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In this paper, the problem of highly performance motion control of tank bidirectional stabilizer with dead zone nonlinearity and uncertain nonlinearity is addressed. First, the electromechanical coupling dynamics model of bidirectional stabilizer is developed finely. Second, the dead zone nonlinearity in bidirectional stabilizer is characterized as the combination of an uncertain time-varying gain and a bounded disturbance term. Meanwhile, an adaptive robust controller with dead zone compensation is proposed by organically combining adaptive technique and extended state observer (ESO) through backstepping method. The adaptive technique is employed to reduce the impact of unknown system parameter and dead zone parameter. Furthermore, the ESO is constructed to compensate the lumped uncertainties including unmodeled dynamics and dead zone residual, and integrated together via a feedforward cancellation technique. Moreover, the adaptive robust control law is derived to ensure final global stability. In stability analysis, the asymptotic tracking performance of the proposed controller can be guaranteed as the uncertainty nonlinearities in tank bidirectional stabilizer are constant. It is also guaranteed to achieve bounded tracking performance when time-varying uncertainties exist. Extensive co-simulation and experimental results verify the superiority of the proposed strategy.
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Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system's bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system's resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems.
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This paper presents a two-loop control framework for robotic manipulator systems subject to state constraints and input saturation, which effectively integrates planning and control strategies. Namely, a stability controller is designed in the inner loop to address uncertainties and nonlinearities; an optimization-based generator is constructed in the outer loop to ensure that state and input constraints are obeyed while concurrently minimizing the convergence time. Furthermore, to dramatically the computational burden, the optimization-based generator in the outer loop is switched to a direct model-based generator when the tracking errors are sufficiently small. In this way, both a high tracking accuracy and fast dynamic response are obtained for constrained robotic manipulator systems with considerably lower computational burden. The superiority and effectiveness of the proposed structure are illustrated through comparative simulations and experiments.
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The widespread use of wheeled mobile robots (WMRs) in many fields has created new challenges. A critical issue is wheel slip, which, if not accurately determined and controlled, causes instability and deviation from the robot's path. In this paper, an intelligent approach for estimating the longitudinal and lateral slip of wheels is proposed that can effectively compensate for the negative effects of slippage. The proposed algorithm relies on three regression networks to estimate the longitudinal slip ratio of the right and left wheels and sideslip angle on terrains with different friction coefficients. The datasets collected during tests on different surfaces with various maneuvers are used to train the artificial neural networks (ANNs). A developed dynamic model of a WMR considering wheel slip and modified traction force is presented. The adaptive robust controller, based on sliding mode control (SMC), is introduced to deal with the problems related to slipping, unknown uncertainties, and disturbances. The simulation results demonstrate that the presented controller has better performance than SMC in handling external disturbances and uncertainties, which leads to reduction in tracking error and faster convergence to zero. The proposed controller with an intelligent slip estimator, has been applied to a four-wheel mobile robot to demonstrate its effectiveness and feasibility. The high accuracy of slip estimation in the mentioned intelligent algorithm has resulted in the presented method being on average 26% more effective in reducing the tracking error than the control method without slip compensation in each test for circular trajectory.
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Wheeled mobile robots (WMRs) have a wide range of applications in logistics transportation and industrial productions, among which the motion control has always been one of the hot spots in the current WMR researches. However, most of previous designed controllers assumed that the WMR motion had no slippage. Ignoring the slippage factors usually results in a decrease in control performance and even leads to unstable motion. To address such a challenge, a kinematic model with differential flatness is established through dynamic feedback-linearization, which comprehensively considers the multidirectional slippage of mobile robot, including longitudinal and steering slippage. Subsequently, benefited from the one-to-one mapping of states and inputs to flat outputs in differential flat system, an adaptive robust control (ARC) method is proposed to stabilize the system. Different from previous robust control studies, even if the knowledge of the upper bound of system uncertainties is unknown in advance, the proposed adaptive robust controller can still achieve satisfying performance by adaptive estimation of the upper bound of system uncertainties. The effectiveness and feasibility of the proposed method are confirmed by comparative experiments on WMR with slippage disturbance.
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An air-ground heterogeneous unmanned swarm system coordination is considered. The system consists of N unmanned aerial vehicles (UAVs) and one unmanned ground vehicle (UGV). This forms a complicated mission, which consists of the following four different tasks. First, the aerial vehicles are in a compact formation, while avoiding collision with each other. Second, the aerial vehicles should stay close to the ground, while avoiding collision with the ground. Third, the aerial vehicles should stay close to the ground vehicle. Fourth, the ground vehicle should follow a desired trajectory. These tasks reflect two seemingly contradictory nature: close to (due to tracking) and away from (due to avoidance). The effective control design should address all four tasks even in the presence of uncertainty. By two creative transformations, this multitude of tasks are consolidated in a χ-measure. An adaptive robust control, which includes a robust control scheme and an online adaptation law, is then proposed to render guarantee boundedness performance of this χ-measure. As a result, the control design is able to accomplish the combined tracking-avoidance mission for the uncertain swarm system. Despite the presence of conflicting aspects between these tasks, the designed controller exhibits outstanding performance.
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This paper proposes a method for high-performance motion control of the dual-valve hydraulic system subject to parameter and model uncertainties, unknown proportional valve dead-zone, and servo valve fault. By constructing a detailed dual-valve fault system model (DFSM), a disturbance observer-based adaptive robust fault-tolerant controller is proposed via the backstepping method. This controller integrates a model-based fault detection algorithm for real-time fault monitoring and subsequent controller reconfiguration. Additionally, the DFSM-based adaptive robust control (ARC) technique is applied to handle the unknown dead-zone problem and other nonlinearities, ensuring precise control. Once the servo valve fault occurs, a nonlinear observer estimates the fault and collaborates with the ARC to establish a reconfigured controller, thereby maintaining motion control. The effectiveness of the proposed method has been experimentally verified.
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Due to the underactuated and unstable nature, the control of unmanned bicycle robots is a challenge. The presence of unknown and time-varying uncertainties in the system adds to the difficulty of control for the unmanned bicycle robot. From the perspective of constraint-following, this paper proposes a new control approach to address uncertainties in the unmanned bicycle robot system. The uncertainties of the unmanned bicycle robot system are constrained within certain limits, but the bounds are unknown, which can pose a challenge for designing a control approach. Through the implementation of a leakage-type adaptive law that modifies the control system in accordance with tracking errors, the potential exists to confine the boundaries of uncertainties. The performance of uniform boundedness and uniform ultimate boundedness is shown by the Lyapunov stability theory. Numerical simulation of a representative case is performed to illustrate the effectiveness of the proposed control.
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In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM.
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The paper proposes a formation tracking control method for the uncertain artificial swarm systems under the inequality constraints. Not only can the agents perform swarm behaviors (e.g., convergence, formation and avoidance of collision), but they can also track the fixed targets in a constrained area (which is formulated as the inequality constraints, such as unilateral constraint and bilateral constraint.). The swarm behaviors are creatively considered as the servo constraints or the control objectives for the swarm agents. Based on the Udwadia-Kalaba (U-K) equation, those prescribed behaviors are realized by a model-based control design (that is the servo constraints force model-based feedforward control). To deal with the inequality constraints in the formation tracking process, a differential homeomorphism transformation is used to relieve the environmental constraints for the swarm agents. Moreover, the uncertainty of the swarm agents (i.e., the parameter uncertainty in modeling and the external disturbances) is considered, which is time-varying and unknown (but bounded). An uncertainty estimation method with dead-zone and leakage term is designed to calculate the possible upper bound of the uncertainty. In virtue of the estimated upper bound of the uncertainty, a robust control is designed for the uncertain swarm agents to obey the prescribed swarm behaviors in the formation tracking task. The system performances of the artificial swarm systems under the proposed control are theoretically guaranteed by a range of rigorous theorems and numerically verified by the simulations of three agents.
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Online critic learning or solving robust control problems of complex systems usually requires knowledge about system dynamics. In order to achieve these goals in data-driven method, a new performance index related to the decreasing rate of the conventional cost is designed. The corresponding optimal control policy can be approximated online using a new actor-critic scheme with three neural networks, without depending on initial stable control and knowledge about system dynamics. The learning process and the learned control policy show excellent robustness. Numerical simulations and an inverted pendulum experiment show that compared with benchmark methods, the proposed method relaxes the dependence on initial admissible control and exhibits better disturbance attenuation performance.
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For strict-feedback nonlinear systems (SFNSs) with unknown control direction, this paper synthesizes an asymptotic tracking controller by a combination of the dynamic surface control (DSC) technique, the Nussbaum gain technique (NGT) and fuzzy logic systems (FLSs). The SFNSs under study feature unknown nonlinear uncertainties and external disturbances. By utilizing the DSC technique with nonlinear filters, the issue of 'differential explosion' is obviated, in which the adaptive laws are constructed to conquer the effect of unknown functions. The FLSs are exploited to cope with uncertainties without any prior conditions of the ideal weight vectors and the approximation errors. In addition, by introducing the NGT, the unknown control direction problem is solved. Compared with the existing results, the proposed design procedure is able to simultaneously overcome the 'differential explosion' and the unknown control direction problems, and asymptotic tracking is accomplished. At the end, a second-order numerical system and a more realistic Norrbin nonlinear mathematical model are applied to confirm the feasibility of the design procedure.
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In the presence of system coupling and dynamic uncertainties, extensive research has been conducted on the precise motion control of industrial manipulators with general reference trajectories. Since repetitive operations are common tasks in industrial applications, it is an essential and practical problem to further improve the control accuracy by taking advantage of the periodicity of the reference trajectory. In this paper, a desired compensation adaptive robust repetitive control is proposed for multi-DoFs industrial manipulators to perform repetitive tasks. Specifically, the link dynamics identified offline is compensated directly to decouple the system and capture the main characteristics of the link effect. Then, the uncertain friction is dealt with through an online adaptation scheme, in which the desired compensation is utilized to avoid measurement noise and chattering at low speed. And periodic disturbances are approximated by Fourier series expansion with unknown Fourier coefficients, which will be learned online. Finally, the robust feedback is designed to guarantee transient control accuracy and robustness against dynamic uncertainties. Comparative experiments on an industrial manipulator show that the proposed controller possesses better transient and steady-state control accuracy and error convergence rate.
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Recently, formation flying of multiple unmanned aerial vehicles (UAVs) found numerous applications in various areas such as surveillance, industrial automation and disaster management. The accuracy and reliability for performing group tasks by multiple UAVs is highly dependent on the applied control strategy. The formation and trajectories of multiple UAVs are governed by two separate controllers, namely formation and trajectory tracking controllers respectively. In presence of environmental effects, disturbances due to wind and parametric uncertainties, the controller design process is a challenging task. This article proposes a robust adaptive formation and trajectory tacking control of multiple quad-rotor UAVs using super twisting sliding mode control method. In the proposed design, Lyapunov function-based adaptive disturbance estimators are used to compensate for the effects of external disturbances and parametric uncertainties. The stability of the proposed controllers is guaranteed using Lyapunov theorems. Two variants of the control schemes, namely fixed gain super twisting SMC (STSMC) and adaptive super twisting SMC (ASTSMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, it is verified that in presence of disturbances, the proposed ASTSMC controller exhibits enhanced robustness as compared to the fixed gain STSMC.
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The structure of the cable-driven serial manipulator (CDSM) is more complex than that of the cable-driven parallel manipulator (CDPM), resulting in higher model complexity and stronger structural and parametric uncertainties. These drawbacks challenge the stable trajectory-tracking control of a CDSM. To circumvent these drawbacks, this paper proposes a robust adaptive controller for an n-degree-of-freedom (DOF) CDSM actuated by m cables. First, two high-level controllers are designed to track the joint trajectory under two scenarios, namely known and unknown upper bounds of uncertainties. The controllers include an adaptive feedforward term based on inverse dynamics and a robust control term compensating for the uncertainties. Second, the independence of control gains from the upper bound of uncertainties and the inclusion of the joint viscous friction coefficient into the dynamic parameter vector are realised. Then, a low-level controller is designed for the task of tracking the cable tension trajectory. The system stability is analysed using the Lyapunov method. Finally, the validity and effectiveness of the proposed controllers are verified by experimenting with a three-DOF six-cable CDSM. In addition, a comparative experiment with the classical proportional-integral-derivative (PID) controller is carried out.
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In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.
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To achieve a high performance synchronized motion trajectory tracking of the hydraulic press slider-leveling electrohydraulic control system, an adaptive robust cross-coupling control strategy that incorporates the cross-coupling approach into adaptive robust control (ARC) architecture has been proposed. The primary objective of this study was describe that the nonlinear ARC controller together with a cross-coupling control (CCC) controller was integrated to solve the slider-leveling synchronization control system using four axes. A discontinuous projection-based ARC controller was constructed. A robust control method with dynamic compensation type fast adaptation was introduced to attenuate the effects of parameter estimation errors, unmodeled dynamics and disturbances, and improved the transient tracking performance of the system. The stability of the controller was proven by Lyapunov theory and the trajectory tracking error asymptotically convergences to zero. The simulation of a desired reference trajectory was included. The max tracking error of the proposed ARC controller of single axis was kept within-0.06 mm. The trajectory tracking error asymptotically converges to zero, which guaranteed the system would possess good transient behavior and confirmed the stability performance of the control system. The four axes synchronous errors of reference trajectory with cross-coupling controller indicated the maximum synchronization error of the proposed ARC + CCC controller between axis was within ±0.1 mm. The ARC together with a CCC controller for four hydraulic cylinders used parameter adaptation to obtain estimates of model parameters for reducing the extent of parametric uncertainties, and used a robust control law to attenuate the effects of parameter estimation errors, unmodeled dynamics, and disturbances. This study result shows that the proposed cross-coupling synchronization control scheme, together with the ARC law, provides excellent synchronization motion performance in a control system with four axes.
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To solve the control problem of nonstrict feedback nonlinear systems, a backstepping technique based controller design method is developed by integrating a robust control law with the excellent parameter identification algorithm of indirect adaptive framework. Each of unknown system functions that contain whole states is approximated by adding together a bounded time-varying parameter and a fuzzy approximator related to the current step and previous states only in the backstepping design procedure, which solves the algebraic loop problem existing in nonstrict feedback systems. Then the command filter is combined with the adaptive backstepping to construct the robust control law by which the differentiation operation of the virtual control signal can be avoided. Subsequently the swapping scheme is used to convert the studied system into a linear time-varying form. Both the weight vector of the fuzzy logic system and the bounded time-varying parameter are estimated by a least-squares identification algorithm under relaxed excitation conditions, so that the accurate value of system functions can be obtained. All signals in the closed-loop system are proved to be bounded. A simulation example is put forward to verify effectiveness of the presented method.
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An adaptive robust controller with non-local memory hysteresis force compensation is investigated for the precision tracking control of pneumatic artificial muscle (PAM). The proposed controller presents a two-layer cascade structure, and each layer has an adaptive law part and a robust control law part. A modified operator based Prandtl-Ishlinskii (PI) model is employed in the development of the robust control algorithm with the hysteresis feedback linearization compensation. Moreover, in the robust control law part, the problem of unbounded uncertain nonlinearities introduced by the hysteresis force term is addressed by applying an on-line monitoring method. In the adaptive law part, model parameters including weights of the modified operator are updated online by the recursive least squares estimation (RLSE) method, and then the effect of the hysteresis non-local memory characteristic is further attenuated. The tracking error is guaranteed to converge to a small residual set. Comparative experimental results demonstrate the significance of the non-local memory hysteresis force compensation, then, a desired precision can be guaranteed.
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In this research, to achieve the altitude and attitude tracking control of an underactuated quadrotor UAV with mismatched uncertainties, based upon Udwadia-Kalaba theory, a novel adaptive robust tracking control approach is proposed and which will be designed in two steps. First, aiming at the uncertain and underactuated quadrotor UAV, regardless of initial constraint deviation and mismatched uncertainties, a nominal control is constructed through transforming the desired trajectories into corresponding servo constraints; second, for the mismatched uncertainties, we decompose them into two parts, i.e. the matched part and mismatched part, and the mismatched part will "vanish" during the stability analysis of proposed adaptive robust controller. Eventually, with such a decomposition technique, the large mismatched uncertainties can be addressed properly and the burden of controller design will be reduced to a certain degree. In addition, two deterministic robust control performances are also guaranteed by our proposed approach. The simulation results have shown a good robustness and tracking precision of our proposed scheme for quadrotor UAV.