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Redundant manipulators have been investigated and employed in various fields, and its trajectory tracking is of much importance in the field of robotic control. In this article, a model predictive control (MPC) scheme for the trajectory tracking of redundant manipulators is constructed, which minimizes the tracking error, velocity norm, and acceleration norm simultaneously. The commonly used trajectory tracking schemes for redundant manipulators, such as the minimum-velocity-norm scheme and minimum-acceleration-norm scheme, handle joint limits at different levels by introducing additional parameters, which reduces the feasible region of decision variables. In contrast, the proposed scheme directly considers these limits at three different levels, without reducing the feasible region of decision variables. In addition, to compensate for the deficiencies of most existing algorithms in noise environments, an error-summation enhanced Newton (ESEN) algorithm is proposed for solving the MPC scheme. Through theoretical analysis, it is determined that the proposed ESEN algorithm has a small steady-state error under noise conditions. Finally, in contrast with the comparative trajectory tracking schemes, as determined through computer simulations and experiments, the proposed MPC scheme solved by the ESEN algorithm not only enables the redundant manipulator to perform the trajectory tracking task in excellent fashion, but also offers advantages of high efficiency, fast responsiveness, and strong noise tolerance. [ABSTRACT FROM AUTHOR]

This article addresses the design of a path following controller for a payload tethered to a quadrotor. Specifically, the goal is to design a smooth dynamic feedback controller that forces the suspended payload to converge and follow a large class of embedded curves. The given curve is treated as a smooth manifold, and set stabilization is used to find the maximal control invariant manifold. The controller design guarantees that once the aerial robot approaches the path, the payload never leaves the path. The performance of the proposed controller is verified through simulations with practical sensor noise and parametric uncertainties. Moreover, experimental implementation and verification of the proposed control scheme are performed on a Quanser UAV platform in an indoor flying arena. [ABSTRACT FROM AUTHOR]

In this article, we propose a new iterative learning control (ILC) scheme for trajectory tracking of pneumatic muscle (PM) actuators with state constraints. A PM model is constructed in three-element form with both parametric and nonparametric uncertainties, while full state constraints are considered for enhancing operational safety. To ensure that system states are within the predefined bounds, the barrier Lyapunov function (BLF) is used in the analysis, which reaches infinity when some of its arguments approach limits. The proposed ILC incorporates the BLF with the composite energy function (CEF) approach and ensures the boundedness of CEF in the closed-loop, thus, assuring that those limits are not transgressed. Through rigorous analysis, we show that under the proposed ILC scheme, uniform convergences of PM state tracking errors are guaranteed. Simulation studies and experimental validations are conducted to illustrate the efficacy of the proposed scheme. Experimental results show that the proposed ILC satisfies the state constraint requirements and the tracking error is less than 2.5% of the desired trajectory. [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, a min-max model predictive control (MPC) method of planar motors is proposed for the first time to achieve robust precision position tracking, which has a low computational burden and strong capability to deal with the problems of stability, robustness, optimization, and input constraints. A state-space model with a homogeneous state equation is built to describe the dynamics of the time-varying reference trajectory. Combining the state-space model of the reference trajectory and that of the planar motor, an augmented state-space model is established to obtain an error state formulation. Then, using the error state formulation, a min-max optimal control problem subject to the constraints on bounded uncertainty, stability, and control input is developed. Moreover, applying the theory of asymptotically stable invariant ellipsoids and employing the nested invariant ellipsoids, the explicitly linear state-feedback control laws are obtained using a linear-matrix-inequalities based offline control algorithm. Finally, the min-max MPC is applied to a planar motor system developed in the laboratory for an experimentally comparative study. The results demonstrate the effectiveness of the proposed min-max MPC of planar motor for robust precision position tracking applications. [ABSTRACT FROM AUTHOR]

This article investigates the predefined time trajectory tracking control of uncertain nonlinear robotic systems. A radial basis function neural network (RBFNN) is used to estimate uncertainties in the robotic system dynamics. To avoid the singularity of terminal sliding-mode control (TSMC), a modified sliding variable is adopted. In order to realize that the tracking errors can converge to a small neighborhood of the origin in predefined time, within which the maximum convergence time can be adjusted by explicit parameters in advance, a nonsingular TSMC based on the RBFNN is proposed. Experiments on a ROKAE platform demonstrate the effectiveness and advantage of the proposed control method. [ABSTRACT FROM AUTHOR]

In this article, we studied an optimal formation control problem of general integrator chain multiagent systems with strongly convex local cost functions. The control goal is to design appropriate controllers for the agents such that they reach a desired formation shape and the global cost function is minimized. To achieve the goal, a modular control scheme with two modules is proposed. First, in Module I, an optimal formation signal generator is designed, whose outputs asymptotically converge to the minimizer of the global cost function. Second, in Module II, by employing the outputs into the feedback loop as the agents’ reference trajectories, some linear-tracking controllers are designed for the agents to track the references asymptotically. With both modules, the proposed control scheme realizes the optimal formation control goal asymptotically. Moreover, the proposed control scheme is also applied to solving an optimal formation control problem of multiple mobile robots, where the robots’ total “relative” weighted squared moving distance requires minimization. The simulations and experiments validate the effectiveness and practicality of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this article, we consider the time-varying trajectory tracking boundary control problem for the flexible rotation beam subject to distributed disturbance. A partial differential equation model for the flexible rotation beam system with the coupling of angle and vibration displacement is established by Hamilton principle. By introducing a servo system generated by the reference signal, a boundary controller is proposed to track the desired time-varying trajectory, which is different from the fixed angular position in most existing work. A Lyapunov-based stability analysis is presented to demonstrate that the tracking error system between the servo system and the original system is uniformly ultimately bounded. Based on the infinite dimensional operator theory, it is proved that the closed-loop system is well posed and all signals remain bounded. Numerical simulations verify the correctness of the theory and the effectiveness of the control strategy. And the experiments based on the Quanser platform demonstrate the same result as well. [ABSTRACT FROM AUTHOR]

In this article, a finite-time trajectory tracking control problem is studied for a rodless pneumatic cylinder system with disturbance in an inner outer loop framework. In the inner loop system, the generalized nonlinear extended state observer and the inner loop controller are designed to estimate and compensate the matched disturbances, respectively. The generalized nonlinear extended state observer designed in this article achieves desired convergence performance under relaxed existence conditions. In the outer-loop system, a super twisting extended state observer and an outer loop controller are designed to estimate and compensate the unmatched disturbances, respectively. The super twisting extended state observer is designed to realize finite-time stable by using the super twisting technology. Finally, experiment results are shown that finite-time control performances are obtained by the proposed control strategy. [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]

Considering the increasingly strict motion precision requirements and repetitive task characteristics of magnetic levitation systems (MLSs) in the sophisticated industry, this article proposes a novel learning adaptive sliding mode control (LASMC) strategy for the MLS to achieve an excellent tracking performance. The LASMC scheme is obtained by combining adaptive sliding mode control (ASMC) and iterative learning control (ILC) terms in a parallel structure. ASMC can guarantee system stability and strong robustness, which employs an adaptive switching gain and parameter adaption algorithm. Even if the accurate disturbance bounds of MLS are not known, the ASMC item can also adjust the switching control gain online to ensure the stability and robustness of the system. Additionally, the ILC term can further improve the MLS performance for repetitive motion tasks without the accurate dynamics model. The asymptotic stability of the LASMC strategy is verified based on the Lyapunov theorem in the presence of unmodeled dynamics, disturbances, and saturation. Comparative experiments carried out on a maglev rotary table demonstrate that the proposed control strategy achieves excellent tracking accuracy and disturbance robustness in the practical MLS. The LASMC scheme provides a decent idea for the application of intelligent control technology in the controller design of MLSs. [ABSTRACT FROM AUTHOR]

Redundant manipulators have been studied and applied in many fields. The trajectory tracking of redundant manipulators is an important topic to explore for applications. This article aims to develop a planning scheme for achieving the trajectory tracking of redundant manipulators, from the receding horizon control (RHC) perspective. For the nonlinear model of manipulators, the linearization operation is conducted to obtain predictive outputs through the forward kinematic equation. Subsequently, an RHC scheme, which minimizes tracking error, velocity norm, and acceleration norm, and directly considers joint limits at three levels as well as the terminal equality constraint, is constructed and further simplified as a convex quadratic programming problem. Furthermore, a recurrent neural network (RNN) model is designed for the constructed RHC scheme, with the help of the technique of converting inequality constraints into equality constraints. The proposed RHC scheme solved by the RNN model is compared with other existing planning schemes and solvers through computer simulations and experiments, without and with the sudden external interference. Simulation and experiment results show that the proposed RHC scheme solved by the RNN model is able to make the redundant manipulator track the given trajectory excellently, and is superior to other existing schemes and solvers in terms of high efficiency, quick-response capacity, and strong robustness. [ABSTRACT FROM AUTHOR]

In this article, we propose a solution to the problem of path following for a quadcopter aircraft with unknown vehicle parameters (mass and moment of inertia) and external disturbances. By employing the backstepping technique, the proposed adaptive control strategy guarantees the following: the quadcopter is globally steered toward, and kept within, an arbitrarily small neighborhood of a desired smooth path, achieving global uniformly ultimately boundedness; compared to trajectory tracking, a smoother convergence is obtained as the control actuation signals (thrust force and torque) are bounded with respect to the position error, and the designed timing law ensures that the desired path starts to move only when the vehicle gets close to the desired path; and a single adaptive control law can be used for accurate motion control of aerial vehicles with a wide range of inertial properties, without the need for retuning control gains or other parameters. Moreover, the controller is also made robust to external constant and slowly time-varying disturbances through the design of disturbance estimators. To demonstrate the effectiveness and performance of the proposed control strategies, simulation and experimental results are presented and analyzed. [ABSTRACT FROM AUTHOR]

Orientation-sensitive trajectory tracking (OSTT) claims that both position and orientation of the autonomous underwater vehicles (AUVs) are regulated to the reference. This article addresses the OSTT controller design problem for AUVs with separate forces or torques for surge, heave, roll, and yaw, in the presence of hydrodynamic uncertainties and external disturbances. In the kinematic stage, these AUVs are capable of moving sideways by exploiting the nonzero angle of the roll, so that the position tracking can be handled while the steering tracks the referenced yaw angle. As for the kinetic stage, a robust nonsmooth controller is implemented to guarantee the exponential convergence of velocity tracking errors. Trajectories are planned for the docking task and bridge pier inspection task. It is shown that the position tracking error can converge to a neighborhood about zero, and the yaw angle tracking error converges to zero exponentially as well. The effectiveness of the proposed method is illustrated by both simulation results and experimental results. [ABSTRACT FROM AUTHOR]

This article presents a novel control scheme that primarily provides a solution to the problem of simultaneously performing stable trajectory tracking and variable impedance control (VIC) of flexible-joint robots. The existing asymptotically stabilizing tracking controllers for compliant robots rely upon several model-based terms and their derivatives. Contrarily, the proposed minimally model-based trajectory tracking control scheme requires knowledge of only the gravitational torque vector model. Although interaction and tracking performance enhancements are achievable through VIC, its operation introduces energy injections, thereby impacting closed-loop stability/passivity. Existing VIC passivity preservation techniques deal exclusively with rigid-joint robots. To this end, the proposed method introduces the minimally model-based trajectory tracking variable impedance control scheme, which ensures passivity/stability when applying VIC to flexible-joint robots. Numerical and experimental results corroborate the controllers’ efficacy in terms of realizing VIC interaction and tracking tasks, thus enabling flexible-joint robots to stably replicate biologically inspired behaviors. [ABSTRACT FROM AUTHOR]

The article studies the trajectory tracking control problem of underactuated marine vehicles via the nonlinear model predictive control (NMPC) strategy, where practical control and state constraints present. It is a well-known challenging issue that the conventional NMPC is not applicable for underactuated marine vehicles, due to the fact that there does not exist a local static continuous state-feedback controller to stabilize the underactuated dynamics. To resolve this issue, this article proposes to construct an auxiliary time-varying tracking controller to aid terminal constraint design in the NMPC framework, where the time-varying tracking controller borrows the ideas from Lyapunov's direct method and backstepping approach. Based on this, a novel NMPC algorithm is designed to ensure trajectory tracking control of underactuated marine vehicles. Furthermore, a systematic parameter design approach is developed. Under the designed parameters, we show that the tracking error system is input-to-state stable (ISS). Finally, the effectiveness of the designed algorithm is verified by thorough simulation and hardware experiments. [ABSTRACT FROM AUTHOR]

Cooperation of connected vehicles is a promising approach for autonomous intersection control. This article presents a systematic approach to the cooperation of connected vehicles at unsignalized intersections without global coordination. A task-area partition framework is proposed to decompose the mission of cooperative passing into three main tasks, i.e., vehicle state observation, arriving time optimization, and trajectory tracking control. To accomplish these tasks, a distributed observation algorithm is introduced to achieve fixed-time observation of other vehicles’ states for passing sequence determination, a distributed optimization algorithm is introduced to schedule conflict-free arriving times for trajectory planning, and a distributed control algorithm is proposed to address parameter mismatches and acceleration saturation for fixed-time trajectory tracking control. Numerical simulations demonstrate that the proposed method can achieve cooperative passing of vehicles without global coordination at the cost of a growth of 8.8–18.1% average travel times in low and medium traffic volumes. [ABSTRACT FROM AUTHOR]

In this article, an uncertainty and disturbance estimator (UDE)-based robust output feedback control technique without using a state observer is proposed for a general class of nonlinear single-input single-output systems that are bounded-input bounded-output stable and subject to input and output disturbances. Instead of designing a controller for the original system directly, an equivalent system consisting of a first-order linear system plus a lumped uncertainty term is used to represent the input–output relationship. Then, a UDE-based robust controller is designed only using the system output feedback and the bandwidth of the open-loop system, making the design almost modeling-free. A prominent advantage of the proposed approach is that it has converted a challenging robust control problem into an intuitive filter design problem. The effectiveness of the proposed approach is validated on an experimental piezoelectric stage in the presence of hysteresis nonlinearity. [ABSTRACT FROM AUTHOR]

This article investigates the trajectory tracking control problem for a family of underactuated autonomous surface vessels under the condition of model uncertainties, actuator faults, and environmental disturbances. The emphasis or difficulty is how to ensure the trajectory tracking with specified accuracy in the fixed-time settings. A new-type fault-tolerant tracking control strategy is developed based on the marriage of a novel bounding function and a method of handling constraints. Compared with the existing results it can be noted that: first, the position error enters into a preassigned residual set in a priori designate time and remains there; second, no adaptive mechanisms, disturbance observers, or command filters are involved, leading to a simplicity control attribute. Results from a comparative simulation on the CyberShip II further illustrate the above theoretical findings. [ABSTRACT FROM AUTHOR]

This paper proposes a finite-time trajectory tracking controller for a space manipulator under model uncertainty, external disturbance, and actuator saturation. The dynamics of space manipulator is derived using Kane's method. Considering the model uncertainty that may exist when the manipulator captures an unknown payload, a radial basis function neural network (NN) is used to estimate the uncertain model of the space manipulator. An auxiliary system is designed to compensate the actuator saturation. Then a NN-based adaptive terminal sliding mode controller is proposed for trajectory tracking of a space manipulator. The stability of the proposed controller is analyzed using Lyapunov theory. Numerical simulations are conducted to verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, a challenging problem of full-state regulation control (FSRC) for an asymmetric underactuated surface vehicle (AUSV) suffering from disturbances is solved. The FSRC objective is divided into two subtasks, i.e., reaching trajectory (RT) guidance and tracking controller synthesis with underactuation and disturbances. The RT guidance is achieved by devising a circular orbit (CO) accurately passing through the desired full-state waypoint (FWP). Using a series of coordinate transformations, tracking error dynamics are shaped in a translation-rotation cascade form with respect to the CO-center frame. Using finite-time approach, lumped disturbances are accurately estimated by exact observers, which facilitate synthesizing surge and yaw controllers. By creating a new coordinate, translation subsystem is converted to a lower-triangular form. Combining with backstepping technique, cascade analysis and Lypunov approach, translation and rotation controllers are derived systematically, and render the entire closed-loop FSRC system globally asymptotically stable. Hence, the AUSV is regulated to the desired FWP. Simulation studies on a benchmark AUSV are conducted to demonstrate remarkable performance. [ABSTRACT FROM AUTHOR]

Considering input constraints and parameter uncertainty of the nonholonomic wheeled mobile robots, a new control law has been designed in this paper to solve the tracking and stabilization control problems simultaneously. The asymptotic convergence of the stabilization or tracking errors is achieved by a smooth controller with time-varying feedback parameters subject to set conditions. A geometric-based parameter design strategy is employed to overcome the difficulties resulting from input constraints. With the parameter design strategy, the designed parameters satisfy the set conditions and the control inputs stay in the constrained domain. Experimental studies have been conducted to verify the effectiveness of the proposed control law. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of asymptotic trajectory tracking with prescribed transient behavior for a fully actuated marine surface vessel subject to unmodeled dynamics and external disturbances. A prescribed transient performance constraint is introduced to specify the transient behavior of the output tracking errors. Neural network (NN) approximation and adaptive control techniques are employed to compensate for the unmodeled dynamics. In general, the NN approximation errors always exist in the NN-based adaptive control systems, and typically result in uniformly ultimate boundedness, where the tracking errors cannot be stabilized asymptotically. In order to obtain an asymptotic tracking performance, a robust integral of the sign of the error (RISE) feedback term is introduced in feedback control design to compensate for the NN approximation errors and external disturbances. Consequently, RISE-based adaptive NN controller with compensations of unmodeled dynamics and external disturbances is proposed such that an asymptotic tracking performance is achieved with guaranteeing prescribed transient behavior. The performance of the proposed tracking controller is shown by simulation results. [ABSTRACT FROM AUTHOR]

This paper studies the trajectory tracking control problem of an autonomous underwater vehicle (AUV). We develop a novel Lyapunov-based model predictive control (LMPC) framework for the AUV to utilize computational resource (online optimization) to improve the trajectory tracking performance. Within the LMPC framework, the practical constraints, such as actuator saturation, can be explicitly considered. Also, the thrust allocation subproblem can be addressed simultaneously with the LMPC controller design. Taking advantage of a nonlinear backstepping tracking control law, we construct the contraction constraint in the formulated LMPC problem so that the closed-loop stability is theoretically guaranteed. Sufficient conditions that ensure the recursive feasibility, and hence the closed-loop stability, are provided analytically. A guaranteed region of attraction is explicitly characterized. In the meantime, the robustness of the tracking control can be improved by the receding horizon implementation that is adopted in the LMPC control algorithm. Simulation results on the Saab SeaEye Falcon model AUV demonstrate the significantly enhance trajectory tracking control performance via the proposed LMPC method. [ABSTRACT FROM PUBLISHER]

This paper aims to provide a clear explanation of the role of the footprint of uncertainty (FOU) parameters on the control signal generation and, thus, to increase the interpretability of specially structured interval type-2 (IT2) fuzzy logic controllers, namely single input IT2 fuzzy PID (SI-IT2-FPID) controller. In this context, we extend the analysis performed for SI-IT2-FPID controllers by providing the effect of the FOU parameters on control surface (CS) generation. We show that, by only adjusting a single parameter that shapes the FOU, it is possible to generate commonly employed CSs without a requirement of an optimization assistance. In order to validate our theoretical analysis, we present comparative real world quadcopter flight tests. The real-time experimental results show that the SI-IT2-FPID controller can achieve better control performance in the presence of uncertainties and strong wind conditions when compared to its type-1 and conventional counterparts. We believe that the results of this study will open the doors to a wider use of SI-IT2-FPID controllers in real world control applications as the proposed structure is easy to design and feasible to deploy, especially in real-time control systems. [ABSTRACT FROM PUBLISHER]

High-speed robots are being applied more and more widely in the scenarios of aviation manufacturing, electronic packaging, and three-dimensional printing, etc. To achieve high speed, the robots are typically designed to be lightweight, hence suffer from undesired vibration, so that the dynamic performance would deteriorate. In light of this, this paper considers the dynamic modeling and active control of an emerging redundant parallel robot. First, the recursively dynamic model is modularly established by means of Lagrangian multipliers method. For completeness, the model of permanent magnet synchronous motor is further integrated with the model of mechanical system to formulate the electromechanically coupled dynamic model (ECDM). To facilitate dynamic control, the ECDM is cleverly projected into the null space of constraint Jacobian matrix eliminating the need to compute Lagrangian multipliers explicitly. Based on singular perturbation approach and Tikhonov theorem, the ECDM is decomposed into two subsystems in different timescales. On this basis, a nonlinear hybrid control scheme is proposed for both trajectory tracking and vibration suppression. Numerical simulations suggest that the nonlinear hybrid control exhibits overwhelming performance than the conventional joint-based proportional and differential feedback control. [ABSTRACT FROM AUTHOR]

A method is proposed to solve the image-based control of a quadrotor Unmanned Aerial Vehicle (UAV) by directly planning and tracking in image space. First, by adopting the virtual camera approach and choosing image moments, which are defined in the virtual image plane, as image features, we design the trajectories of the image features in image space to perform the image-based visual control task of the quadrotor. Then, a feature's trajectory tracking controller is proposed to track the designed trajectories. The stability of the proposed tracking controller is analyzed and proved by means of Lyapunov analysis. In addition, improved visibility during the image-based visual servoing process is achieved with this method. Simulation results are provided to show the feature's trajectory tracking performance. Finally, real-world experiments are conducted to validate this method. [ABSTRACT FROM AUTHOR]

In this paper, the robust trajectory tracking design of uncertain nonlinear systems is investigated by virtue of a self-learning optimal control formulation. The primary novelty lies in that an effective learning based robust tracking control strategy is developed for nonlinear systems under a general uncertain environment. The augmented system construction is performed by combining the tracking error with the reference trajectory. Then, an improved adaptive critic technique, which does not depend on the initial stabilizing controller, is employed to solve the Hamilton–Jacobi–Bellman (HJB) equation with respect to the nominal augmented system. Using the obtained control law, the closed-loop form of the augmented system is built with stability proof. Moreover, the robust trajectory tracking performance is guaranteed via Lyapunov approach in theory and then through simulation demonstration, where an application to a practical spring–mass–damper system is included. [ABSTRACT FROM PUBLISHER]

This paper develops a novel integral sliding mode controller (ISMC) for a general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured velocities, unknown disturbances, and uncertain hydrodynamics of the robot have been successfully solved in the control design. An adaptive MIMO-ESO is designed not only to estimate the unmeasurable linear and angular velocities, but also to estimate the unknown external disturbances. An ISMC is then designed using Lyapunov synthesis, and an adaptive gain update algorithm is introduced to estimate the upper bound of the uncertainties. Rigorous theoretical analysis is performed to show that the proposed control method is able to achieve asymptotical tracking performance for the underwater robot. Experimental studies are also carried out to validate the effectiveness of the proposed control, and to show that the proposed approach performs better than a conventional potential difference (PD) control approach. [ABSTRACT FROM AUTHOR]

Ultrahigh-precision tracking in nanomanipulations poses major challenges for mechanical design as well as servo control, due to the general confliction between the precision requirement and large stroke tracking. The situation is further complicated by input saturation, which is almost inevitable for microactuators. This paper presents a novel control architecture combining a parallel internal-model-based tracking design and a robust antiwindup control structure, such that asymptotic tracking can be achieved for nanoservo systems in the presence of saturation nonlinearity and model uncertainties. For the augmented system with internal-model dynamics, an I/O-based equivalent representation from control (free of saturation) to system output is derived by incorporating the dead-zone nonlinearity, saturation compensation blocks, as well internal-model units. The robustness condition on the saturation compensator is also derived based on the sector bound criterion and an \mathcal H_\infty-optimal design is developed accordingly. The proposed robust antiwindup tracking control architecture is deployed on a customize-designed nanostage driven by a piezoelectric (PZT) actuator, where numerical simulations and real-time experiments demonstrate excellent tracking performance and saturation compensation capability, achieving tracking precision error less than $0.23\%$. [ABSTRACT FROM PUBLISHER]

In this paper, a walking robot that consists of its body and six legs is considered for trajectory control. The leg mechanism is a 1-UP&2-UPS (U-universal joint, P-prismatic joint, and S-spherical joint) parallel mechanism with the arm–leg conversion function. Due to the nonlinearity and uncertainties of the robot, it is difficult to obtain its exact dynamic model, therefore this paper utilizes the decentralized control strategy to design a dynamic hybrid controller, in which the outer loop is an impedance controller, the middle and inner loops are designed by using the sliding-mode control and the nonlinear proportional–integral–derivative (PID) control, respectively. The inverse kinematics of one leg is derived for mathematic model, based on which three control methods including the classical PID control, position–velocity loops cascade control, and dynamic hybrid control are examined. Contrast tracking experiments were performed with one robotic leg. The experimental results are shown to identify their differences and prove the effectiveness and applicability of the proposed dynamic hybrid control. [ABSTRACT FROM PUBLISHER]

This paper presents a robust adaptive iterative learning control (ILC) algorithm for 3-DOF permanent magnet (PM) spherical actuators to improve their trajectory tracking performance. The dynamic model of a PM spherical actuator is a multivariable nonlinear system with interaxis coupling terms. Uncertainties such as modeling errors, loads, and external disturbances exist in the model unavoidably, which will affect the performance, including the precision of the control system. Hence, to compensate for these uncertainties, a new hybrid control scheme that consists of a proportional–derivative (PD) feedback control with varying gains, a PD-type ILC with adjustable gains, and a robust term is developed. The new control law combines the advantages of simplicity and easy design of the PD control, the effectiveness of the ILC to handle model uncertainties and repetitive disturbances, and the robustness of the robust term to random disturbances. In addition, to expedite the convergence rate, the gains in the PD feedback control and the PD-type ILC are adaptively adjusted according to the iteration times. It is shown that the system tracking error approaches zero as the number of iterations increases. To demonstrate the proposed algorithm and verify the established theoretical results, both simulations and experiments are conducted. The results have shown that the proposed control algorithm can effectively compensate for various uncertainties and can thus improve the trajectory tracking performance of spherical actuators. [ABSTRACT FROM AUTHOR]

This paper deals with the control of differentially driven wheeled mobile robots. Two families of wheeled mobile robots are considered: those that are capable of forward motion only and those that can perform forward and backward motion. A unified framework for the control law analysis and design of both robot types is proposed. The control laws are developed within a Lyapunov stability analysis framework. Periodic Lyapunov functions are proposed, and the constructive procedure leads to periodic control laws. These laws are very natural for wheeled mobile robots since they are inspired by the periodic nature of a robot's orientation. The simple form of the control laws enables their easy implementation in practical applications. Global convergence is proven based on the usual requirements for reference velocities. Some important properties of these systems are also treated, such as continuity and the presence of unstable equilibria. Some guidelines for how to choose a suitable control law and its parameters are also given. An extensive simulation study was performed, and the results of the proposed control laws are compared with some control laws from the literature. The algorithms were also validated using the Mirosot-type robot soccer robot and a vision-based system. [ABSTRACT FROM PUBLISHER]

The navigation for a car-like wheeled robot (CLWR) in a distributed active-vision network-space system (DAVNSS) subject to three fuzzy variable-structure decentralized controls (FVSDC5) is developed. The scheme also includes trajectory tracking and obstacle avoidance. Two distributed wireless charge- coupled-device (CCD) cameras individually driven by two stepping motors, viz., active CCD1 and active CCD2 (or simply ACCD1 and ACCD2), are constructed to capture the dynamic pose of the CLWR and the obstacle. The proposed control system includes quad processors with multiple sampling rates. First, a personal computer (PC) is employed to receive the image of the CLWR or obstacle from ACCD1 or ACCD2 by a wireless transmitter and then to plan three reference commands for CLWR, ACCD1, and ACCD2. Next, a six-step image-processing routine and the calibration between the world coordinate and the image plane coordinate using multilayer perceptrons (MLPs) are established. Finally, experiments are performed to validate the proposed control system. [ABSTRACT FROM AUTHOR]

Medium voltage ac machines fed by high-power inverters operate at a low switching frequency to restrain the switching losses of power semiconductor devices. Particular care is thus required in the design of the drive control system. The signal delay caused by low switching frequency operation increases undesired cross-coupling effects in vector-controlled schemes. These are not sufficiently compensated by established methods like feedforward control. Improvements are achieved by a more accurate modeling of the machine and the inverter. An adequate controller is introduced, having a transfer function with complex coefficients. The high harmonic distortion due to the low switching frequency is a tradeoff. Using synchronous optimal pulsewidth as an alternative permits reducing the switching frequency without increasing the harmonics. The detrimental effects of conventional control methods are eliminated by forcing the harmonic components on an optimal spatial trajectory. Deadbeat behavior and complete decoupling are thus achieved. The performance of the aforementioned schemes is compared based on mathematical analyses and experimental results. [ABSTRACT FROM AUTHOR]