Constrained Control Scheme Research Articles

Adaptive-constrained admittance control for physical human–robot interaction

In this paper, an adaptive constrained admittance control scheme is proposed, which can effectively solve physical human–robot interaction (pHRI) tasks with output constraints. To ensure the safety of robot behavior, the constraint controller is designed in the trajectory planning layer and the control layer. The asymmetric soft saturation function (ASSF) is designed to obtain variable compliant motion trajectories generated from the desired admittance model. In addition, the controller based on the asymmetric integral barrier Lyapunov function (AIBLF) is designed to deal directly with asymmetric Cartesian space constraints. Finally, radial basis function neural network (RBFNN) is utilized to approximate the dynamics uncertainty of the robot manipulator and to improve the tracking accuracy. According to the Lyapunov stability principles, it can be proved that all states of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed control scheme is demonstrated by several simulations and experiments.

Constrained Control of Autonomous Underwater Gliders Based on Disturbance Estimation and Tracking Back Calculation

ABSTRACTThis article presents an anti‐disturbance constrained control scheme for the pitch channel of autonomous underwater gliders subject to internal and external disturbances, as well as actuator saturation. First, the integral control technique is employed to develop a disturbance observer to estimate the overall effect of possible uncertainties and disturbances on the nominal vehicle model, which is referred to as the mismatched lumped disturbance. Then, a disturbance rejection control law is constructed based on the disturbance observer. Following that, a straightforward anti‐windup modification is proposed to handle potential input constraints by using the tracking back calculation technique. Specifically, the difference between saturated and unsaturated control input signals is utilized to create a feedback signal that addresses the disturbance observer input, thereby alleviating system windup during actuator saturation events. Furthermore, the stability of the overall closed‐loop system is established in term of the Lyapunov stability theorem, demonstrating that the tracking error is ultimately bounded. Compared with some existing anti‐windup control schemes, the suggested approach offers intuitive design guidelines, resulting in a simple controller that can be easily implemented. Finally, the effectiveness and robustness of the proposed control scheme are verified through simulation results.

Adaptive fixed-time neural consensus control for a class of uncertain nonlinear multi-agent systems with full state constraints

This paper is concerned with the fixed-time consensus control problem for non-strict feedback multi-agent systems with asymmetric output constraints and full state constraints. Considering the feasibility of controlling execution, a novel practical virtual control signal is developed utilizing both saturation function and hyperbolic tangent function to ensure that this signal can remain within the same restricted range as the corresponding state variable throughout entire operation process. In backstepping steps, the design of ideal virtual control signal also adopts a different form of piecewise function than before, introducing high-order polynomial functions to avoid singularity problems in the derivation process. In addition, function approximation ability of radial basis function neural networks technique is applied to estimate uncertainties derived from the system functions and controller design procedure. Moreover, universal barrier Lyapunov function approach is improved for constructing an adaptive constrained synchronization control scheme. By fixed-time stability theory, it is shown that the tracking errors of the MAS converge to an adjustable region around the origin in a fixed time and the state variables always obey their constraints. And the upper bound of the settling time is merely dependent on design parameters, which is not affected by the initial states of MAS. The effectiveness of the proposed control strategy is shown by a numerical simulation example at last. Two scenarios are provided to demonstrate the advantages of the control protocol proposed in this paper.

A dynamic-event approach to adaptive asymptotic tracking control of [formula omitted]-normal nonlinear systems under full state constraints

In this paper, we study the problem of asymptotic tracking control for a class of interconnected p-normal nonlinear systems (p-NNS) with full-state constraints (FSCs). An event-triggered mechanism is constructed by introducing a new dynamic variable. It effectively reduces the update frequency of the controller compared to temporal-triggered. By building a new barrier function, the unknown functions of the system are converted into unknown scalars, which will be disposed of by the adaptive technique. To implement the FSCs control, we propose a low-complexity constrained control scheme while removing the condition that the initial value of the output is constrained. According to Barbalat’s lemma, it is proved that the output signal can asymptotically track the predetermined signal, and the system states are kept within the constraint bound, with all signals bounded. Finally, two simulation examples illustrate the effectiveness of the scheme.

Constrained Motion Control of an Electro- Hydraulic Actuator Under Multiple Time-Varying Constraints

The motion control technology of electro-hydraulic actuators has great significance in industrial applications. Constraints significantly limit the motion control performance of actuator motion control, in addition to the inherent nonlinearities and uncertainties of the electro-hydraulic systems. The constraints comprise kinematic and dynamic constraints, and they can be time-varying owing to variations in the system. If the constraints are not fulfilled, the control accuracy may be adversely affected, for instance by actuator vibration, cavitation, or even instability. This study proposes a constrained motion control strategy for a variable-speed pump-driven hydraulic actuator. To robustly track a desired trajectory under constraints, the control strategy combines a nonlinear filter-type trajectory planning strategy and an adaptive robust motion controller. The trajectory planning strategy is designed by considering dynamic and kinematic constraints of the electro-hydraulic system, and it synthesizes a trajectory that reaches the given reference in minimum time while fulfilling these multiple constraints. Meanwhile, the adaptive robust motion controller tracks the synthesized trajectory with guaranteed control accuracy in the presence of inherent nonlinearities of the electro-hydraulic actuator. In addition, the assignments of the multiple constraints are adjusted in real time, which further optimize the constrained motion control performance. Comparative experiments with various given references were conducted to verify the advantages of the proposed constrained control strategy.

Safe Deep Reinforcement Learning-Based Constrained Optimal Control Scheme for HEV Energy Management

Considering physical constraints in online optimization and training safety is a challenge for implementation of deep reinforcement learning (DRL) algorithm. Especially for the non-linear system, the mapping relationship between output action of the agent and the control signals is difficult to obtain. This paper proposes a novel DRL framework for online optimization in energy management of a power-split hybrid electric vehicle (HEV) which combines a neural-network (NN)-based multi-constraints optimal strategy and a rule-based-restraints system (RBRS). The proposed method named Reward Directed Policy Optimization (RDPO) adopts exterior point method (EPM) and curriculum learning (CL) to direct the agent to recognize and avoid irrational control signals and optimize the fuel economy. The EMS considering fuel consumption minimization and irrational control signals avoidance is optimized by training the agent through WLTC. A competitive fuel economy, 4.495L/100km, is achieved with no irrational control signals. Based on the online adaptability evaluation conducted, the fuel consumption of the vehicle under NEDC and CTUDC has been reduced to 4.113L/100km and 3.221L/100km, respectively, with no irrational control signals. The superiority in optimization, calculation efficiency and safety is verified through the comparisons with various DRL agents.

Event‐triggered model‐free adaptive fractional order sliding mode I/O constrained control for a class of nonlinear systems

AbstractTo tackle the trajectory tracking issue of nonlinear systems with unknown dynamics and input/output (I/O) constraints, a novel model‐free adaptive constrained control (MFACC) scheme is first proposed with improvement of tracking precision and reduction of computation cost. The system model is processed via the compact form dynamic linearization approach. Then, an event‐triggered observer‐based pseudo partial derivative (PPD) estimation algorithm is proposed to implement the data‐driven modeling information, which relieves the computation burden of modeling. Relying on the reconfigured data model, an improved prescribed performance control (PPC) approach with asymmetric predefined boundaries and the fractional order sliding mode control strategy are both exploited and integrated with MFACC for high‐precision and robustness assurance. Moreover, a novel prescribed‐boundary‐based event‐triggered mechanism is proposed to implement MFACC scheme so that the remarkable balance between tracking precision and computation cost can be maintained, and the I/O constraints can be handled via the proposed PPD‐based anti‐windup compensator and PPC technique. Finally, the stability analysis and contrast simulation are pursued which verifies the validity of proposed scheme.

Unified Mapping Function-Based Neuroadaptive Control of Constrained Uncertain Robotic Systems.

For the existing adaptive constrained robotic control algorithms, the demanding "feasibility conditions" on virtual controller is normally inevitable and the extra limits on constraining functions have to be imposed, making the corresponding approaches more demanding and less user friendly in control development. Here, we develop a new neuroadaptive constrained control strategy for uncertain robotic manipulators in the presence of position and velocity constraints. First, a novel unified mapping function (UMF) is constructed so that the restriction on constraining boundaries is removed and more kinds of constraining forms can be handled. Second, by integrating the UMF-based coordinate transformation with the "universal" approximation characteristic of neural networks over some compact set, the developed neuroadaptive control completely obviates the complicated yet undesired "feasibility conditions." Furthermore, it is proven that all closed-loop signals are semiglobally bounded and the constraints are not violated. The effectiveness of the proposed control is validated via a two-link rigid robotic manipulator.

Finite-time adaptive anti-disturbance constrained control design for dynamic positioning of marine vessels with simulation and model-scale tests

For practical and complex offshore operations, preventing violations of system constraints can ensure safety and avoid potential marine accidents in harsh environments. This paper addresses a finite-time constrained control for dynamic positioning (DP) of vessels in the presence of inherent uncertainties, ocean disturbances, full-state and input constraints. For operational safety assurance, the core logic of the proposed constrained control strategy is to constrain the state variables including position output, velocity state, input control command within the designed safe region by employing barrier function and auxiliary system, respectively. To attenuate the adverse effects of unknown disturbances on safety and stability, an adaptive neural disturbance observer (ANDO) is constructed to compensate for lumped disturbances caused by inherent uncertainties, unmodeled dynamics, and sea loads. Then, the composite finite-time anti-disturbance constrained control strategy is formally proposed, which can guarantee semi-global practical finite-time stable (SGPFS) of the closed-loop systems. In offshore engineering, control schemes are not only theoretically feasible, but also need to be accurately verified to provide practical guidance value. For this purpose, the simulation and model-scale test are carried out to realistically reproduce the time-domain motion response of the vessels under ocean disturbances. The verification results further elucidate the applicability and feasibility of the proposed control design in practical offshore engineering.

State constrained control strategy for unmanned surface vehicle trajectory tracking based on improved barrier Lyapunov function

This paper addresses the state-constrained control of the symmetric underactuated unmanned surface vehicles (USVs) under unknown dynamics and time-varying interference based on improved Lyapunov function. Inspired by the prescribed performance control (PPC), the improved logarithmic barrier Lyapunov function (BLF) is proposed with a reasonably defined performance function in advance. The tracking errors of position and yaw direction are constrained within the set range. Meanwhile, the symmetric barrier Lyapunov function (SBLF) and dynamic surface control (DSC) are introduced to simplify the controller design and indirectly restrict the yaw velocity. On this basis, the finite-time disturbance observer (FDO) is proposed to estimate the unknown disturbance. The finite-time auxiliary dynamic system is introduced to deal with the input saturation. In addition, the neural network minimum learning parameter (MLP) can reduce the computational complexity while figuring out the unknown dynamics. Finally, numerical simulation verifies the effectiveness of the control system.

Model‐free adaptive integral sliding mode constrained control with modified prescribed performance

AbstractIn this work, a novel model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL). Firstly, a generalized nonlinear nonaffine system with external disturbance is transformed into an affine system via the FFDL method, which contains a linearly parametric term affine to the control input and preceding output data, and an unknown nonlinear time‐varying term. Then, an adaptive estimation method and a discrete‐time extended state observer (DESO) are used to estimate the pseudo gradient (PG) vector and lumped uncertainties, respectively. Furthermore, an integral sliding mode control scheme containing a modified prescribed performance function and an anti‐windup compensator is designed to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady‐state and to suppress the influence of input saturation. Simulation results demonstrate the superiority of the proposed control scheme.

Dual Event-Triggered Constrained Control Through Adaptive Critic for Discrete-Time Zero-Sum Games

In this article, through adaptive critic, a dual event-triggered (DET) constrained control scheme is established for discrete-time nonlinear zero-sum games. The neural networks are trained from the dual heuristic dynamic programming technique to obtain the approximate optimal policy pair. Two corresponding independent triggering conditions are constructed for the control input and the disturbance to improve the utilization efficiency and ensure the independence between them. In addition, in order to overcome the challenge caused by the actuator saturation, we constrain the control input to a bounded range. Meanwhile, the asymptotically stability is proved for the DET control system. Finally, experimental simulations are conducted to verify the effectiveness of the proposed algorithm.

Disturbance Interval Observer-Based Robust Constrained Control for Unmanned Aerial Vehicle Path Following

This work presents a robust constrained path-following control scheme for the unmanned aerial vehicle (UAV) under wind disturbances. Through appointing the projection from the UAV to the path, the Serret–Frenet frame is introduced to reduce the complexity of the path-following problem. Specifically, the disturbance interval observer is employed to generate the interval of the wind disturbances. Then, the path-following control design is presented based on the dynamic surface control technique, and the auxiliary system is adopted to deal with the command limitation during the design process. Accordingly, the stability of the closed-loop system is analyzed. The effectiveness of the developed control scheme is demonstrated using numerical simulations.

Adaptive Constrained Kinematic Control Using Partial or Complete Task-Space Measurements

Recent advancements in constrained kinematic control make it an attractive strategy for controlling robots with arbitrary geometry in challenging tasks. Most of the current works assume that the robot kinematic model is precise enough for the task at hand. However, with increasing demands and safety requirements in robotic applications, there is a need for a controller that compensates online for kinematic inaccuracies. We propose an adaptive constrained kinematic control strategy based on quadratic programming, which uses partial or complete task-space measurements to compensate online for calibration errors. Our method is validated in experiments that show increased accuracy and safety compared to a state-of-the-art kinematic control strategy.

Virtual-Sensor-Based Model-Free Adaptive Fault-Tolerant Constrained Control for Discrete-Time Nonlinear Systems

Intending to address the issue of sensor fault-tolerant control, a model-free adaptive fault-tolerant constrained control strategy is proposed with virtual sensor technology. The novel introduction of observer-based model-free adaptive control to fault-tolerant control brings benefits to the nonlinear systems with unknown dynamics in the presence of sensor faults. In the design procedures, the dynamic linearization technique is utilized first to establish the equivalent linear model of nonlinear systems. Then, the virtual sensor technology is introduced by designing the novel state-observer-based estimation algorithm to fulfill the data-driven system dynamic and sensor fault modeling. Besides, the broad learning technique is employed with offline training to provide output information for observer-based data-driven modeling under the fault case. Based on the data-driven model, the fault-tolerant constrained control scheme is explored with the anti-windup compensator against input constraints caused by actuator saturation, and the stability analysis is supplied. Finally, the simulations are carried out for both affine and non-affine nonlinear systems to manifest the effectiveness and superiority of the proposed control strategy.

Adaptive Robust Tracking Control for Near Space Vehicles with Multi-Source Disturbances and Input–Output Constraints

In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network (MTPN) is utilized to handle the system uncertainties, and the nonlinear disturbance observer (NDO) based on MTPN is designed to estimate the external disturbances. Furthermore, by constructing the auxiliary system to tackle the input saturation and introducing the Tan-type barrier Lyapunov function (TBLF) to solve the output constraint, the constrained control strategy can be obtained. Combining with backstepping control (BC) technique and stochastic control method, an adaptive robust stochastic control scheme is developed based on NDO, MTPN, and auxiliary system, and the closed-loop system stability in the sense of probability is analyzed based on stochastic Lyapunov stability theory. Finally, numerical simulations further demonstrate the feasibility of the proposed tracking control scheme.

Asymmetric constrained control scheme design with discrete output feedback in unknown robot–environment interaction system

AbstractIn this paper, an overall structure with the asymmetric constrained controller is constructed for human–robot interaction in uncertain environments. The control structure consists of two decoupling loops. In the outer loop, a discrete output feedback adaptive dynamics programing (OPFB ADP) algorithm is proposed to deal with the problems of unknown environment dynamic and unobservable environment position. Besides, a discount factor is added to the discrete OPFB ADP algorithm to improve the convergence speed. In the inner loop, a constrained controller is developed on the basis of asymmetric barrier Lyapunov function, and a neural network method is applied to approximate the dynamic characteristics of the uncertain system model. By utilizing this controller, the robot can track the prescribed trajectory precisely within a security boundary. Simulation and experimental results demonstrate the effectiveness of the proposed controller.

Data-Driven Model Predictive Control for Wave Energy Converters Using Gaussian Process

The energy harvested by an ocean wave energy converter (WEC) can be enhanced by a well-designed wave-by-wave control strategy. One of such superior control methods is model predictive control (MPC), which is a nonlinear constrained optimization control strategy. A limitation of the classical MPC algorithm is its requirement of an accurate WEC dynamic model for real-time implementation. This article overcomes this challenge by proposing a data-driven MPC scheme for wave energy converters. The data-based WEC model is developed by a Gaussian process (encompassing mean predictions and symmetric uncertainties) for a more accurate description of nonlinear and unmodeled system dynamics. A cross-entropy solver for data-driven MPC is employed for rapid, high-performance results, which samples trajectories from Gaussian distributions based on the concept of the symmetry principle. The proposed strategy is verified numerically by simulations which demonstrate its superior performance over a classical complex-conjugate controller.

Adaptive constrained control for automotive electronic throttle control system with experimental analysis

Abstract This article proposes an adaptive constrained control strategy with state constraints and uncertain parameters for an electronic throttle control (ETC) system. Compared with the current control strategies for an ETC system, state constraints and parameter uncertainties are adequately considered in the proposed control strategy. First, the nonlinear dynamic model for control of an ETC is described. Second, the asymmetric Barrier Lyapunov Function (BLF) and a backstepping control algorithm are used to ensure that the throttle opening does not exceed the constrained boundary. A parameter adaptive law is given to estimate the unknown parameter and external disturbances with an ETC system. Third, the proposed BLF controller is compared with the existing Quadratic Lyapunov Function (QLF) controller by simulation and experiment. The results show that the proposed control algorithm not only ensure fast transient performance in the control response, but also avoid the out of bounds of the throttle opening. The proposed constrained control strategy can provide excellent control performance for an ETC system.

Convex-Optimization-based Constrained Control Strategy for 3-DOF Tandem Helicopter using Feedback Linearization

Convex-Optimization-based Constrained Control Strategy for 3-DOF Tandem Helicopter using Feedback Linearization