Robust Adaptive Control Method Research Articles

Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory

The introduction of a dynamic model in robot trajectory tracking control design can significantly improve its trajectory tracking accuracy, but there are many uncertainties in the robot dynamic model which can be dealt with through robust control and adaptive control. The prevailing robust control as well as adaptive control methods require real-time computation of robot dynamics, but the extreme complexity of the robot dynamics equations makes it difficult to apply these methods in real industrial systems. To this end, this article proposes a robust adaptive control method based on the desired trajectory, which uses the desired trajectory to compute most of the control terms offline, including the robot’s nominal dynamics and regression matrices, and substantially reduces the need for real-time computation of the feedback signals. The robust term modifies the perturbation of the inertial parameters of the links, the adaptive term learns the friction coefficients of the joints online, and an additional compensation term is designed to satisfy the Lyapunov stability condition of the system. Finally, taking a universal manipulator as the experimental platform, the control performances of different control methods are compared to show the feasibility of the controller and the effective reduction in real-time computational complexity.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Robust adaptive dynamic surface control of high-order strict feedback systems based on fully actuated system approach

In this study, the authors propose a robust adaptive dynamic surface control method by using the fully actuated system approach for high-order strict-feedback systems (SFSs) with parameter uncertainty and disturbance. Each subsystem of the SFSs is a high-order system with a full actuation structure. In contrast to the traditional first-order state space method, the proposed control method directly treats each high-order subsystem as a whole without transforming it into a first-order system, which is a concise and efficient treatment. By introducing a series of first-order low-pass filters in each step of the design, the high-order derivatives of the virtual control law are obtained, and the complex and multiple derivation operations are transformed into simple algebraic operations. Adaptive control and robust control are combined to address parameter uncertainty and external disturbances in the system. The Lyapunov stability theory is utilised to demonstrate that all signals in the closed-loop system are uniformly ultimately bounded. The system output can effectively track the desired reference signal under specific constraints, and the tracking error can be adjusted by tweaking the parameters to converge to a sufficiently small neighbourhood around zero. Ultimately, the efficiency of the proposed control method is validated through simulations on a flexible joint manipulator system.

Decentralized model reference adaptive control for interconnected systems with time‐varying delays and unknown dead‐zone inputs

AbstractIn this study, the decentralized model reference adaptive control (DMRAC) problem is tackled for a class of time‐varying delay interconnect systems that comprise unknown system matrices and unknown dead‐zone inputs. Two robust adaptive control methods are proposed for state tracking based on the moderate matching time‐varying delay nonlinear assumptions and the matching between the controlled system and reference model matrices, respectively. The control gain function is explicitly expressed, and it is applied to the adaptive law gains simultaneously. Moreover, a Lyapunov–Krasovskii functional with two integral functions is developed. Besides the properties of the type‐B Nussbaum function, the circumstance where the system parameters are fully unknown is considered. As indicated by the results, all signals in the closed‐loop system are bounded while fulfilling asymptotically tracked control objectives. The simulation example of this study verifies the effectiveness and feasibility of the proposed design method.

Decentralized Secondary Frequency Control of Autonomous Microgrids via Adaptive Robust -Gain Performance

This paper presents an innovative passivity-based L2-gain performance (L2-GP) adaptive robust control method for the design of primary and secondary frequency control in the low-inertia microgrid with lines and the inverter-interfaced generation. First, by exploiting and analyzing the internal structural properties, the Port-controlled Kuramoto-Hamiltonian model with dissipation for the microgrid system is derived. Combining the dynamics of Kuramoto oscillators with energy viewpoints and following the physical features of dynamic energy coordination in the microgrid, an adaptive robust L2-GP decentralized secondary control is developed to guarantee frequency restoration and active power sharing. An adaptive mechanism is adopted to estimate the electrical parameters so that the parameter randomness can be overcome and fast transient response, high performance, and robust stability can be achieved. Via local measurements, the decentralized framework accordingly obviates the need of any communication links for information broadcast or exchange. Finally, the comprehensive case studies are presented to validate satisfactory performance under rapid randomness and flexibility during plug-and-play operations.

Robust adaptive fault-tolerant control for path maneuvering of autonomous surface vehicles with actuator faults based on the noncooperative game strategy

This paper investigates a parameterized path-guided following/maneuvering control problem of an autonomous surface vehicle (ASV) subject to actuator faults via a noncooperative game strategy. Unlike the existing path maneuvering control methods, the noncooperative game of this paper includes two sub-games. On one hand, the path update is considered to be against the effort of kinematic control. On the other hand, the kinetic control is considered to be against the total disturbances consisting of actuator faults and external disturbances. A robust adaptive fault-tolerant path maneuvering controller is designed based on a noncooperative game approach. Specifically, we design a kinematic control law using an improved dynamic surface control approach to achieve the geometric objective. An improved neural predictor is constructed, in which a concurrent learning-based adaptation is designed to identify unknown fault coefficients. A path update law and a kinetic control law are calculated to cover noncooperative games by using an adaptive dynamic programming approach. Theoretical analysis shows that the closed-loop system is input-to-state stable, and the proposed method can meet the geometric objective, the dynamic objective, and the fault-tolerant objective. Finally, simulation results demonstrate the efficacy of the proposed robust adaptive fault-tolerant control method for path maneuvering.

Robust adaptive vibration control of a gantry crane system with flexible cable

SummaryIn this paper, a robust adaptive vibration control method for gantry crane system is proposed considering unknown parameters brought by unknown payload, cable length and tension. The control objective is to transport the cargo to the desired position while suppressing the vibration of the cable in the presence of unknown parameters. For this purpose, a boundary controller is designed with an adaptive law to compensate the parameter uncertainty of the system. The well‐posedness of the closed‐loop system is proved by means of operator semigroup theory and the asymptotic stability of the closed‐loop system is analyzed. Finally, the effectiveness of the proposed control approach is demonstrated through both numerical simulation comparisons and physical experiments.

Robust model predictive control of wind turbines based on Bayesian parameter self-optimization

This paper studies the effect of different turbulent wind speeds on the operation of wind turbines. The proportion of wind power in the field of new energy generation has increased massively and has gained wide application and attention. However, the smooth operation and the stability of the output power of the wind power generation system are susceptible to wind speed fluctuations. To tackle this problem, this paper takes a 5 MW horizontal axis wind turbine as the research object that proposes a parameter adaptive robust control method to achieve self-optimization of controller parameters by means of Bayesian optimization. The 5 MW wind turbine model is utilized to verify the feasibility of the algorithm by combining the wind speed types commonly found in a high-altitude region in northwestern. The simulation results validate the effectiveness of the proposed scheme. The outcomes demonstrate that Bayesian optimization can significantly decrease the effects of wind speed instability. The output power increases by 1.9% on average at low wind speed and stabilizes on 5 MW at high wind speed. Therefore, the stable controller for wind power output is the robust model predictive controller with parameter improvement.

Differential flatness-based adaptive robust tracking control for wheeled mobile robots with slippage disturbances

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.

Adaptive cooperative control for multiple high-speed trains with uncertainties, input saturations and state constraints

This paper investigates the adaptive cooperative control of multiple high-speed trains, where uncertainties, input saturations and state constraints are considered simultaneously. Within the multi-agent framework, the multi-train system cooperative operation problem is formulated, with the leader train tracks the desired operation trajectory, and the follower trains automatically operate according to the state information of the front train. In order to overcome the parameter uncertainties and unknown external disturbances of the system, a bounded-parameter robust adaptive control (BRAC) method is proposed for speed and position tracking of the leader train. Then a novel state-constraint bounded-parameter robust adaptive controller (SBRAC) is further designed in two steps to avoid the risk of overspeed and collision of the follower trains. The auxiliary dynamic system (ADS) with saturation compensation is introduced to solve input saturations, and the barrier Lyapunov function (BLF) is constructed to actively constrain the speed and position of the trains. Finally, numerical simulations are given for the multi-train system to demonstrate the effectiveness of the theoretical studies.

Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive

Flapping-wing flight control is a multi-input and multi-output nonlinear system with uncertainties, which is affected by modeling errors, parameter variations, external disturbances, and unmodeled dynamics. Parameter uncertainty has a great impact on the stability control of flapping-wing flight, and gain adjustment is a common means to deal with parameter uncertainty, but it is complex and time-consuming. Based on the mechanism of flapping-wing flight, a nonlinear dynamic model for flapping-wing dynamic flight is established by analyzing the forces, moments, and attitude changes of the fuselage and wing in detail. Based on the constructed dynamic model, a fast robust adaptive flapping-wing flight control method is proposed. The state predictor is designed to estimate and monitor the uncertain parameters in the flapping-wing attitude control model, and the adaptive law adjusts the parameter estimation to ensure that the output error between the state predictor and the controlled object is stable in the Lyapunov sense, and finally the adaptive control law is obtained. At the same time, the Monte Carlo-support vector machine method is used to optimize the boundary of the control parameters in the flight control to obtain the control parameters that can meet the control expectations, and the obtained parameters are classified and judged according to the stable level flight conditions. Based on the adjusted parameters and the predetermined control signal, the control amount is adjusted according to the control law. When the adaptive gain is large enough, the simulation results show that the system has good transient response characteristics.

Performance Improvement of Active Suspension Constrained System via Neural Network Identification.

A robust adaptive control method for a certain type of quarter active suspension system (ASS) is proposed in this work. The constraint issue of ASS is put into consideration primarily. Due to the limitation of the traditional barrier Lyapunov functions (BLFs), the integral barrier Lyapunov function (iBLF) is introduced to exert direct constraints on state variables in each stage under the backstepping frame, and neural networks (NNs) are applied to identify those unknown functions. Then, an adaptive law based on the projection operator is defined to eliminate the influence caused by the actuator failure. It is widely known that only the vertical displacement and velocity constraints are not violated, can the ASSs become stable and secure. It can be ultimately confirmed that all signals in the closed-loop system are bounded, and the control goals are satisfied. Last but not least, the feasibility of the approach is illustrated directly through a contrast simulation example.

Model-Free Robust Backstepping Adaptive Cruise Control

This paper studies the model-free robust adaptive cruise control problem of a vehicle with unknown nonlinear dynamics and disturbances. First, under backstepping control framework, the position tracking errors with different spacing strategies are used to design a virtual control law, which provides a velocity reference. Then, a novel data-driven sliding surface whose parameters are updated by designing estimation algorithm is developed to handle the unknown uncertainties and disturbances. Finally, the model-free robust backstepping adaptive cruise control (MFRB-ACC) method including PI control, model-free control, and robust control is designed. The novelty of the proposed control technique lies in its strong robustness, which is not based on the precise vehicle model. The designed data-driven sliding surface releases the necessity for the accurate mathematical model of the vehicle and guarantees the inherent robustness of the controller, in particular to uncertainties, modelling error, or external disturbance. Moreover, the designed controller contains three terms such that it has an effective decoupling ability and strong robustness. The effectiveness and superiority of the designed MFRB-ACC method are validated on MATLAB, and the simulation results show that compared to the PID algorithm, the designed MFRB-ACC algorithm can track its preceding vehicle with lower tracking error under different spacing strategies, different operating conditions, and low sampling frequencies. Especially at a sampling frequency of 0.1 s, the error under the PID-ACC increases from 0.2 m at a sampling frequency of 0.01 s to 2 m, and the error under MFRB-ACC has little change compared to the error at a sampling frequency of 0.01 s.

Robust adaptive control for hybrid electric vehicles using sliding mode control

The complicated transient coupling driving performance of the electromechanical coupling driving system for hybrid electric vehicles can be guaranteed by reasonably distributing the power between the engine and the electric machine. However, the disturbances resulting from system parameter perturbation, model uncertainty, unknown road condition, and uncertain preceding vehicle acceleration would degrade the control performance. To this issue, this article investigates a robust adaptive control scheme based on sliding mode control techniques to handle the complicated transient coupling driving problem for the electromechanical coupling driving process. In the robust adaptive control method, the coordinated control mechanism is designed using the electric machine to compensate for the response deviation of engine torque. Meanwhile, the double-loop adaptive terminal sliding mode speed-tracking control scheme and the adaptive dynamic sliding mode torque tracking control method are designed for the engine and the electric machine, respectively. The proposed control scheme ensures accurate tracking of the desired trajectories of the engine and electric machine in the presence of parameter perturbation and the unknown driving condition. Both simulation and actual vehicle experimental results demonstrate the effectiveness and practicality of the proposed control strategy.

Adaptive Robust Active Vibration Control for Vehicle Seat Suspension with Unknown Passenger Mass

The vibration of the car seat can affect the comfort of passengers and can even cause some health hazards to them. In this paper, a vehicle seat control system is built to reduce the hazards of vehicle seat vibration. Firstly, the dynamic of the vehicle seat suspension is modelled. Then, an adaptive robust vibration control method is introduced: a base controller with an enhanced Youla parameterization is designed to turn it into a global stable controller set, followed by online tuning of the Q-parameters using an adaptive algorithm. The results of the vehicle seat vibration control were simulated in MATLAB/Simulink.

Adaptive Robust Control for Uncertain Underactuated Mechanical Systems

AbstractAn adaptive robust control method is proposed for underactuated mechanical systems (UMSs) with uncertainty in the paper. UMSs are supposed to follow prescribed constraints when there is uncertainty. Inspired by the Udwadia–Kalaba modeling approach, those constraints are considered as control objectives realized by robust control. The uncertainty considered in UMSs is characterized as time‐varying and bounded, albeit with unknown bounds. In order to estimate the boundary information, a novel adaptive law is developed. By using the Lyapunov function, the UMS is guaranteed to be uniform boundedness and uniform ultimate boundedness. The simulation is validated by using a two‐wheeled bicycle as an example, compared to conventional PD control methods. The simulation comparison results show that the proposed control method exhibits notable characteristics, including fast transient response, small overshoots, and good robust performance.

Practical adaptive robust tracking control of the dual-valve parallel electro-hydraulic servo system with reduced-order model

To meet the increasing demand for trajectory tracking accuracy and high-efficiency requirements in modern mobile machinery, this paper proposes a practical adaptive robust control method based on the dual-valve parallel electro-hydraulic servo system. Existing tracking control strategies for such servo systems rely on a backstepping method and assume that full states are known, which is stringent in practice. To cope with the problem, we reduce the mathematical model order of the studied system using singular perturbation theory in this paper, and only the position feedback is required for the control implementation. Then, to achieve high accuracy tracking control, a direct adaptive robust control scheme combing with a dynamic flow allocation layer is adopted to synthesize the controller. With this method, parameter uncertainties and load disturbance are rejected, and valve characteristics are considered in the dynamic flow allocation layer to solve the flow redundancy problem, respectively. Convergence of the simplified system tracking results is proved theoretically. Extensive co-simulations and experimental tests are carried out to illustrate the effectiveness of the proposed strategy, both tracking precision and flow distribution can be achieved efficiently by simple parameter adjustments in the field.

μ-Synthesis-based robust L1 adaptive control for aeropropulsion system test facility

For Aeropropulsion System Test Facility (ASTF) applied in aero-engine performance testing, it is critical to accurately control the pressure and temperature of the airflow to simulate the flight environment. This article presents a μ-synthesis-based robust L1 adaptive control method for ASTF to achieve excellent control performance within a wide working envelope. A μ controller based on a reference model is first designed as the baseline, which can address the coupling effect and guarantee the desired dynamic despite uncertainties. Thereafter, an L1 adaptive compensation term is incorporated to cancel out the large (matched and unmatched) uncertainties, and then recover the baseline performance. This control scheme augments the μ controller to control the ASTF over a wide working envelope and avoids complex gain scheduling design. Furthermore, μ controller can still ensure the robust performance of the closed-loop system even if the adaptive compensation is inadequate. Numerical simulations are performed on high-fidelity nonlinear model to verify the effectiveness of the proposed method. Compared with other state-of-the-art techniques, the robust L1 adaptive control possesses better performance and robustness.

Event-triggered adaptive finite time trajectory tracking control for Underactuated Vessel considering unknown time-varying disturbances

Abstract This paper aims to solve the finite-time trajectory tracking problem of underactuated surface ships under the influence of dynamic uncertainty, unknown external time-varying interference and limited communication resources, an event-triggered robust adaptive finite-time trajectory tracking control method for underactuated ships is designed by combing the existing trajectory tracking control methods and engineering needs in actual navigation. It can obviously improve the tracking accuracy of the ship, and complete the tracking task safely and efficiently. This scheme transforms the composite uncertain vector synthesized by uncertain parameters and external disturbances into a linear parameterized form. Next, considering the limitation of communication resources, a relative threshold event triggering mechanism is introduced to further extend the triggering time interval on the basis of the fixed threshold triggering scheme. Then, an event-triggered robust adaptive finite-time trajectory tracking control scheme is designed for underactuated ships, and a rigorous stability proof is provided for the finite-time trajectory tracking control scheme designed above through Lyapunov stability theory. Simulation experiment is carried out in MATLAB and the results show that the finite-time trajectory tracking control scheme proposed in this paper can effectively solve the problems of dynamic uncertainty, unknown time-varying interference from the outside world, and limitation of communication resources. This paper can provide theoretical support for the autonomous navigation of intelligent ships.

Resilient Event-Triggered Adaptive Cooperative Fault-Tolerant Tracking Control for Multiagent Systems Under Hybrid Actuator Faults and Communication Constraints

This paper investigates the cooperative fault-tolerant tracking problem for multiagent systems with directed topology that experience communication link faults and actuator faults under malicious attacks. Based on a resilient event-triggered mechanism, a novel distributed high-order sliding-mode observer and a robust adaptive cooperative fault-tolerant control method are proposed, which can achieve the desired formation shape tracking under communication limitations and hybrid actuator faults caused by malicious attacks. First, the designed distributed event-triggered observer can estimate the unknown leader. Second, the proposed event-triggered mechanism can be used to reduce the impact of malicious attacks on the information and physical layers of the multiagent system. Additionally, the utilization rate of network communication is further improved so that it can save communication resources by reducing the number of event triggers. Then, based on the information of the leader, the designed robust adaptive cooperative fault-tolerant controller can compensate for the influence of the hybrid actuator faults on the system under malicious attacks. Finally, the effectiveness of the method is verified by two sets of example simulations.