathematical model of the quadrotor helicopter for application is obtained from its dynamic equations with pitch, roll, and yaw axes, which includes the parameter perturbation and outer disturbances. Several simulation and experimental results for the faulty helicopter validate the robustness and reconfiguration control capability of the proposed scheme. [ABSTRACT FROM PUBLISHER]

This paper studies the distributed consensus problem of multiagent systems (MASs) in the presence of nonidentical unknown nonlinear dynamics and undetectable actuation failures. Of particular interest is the development of a robust adaptive fault-tolerant consensus protocol capable of compensating uncertain dynamics/disturbances and time-varying yet unpredictable actuation failures simultaneously. By introducing the virtual parameter estimation error into the artfully chosen Lyapunov function, the consensus problem is solved with a robust adaptive fault-tolerant control scheme based upon local (neighboring) agent state information. It is shown that the proposed method is user friendly in that there is no need for detail dynamic information of the agent or costly detection/diagnosis of the actuation faults in control design and implementation, resulting in a structurally simple and computationally inexpensive solution for the leaderless consensus problem of MAS. Simulation results illustrate and verify the benefits and effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a multiple adaptive observer-based strategy is proposed for the control of multi-input multi-output nonlinear processes using input/output (I/O) data. In the strategy, the pseudopartial-derivative parameter matrix of compact form dynamic linearization is estimated by a multiple adaptive observer, which is used to dynamically linearize a nonlinear system. Then, the proposed data-driven model-free-adaptive-control algorithm is only based on the online identified multiobserver models derived from the I/O data of the controlled plants, and Lyapunov-based stability analysis is used to ensure that all signals of the close-loop control system are bounded. A numerical example and a Wood/Berry distillation column example are provided to show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations. [ABSTRACT FROM PUBLISHER]

This paper proposes an adaptive control for a class of nonlinear mechanisms with guaranteed transient and steady-state performance. A performance function characterizing the convergence rate, maximum overshoot, and steady-state error is used for the output error transformation, such that stabilizing the transformed system is sufficient to achieve the tracking control of the original system with a priori prescribed performance. A continuously differentiable friction model is adopted to account for the friction nonlinearities, for which primary model parameters are online updated. A novel high-order neural network with only a scalar weight is developed to approximate unknown nonlinearities and to dramatically diminish the computational costs. Comparative experiments on a turntable servo system are included to verify the reliability and effectiveness. [ABSTRACT FROM PUBLISHER]

A novel online discrete-time parameter identification algorithm suitable for surface-mounted permanent magnet synchronous machines (SPMs) is presented in this paper. It is developed by means of the model reference adaptive system technique and the Popov Hyperstability Criterion in order to identify SPM discrete-time model parameters. In particular, good accuracy of discrete-time parameters is required by digital control systems, particularly by predictive control algorithms, which present a low robustness against parameter mismatches. Hence, an extensive simulation study is first carried out in the Matlab Simulink environment with the aim of testing the effectiveness and robustness of the proposed identification algorithm against inverter unidealities. Then, the proposed identification procedure is experimentally validated on a predictive controlled radial-flux SPM, driven by a field programmable gate arrays control board. [ABSTRACT FROM PUBLISHER]

The paper proposes a new adaptive controller design for a synchronous reluctance motor drive system. In this paper, an online tuning adaptive controller, which is based on the least-mean-square algorithm, is proposed. The adaptive controller is constructed by using an adaptive model and an adaptive control. Both the adaptive-model parameters and the adaptive-control parameters are online tuned. A nonlinear programming optimization technique is used to improve the convergence rate of the online tuning method. By using the proposed method, the transient responses, load disturbance responses, and tracking responses of the drive system are improved. The experimental results show that the implemented drive system can perform a wide range in adjustable speed from 1 to 1800 r/min and a precise position control as well. A digital signal processor, TMS-320F-28335, is used to execute the adaptive current-loop, speed-loop, and position-loop controllers. Experimental results can validate the theoretical analysis and show the feasibility and correctness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a discrete-time adaptive current controller for grid-connected pulse width modulation voltage source converters with LCL filter. The main attribute of the proposed current controller is that, in steady state, the damping of the LCL resonance does not depend on the grid characteristic since the adaptive feedback gains ensure a predefined behavior for the closed-loop current control. An overview of model reference adaptive state feedback theory is presented aiming to give the reader the required background for the adaptive current control design. The digital implementation delay is included in the model, and the stability concerning the variation of the grid parameters is analyzed in detail. Furthermore, current distortions due to the grid background voltage are rejected without using the conventional stationary resonant controllers or the synchronous proportional-plus-integral controllers. Simulation and experimental results are presented to validate the analysis and to demonstrate the good performance of the proposed controller for grid-connected converters subjected to large grid impedance variation and grid voltage disturbances. [ABSTRACT FROM PUBLISHER]

This paper presents a novel model reference adaptive control of five-phase interior-permanent-magnet (IPM) motor drives. The primary controller is designed based on an artificial neural network (ANN) to simulate the nonlinear characteristics of the system without knowledge of accurate motor models or parameters. The proposed motor drive decouples the torque and flux components of five-phase IPM motors by applying multiple-reference-frame transformation. Therefore, the motor can be easily driven below the rated speed with the maximum-torque-per-ampere operation or above the rated speed with the flux weakening operation. The ANN-based primary controller consists of a radial basis function network which is trained online to adapt system uncertainties. The complete IPM motor drive is simulated in Matlab/Simulink environment and implemented experimentally utilizing a dSPACE DS1104 controller board on a five-phase prototype IPM motor. [ABSTRACT FROM PUBLISHER]