Robust Adaptive Control Scheme Research Articles

Adaptive robust control of electromagnetic actuators with friction nonlinearity and uncertainty compensation

Friction nonlinearity and uncertainty are the main factors affecting the highly performance control of electromagnetic actuators. In this paper, a nonlinear adaptive robust control strategy is proposed of electromagnetic actuators with friction nonlinearity and uncertainty compensation. First, the dynamical model of the electromagnetic actuator is established considering nonlinearity and uncertainty. Then, an adaptive robust controller is designed based on the continuously differentiable friction model to ensure that the control input is continuously and bounded. In the design of the controller, the unfavorable effects of unknown parameters in the electromagnetic actuator are eliminated by constructing a parameter adaptive law. Meanwhile, in order to improve the tracking accuracy of the electromagnetic actuator, a nonlinear robust control law is designed to ensure the robustness of the controller. The stability analysis by Lyapunov function shows that the asymptotic tracking effect can be obtained when only parameter uncertainty exists in the closed-loop system of the electromagnetic actuator, and the consistent bounded stability can be ensured when the system also exists uncertain nonlinearity. Extensive comparative results verify the effectiveness of the proposed control method.

Game theory based finite-time formation control using artificial potentials for tethered space net robot

The Tethered Space Net Robot (TSNR) is an innovative solution for active space debris capture and removal. Its large envelope and simple capture method make it an attractive option for this task. However, capturing maneuverable debris with the flexible and elastic underactuated net poses significant challenges. To address this, a novel formation control method for the TSNR is proposed through the integration of differential game theory and robust adaptive control in this paper. Specifically, the trajectory of the TSNR is obtained through the solution of a real-time feedback pursuit-evasion game with a dynamic target, where the primary condition is to ensure the stability of the TSNR. Furthermore, to minimize tracking errors and maintain a specific configuration, a robust adaptive formation control scheme with Artificial Potential Field (APF) based on a Finite-Time Convergent Extended State Observer (FTCESO) is investigated. The proposed control method has a key advantage in suppressing complex oscillations by a new adaptive law, thus precisely maintaining the configuration. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed scheme.

Cascade NMPC-PID control strategy of active heave compensation system for ship-mounted offshore crane

In this paper, the problem associated with active heave motion compensation during heavy-lift operations performed by ship-mounted offshore cranes beneath rough marine environment is investigated. To effectively decouple the correlated motion of the suspended payload from the support vessel via the secondary regulated active heave compensation (SRAHC) system, a novel hierarchical control strategy is proposed by integrating a cascade control structure with an adaptive robust control scheme that incorporates a vertical motion forecast algorithm based on autoregressive integrated moving average (ARIMA) model, a conventional proportional-integral-derivative (PID) framework, and a nonlinear model predictive control (NMPC) method. The introduction of the cascade structure is motivated by its prompt error response against control lag and efficient system order reduction for computational burden alleviation, which forms a crucial foundation for the NMPC-based real-time regulation of PID gains, ensuring optimal evolution of the SRAHC system while improving its practical feasibility. Furthermore, the enhancements in heave compensation and trajectory tracking performance, noise resistance, constraint satisfaction, and engineering application potential of the proposal are demonstrated through a thorough comparative analysis conducted in a practical co-simulation research system.

Constraint-following adaptive robust control for multi-vehicle system formation with collision avoidance

For the formation on unstructured road with complex road conditions, the rationality of constraint design and uncertainty compensation seriously affect the connected and autonomous vehicle (CAV) system performance. The system constraints are studied for safe and efficient formation driving, and an adaptive robust formation control scheme is proposed. First, for the desired configuration and global collision avoidance, a series of equality and unilateral inequality constraints are designed for inter-vehicle spacing. Second, a constraint handling scheme is proposed based on the diffeomorphism method, adjustable transformation function, and bounded constraint. Third, (possibly fast) time-varying but bounded multi-source uncertainty is considered, and then a self-adjusting leakage-type adaptive law is proposed to estimate the integrated uncertainty bound online. Fourth, the performance measure β is defined to evaluate the constraint-following error, and an adaptive robust control is proposed. The effectiveness of the proposed control scheme is verified by simulation. The results show that the proposed control renders the error to be uniformly bounded and uniformly ultimately bounded. The CAV system approximately follows the constraints, regardless of the uncertainty.

Robust adaptive leaderless consensus of unknown non-minimum phase linear multi-agent systems subject to disturbances and/or unmodeled dynamics

This article investigates the problem of robust adaptive leaderless consensus for heterogeneous uncertain non-minimum phase linear multi-agent systems over directed communication graphs. Each agent is assumed to be of unknown nominal dynamics and also subject to external disturbances and/or unmodeled dynamics. A novel distributed robust adaptive control strategy is proposed. It is shown that the robust adaptive leaderless consensus problem is solved with the proposed control strategy under some sufficient conditions. Two examples are provided to demonstrate the efficacy of the proposed control strategy.

Nonlinear Robust Adaptive Sliding Mode Control Strategies Involve a Fractional Ordered Approach to Reducing Dengue Vectors

This study presents an innovative approach by integrating adaptive sliding mode control strategies with fractional order modeling to address the challenge of reducing Aedes aegypti mosquito populations, the primary vector of Dengue - a widespread and debilitating disease. By employing the Atangana-Baleanu-Caputo fractional operator to model the dynamics of the mosquito population, we achieve a more precise representation of complex and non-linear behaviors. The motivation behind adopting the Adaptive Sliding Mode Control (ASMC) approach lies in the critical need to efficiently control Aedes aegypti mosquito populations, a key step in combating the prevalence of dengue. The ASMC method dynamically adjusts control parameters based on evolving conditions, enhancing its adaptability to the changing dynamics of mosquito populations.The Lyapunov stability theorem ensures the reliability of tracking convergence and control structure. Additionally, we implement the Toufik Atangana method to solve both state and adjoint fractional differential equations using the ABC derivative operator. This incorporation adds a novel dimension to the study, providing a comprehensive framework for addressing the intricate dynamics inherent in the Aedes aegypti mosquito population. To assess the effectiveness of the proposed strategy, a numerical performance index is introduced at the end of the abstract. This index justifies the controller’s efficacy by comparing it to other conventional controllers. The inclusion of this quantitative measure reinforces the significance of the proposed strategy in the context of dengue prevention and control efforts.

Dual cerebella model neural networks based robust adaptive output feedback control for electromechanical actuator with anti‐parameter perturbation and anti‐disturbance

SummaryTo realize a high‐accuracy tracking control of an electromechanical actuator in which only position signal is available, a new robust adaptive output feedback control strategy based on dual CMAC neural networks is proposed in this article. A high‐gain observer and a neural network are combined to estimate the unmeasured system states, in which a neural network is designed to estimate and compensate the system parameter estimation error and other uncertain nonlinearity to improve the performance of the traditional observer. A robust adaptive controller and another neural network are combined to decrease the impact of parameter perturbation and external disturbance and strengthen the system robustness. Lyapunov theorem is used to derive an on‐line weight adaption law for the two neural networks and an on‐line parameter adaptation law for the uncertain parameters to stabilize the system. Thus the parameter uncertainty and disturbance can be compensated with feedforward compensation technique. A nonlinear robust feedback term is designed to suppress the model compensation deviation. Considering the stronger approximation ability of CMAC basis function against RBF basis function, an improved CMAC neural network is introduced in the proposed controller. A large number of simulations and experiments show that the proposed controller has better tracking performance than many main‐stream output feedback controllers in present.

Minimal learning parameters-based simplified robust adaptive saturated control for dynamic positioning ship with input magnitude and rate saturations

In this study, a simplified robust adaptive saturated control strategy based on minimal learning parameters is developed for the dynamic positioning of ship with input magnitude and rate saturations, unknown external environment disturbance and dynamic uncertainties. Firstly, an augment model is developed to restrict the boundedness of the actuators while the input magnitude and rate saturations can be shown in a high-order model. Then, radial basis function neural networks are applied to formulate a new control law via the velocities backstepping method to hand with the dynamic uncertainties. In particular, the minimal learning parameters method is used to reduce the computational complexity, with a single parameter needing to be updated in each step of backstepping. Meanwhile, robust adaptive compensation terms are introduced into the design of virtual and actual control while the error caused by neural networks is mitigated. In line with the Lyapunov theory, the uniformly ultimately boundedness of all signal in closed-loop control system is demonstrated. Finally, simulation is performed to illustrate the advantage and effectiveness of the proposed control strategy.

Enhanced robust adaptive flight control for a convertible VTOL UAV

In this paper, we propose less conservative formulations for candidate controllers of robust adaptive mixing control (RAMC) strategies. The RAMCs are employed to solve the trajectory tracking problem throughout the full flight envelope, with guaranteed stability, of a convertible plane (CP) vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). Initially, the multi-body nonlinear dynamic model of the CP VTOL UAV is obtained using the Euler–Lagrange formalism, from which a linear parameter-varying (LPV) model is derived to be used in the design of the RAMC candidate controllers. The new formulations of the candidate controllers are proposed considering two approaches: a (i) Parallel Distributed Compensation (PDC); and (ii) a parameter-dependent Lyapunov function. Hardware-In-the-Loop (HIL) experiments are performed in a high-fidelity flight simulator to verify the fulfillment of real-time constraints, ensuring a computationally lightweight control strategy ready for implementation in a low-cost embedded computational system.

Robust adaptive control of underwater glider for bottom sitting-oriented soft landing

This paper deals with the formalization, design, theorization, and implementation of a robust adaptive nonlinear control scheme to solve the bottom sitting-oriented soft landing control problem for an underwater glider under model uncertainties, actuator dynamics, and external disturbances. First, uniformized motion equations are derived based on Lie derivatives for describing pitch angle and vertical velocity tracking control. Then, the robust adaptive control framework is enhanced by containing an anti-windup auxiliary system and a disturbance observer. The stability of the closed-loop control system based on Lyapunov theory is proven to guarantee the boundedness of the entire internal signals. Finally, multifarious experimental results are also conducted by a self-developed underwater glider to demonstrate the effectiveness of the proposed controller.

Asymptotic tracking control of uncertain MIMO nonlinear systems with extended condition for controllability

AbstractFor uncertain multiple‐inputs multi‐outputs nonlinear systems, it is nontrivial to achieve asymptotic tracking due to the intrinsic coupling among inputs, while the controllability conditions in most existing methods are rather restrictive or even impractical especially when unexpected actuator faults are involved. In this article, we focus on extending such controllability condition by resorting to the existence (instead of a priori knowledge) of some feasible auxiliary matrix, upon which a robust adaptive control scheme is first presented in the absence of actuator faults that is not only able to achieve asymptotic tracking even in the presence of non‐parametric uncertainties with all the closed‐loop signals globally ultimately uniformly bounded, but also able to deal with a larger class of system models. Furthermore, for the case with intermittent actuator faults, we develop a fault‐tolerant control scheme with extended condition for controllability that is able to accommodate such faults automatically without using any fault detection or fault diagnosis unit. The effectiveness and benefits of the proposed method are verified via simulation on robotic systems.

Robust adaptive tracking control for dynamic positioning ships subject to dynamic safety constraints and actuator saturation

This paper proposed a novel robust adaptive trajectory tracking control scheme for dynamic positioning (DP) ships subjected to unknown time-varying disturbances, unknown model parameters, dynamic safety constraints, and actuator saturation. The dynamic safety constraints were guaranteed based on the tan-type barrier Lyapunov function (BLF) technique, which ensured the position and velocity of the DP vessel within the given constraints. Moreover, the radial basis function neural network (RBFNN) was adopted to approximate the unknown parameters of the dynamics model, and an adaptive neural disturbance observer (ANDO) was designed to compensate for lumped disturbances. An anti-windup system was developed to handle the actuator saturation effect. By combining the above techniques and back-stepping method, the final control law was presented, and the semi-global asymptotic convergence was achieved via rigorous theoretical analysis and Lyapunov proof. The results of simulations demonstrated the superiority and robustness of this approach in resolving the trajectory tracking challenges of DP ships. Moreover, the proposed control scheme is highly effective in optimizing the trajectory tracking of DP ships, as it takes into account the multiple constraints that may arise during the process, which is a valuable contribution to the field of DP ship trajectory tracking, providing a more comprehensive and effective solution to this complex problem.

Event-Triggered Adaptive Robust Control for a Class of Uncertain Nonlinear Systems With Application to Mechatronic System

Adaptive robust control technique is investigated to devise trajectory tracking event-triggered controller for a class of uncertain nonlinear systems. Different from the most existing results, this paper divides the entire control input into two parts, including model compensation input term and the robust term, which are separately dealt with by means of special triggered mechanisms. The advantage of separation is that the system reduces the magnitude of chattering amplitude within triggered inputs. To balance the information updating frequency and the magnitude of chattering amplitude, this paper proposes the dynamic threshold triggered mechanism and provides a new Lyapunov function to guarantee the global boundedness of all the closed-loop signals as well as the convergence of the tracking error to an arbitrary small set around zero. This paper also considers desired compensation adaptive robust event-triggered control strategy, thereby further saving bandwidth resources and smoothing the triggered inputs. The Zeno behavior is excluded for two triggering schemes. Finally, simulation and Hardware-in-the-loop (HIL) experiment are used to verify the effectiveness of the proposed strategies.

Adaptive robust control for unmanned combat platform: Obstacles avoiding and dynamic trajectory tracking

AbstractThis paper proposes an adaptive robust control scheme to deal with the uncertainty problem of unmanned combat platform. Under the constraint of the controller, the rescue vehicle aims to follow the trajectory of the reconnaissance vehicle and avoid obstacles (e.g., craters and destroyed tanks), while they eventually reach the defined target location (an accident area). First, for the control objective, the original constraints are innovatively separated into two parts: arrival constraint and tracking‐avoidance constraint. An approximate constraint‐following control substitutes for the triple problem, and the constraint‐following error is subsequently established. Second, for the control strategy, a ‐measure is introduced to measure the constraint‐following error. To ensure the tracking‐avoidance constraint and the arrival constraint can be fully satisfied by the system, an adaptive robust control is designed. Finally, for the fast time‐varying uncertainty with unknown boundary, an adaptive law is introduced to evaluate the triple tracking‐avoidance‐arrival performance. In conclusion, the main contribution of this paper is to achieve triple tracking‐avoidance arrival for unmanned combat platform, and the problem of military detection and rescue on complex battlefields can be eliminated.

Adaptive predefined-time consensus control for disturbed multi-agent systems

The paper presents a robust adaptive control strategy to attain consensus of a specific group of nonlinear and disturbed multi-agent systems during a predefined-time. To address the impacts of nonlinear dynamics and disturbances, adaptive schemes are developed to assess the state-dependent rates and constant boundaries of disturbances and nonlinearities immediately. Relying on the adaptive updated information, a disturbance rejection consensus controller is proposed to synchronize consensus errors within the defined error model, so that the errors can tend to zero within predefined time impacted by nonlinearities and disturbances. The efficacy and validity of the proposed approach is substantiated by the comparative simulation results obtained from a system comprising Chua’s circuits.

Robust adaptive control of dynamic positioning ship under thruster faults and unknown disturbances

The robust adaptive anti-disturbance fault-tolerant control strategy for the dynamic positioning (DP) system of ships is built on the disturbance observer, the adaptive fault observer with the vectorial backstepping approach. The disturbance observer is constructed to estimate the first-order Markov disturbances. The adaptive fault observer with the projection algorithm is designed to estimate partial thruster faults. By employing the vectorial backstepping method, the robust adaptive anti-disturbance fault-tolerant control law is developed to simultaneously achieve the disturbance compensation and the partial thruster fault tolerant. It is demonstrated using the Lyapunov functions that the robust adaptive anti-disturbance fault-tolerant controller can maintain the DP of the ship’s position and heading to achieve the desired value, while guaranteeing the global stability of all signals in the DP closed-loop control system. Finally, different cases in the unknown ocean disturbance environment demonstrate the effectiveness of the robust adaptive anti-disturbance fault-tolerant control strategy.

Robust adaptive output feedback for the formation control of heterogeneous ships based on a nonlinear extended state observer

This study considers the formation control problem of heterogeneous ships with unknown time-varying disturbances, dynamic uncertainties, and unmeasurable speeds, a robust adaptive output feedback formation control scheme for the heterogeneous ships based on nonlinear extended state observer is developed. In order to solve the heterogeneous ships formation control problem with leader-following structure, transforming the formation control problem into a class of ship trajectory tracking problem by improving the design of the virtual ship. Firstly, a nonlinear extended state observer is designed for estimating ship speeds due to technical limitations and external disturbances. Then, combining the adaptive neural networks with disturbances adaptive law to deal with the unknown time-varying disturbances and dynamic uncertainties. Meanwhile, the second-order tracking differentiator is designed to calculate the derivative of the virtual control law, which has higher precision comparing to the first-order filter. Stability theory is proved that all the signal of closed-loop control system are uniformly ultimately bounded, and the tracking errors can converge into arbitrary small neighborhoods around zero. Finally, comparative simulations were performed to demonstrate the effectiveness of the proposed controller.

Robust consensus of unknown heterogeneous non‐minimum phase multi‐agent systems with unmodeled dynamics and disturbances

SummaryIn this paper, the robust leader‐following output consensus problem is addressed for unknown heterogeneous non‐minimum phase linear multi‐agent systems with unmodeled dynamics and external disturbances under directed graphs. The system matrix of the leader and the parameters of the nominal dynamics of the followers are assumed to be unknown. A novel distributed robust adaptive pole placement control scheme consisting of an adaptive distributed observer and a robust adaptive pole placement controller is developed. It is shown that the robust leader‐following output consensus problem of the concerned multi‐agent system can be solved by the proposed distributed robust adaptive control scheme under some sufficient conditions. It is also shown that the exact output consensus can be achieved if the unmodeled dynamics and external disturbances vanish as time goes to infinity. Two examples are used to demonstrate the effectiveness of the proposed control approach.

Robust-Adaptive Controllers Designed for Grid-Forming Converters Ensuring Various Low-Inertia Microgrid Conditions

As the integration of renewable energy sources (RESs) and distributed generations (DGs) increases, the need for stable and reliable operation of microgrids (MGs) becomes crucial. However, the inherent low inertia of such systems poses intricate control challenges that necessitate innovative solutions. To tackle these issues, this paper presents the development of robust-adaptive controllers tailored specifically for grid-forming (GFM) converters. The proposed adaptive-robust controllers are designed to accommodate the diverse range of scenarios encountered in low-inertia MGs. The proposed approach applies both the robust control techniques and adaptive control strategies, thereby offering an effective means to ensure stable and seamless converter performance under varying operating conditions. The efficacy of the introduced adaptive-robust controllers for GFM converters is validated within a low-inertia MG, which is characterized by substantial penetration of converter-interfaced resources. The validation also encompasses diverse MG operational scenarios and conditions.

Adaptive Robust Output Tracking of Uncertain Strict-Feedback Nonlinear Systems with Full State Constraints via Control Schemes with Simple Structure

In this paper, the problem of output tracking control is considered for a class of uncertain strict-feedback nonlinear systems with virtual control coefficients and full state constraints. It is assumed only that the nonlinear uncertainties of the systems are some smooth functions enough to guarantee the existence of solutions to the differential equations which describe uncertain systems, and that the virtual control coefficients are unknown. Based on backstepping algorithm, by combining the barrier Lyapunov function with an integral inequality reported in the current control literature, a design method is presented whereby a simple adaptive robust output tracking control scheme can be synthesized. The presented design method need not know all the nonlinear upper bounds of uncertainties, which are repeatedly employed at each step of backstepping procedure. It is also shown that the output tracking error of the uncertain nonlinear systems with both unknown virtual control coefficients and full state constraints can converge uniformly exponentially towards a ball which can be as small as desired. Finally, a numerical example is given, and the corresponding simulations are also implemented to demonstrate the simplicity of the proposed method and the validity of the theoretical results.