Presence Of Actuator Saturation Research Articles

A novel finite-time leader-follower consensus control for a disturbed mechanical nonlinear system in presence of actuator saturation

This paper presents a novel leader-follower consensus control for a particular class of nonlinear multi-agent mechanical systems in the presence of control input constraints and external disturbances which includes robot system dynamics with a wide range of potential applications in industry. In this case, one of the agents is selected as the leader to direct the other agents in such a way that the whole system can reach consensus within certain prescribed performance transient bounds. Due to the presence of disturbances in most practical systems, the effect of limited disturbance in the consensus control method has been investigated, and actuator saturation is included in the design process. A terminal sliding mode control method has been adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in MATLAB environment, in different scenarios with simultaneous consideration of actuator saturation and external disturbance, will show the efficiency of the proposed control method.

Synthesis of ℋ∞ Control for Descriptor Hybrid Systems with Actuator Saturation

This paper addresses a mode-dependent state-feedback H∞ control for stochastic descriptor hybrid systems, considering both the absence and presence of actuator saturation. Firstly, the necessary and sufficient conditions for the stochastic admissibility criterion with H∞ performance γ of the closed-loop system are proposed. Given the proposed non-convex condition, the author reformulates it into linear matrix inequalities (LMIs). Then, to extend the result to the systems with actuator saturation, the actuator-saturated control input is expressed as a linear combination of a given state-feedback control input and a virtual control input that always remains under the saturation level. To verify this expression, the set invariant condition is also suggested by using the singular mode-dependent Lyapunov function candidate. Therefore, the conditions for the existence of both the mode-dependent state-feedback H∞ control and the ellipsoidal shape invariant sets are successfully derived in terms of LMIs. Two numerical examples demonstrate the effectiveness of the proposed method by solving optimization problems subject to the proposed LMIs that minimize H∞ performance γ and maximize the invariant set, respectively.

Real-time constraint-based planning and control of robotic manipulators for safe human–robot collaboration

A recent trend in industrial robotics is to have robotic manipulators working side-by-side with human operators. A challenging aspect of this coexistence is that the robot is required to reliably solve complex path-planning problems in a dynamically changing environment. To ensure the safety of the human operator while simultaneously achieving efficient task realization, this paper introduces a computationally efficient planning and control architecture that combines a Rapidly-exploring Random Tree (RRT) path planner with a trajectory-based Explicit Reference Governor (ERG) by means of a reference selector. The resulting scheme can steer the robot arm to the desired end-effector pose in the presence of actuator saturation, limited joint ranges, speed limits, a cluttered static obstacle environment, and moving human collaborators. The effectiveness of the proposed framework is experimentally validated on the Franka Emika Panda robot arm and fed with feedback information from state-of-the-art depth perception systems. Our method outperforms both the standalone RRT and ERG algorithms in cluttered static environments where it overcomes: i) the RRT’s inability to handle dynamic constraints which result in constraint violations and ii) the ERG’s undesirable property of getting trapped in local minima. Finally we employed the RRT+ERG in highly dynamic human–robot coexistence experiments without sacrificing the real-time requirements.

Finite-Time Anti-Saturated Formation Tracking Control of Multiple Unmanned Aerial Vehicles: A Performance Tuning Way

A highly effective control method is very important to guarantee the safety of the formation of flying missions for multiple unmanned aerial vehicles (UAVs), especially in the presence of complex flying environments and actuator constraints. In this regard, this paper investigates the formation tracking control problem of multiple UAVs in the presence of actuator saturation. Firstly, a brand-novel finite-time anti-saturated control scheme is proposed for multiple UAVs to track the desired position commands, wherein the tracking performance is tuned by introducing a logarithmic function-based state-mapping policy. Then, an adaptive scheme based on projection rules is devised to compensate for the negative effects brought by the actuator saturation. Based on the proposed formation tracking controller, the finite-time formation tracking performance tuning and control saturation problems can be addressed simultaneously with a comparatively allowable system robustness. Finally, three groups of illustrative examples are organized to verify the effectiveness of the proposed formation tracking control scheme.

Neural-Network-Based Adaptive Finite-Time Output Feedback Control for Spacecraft Attitude Tracking.

This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state. Then, based on the estimated state, the adaptive neural finite-time command filtered backstepping (CFB) is applied to construct virtual control signal and controller with updating law. The finite-time command filter is given to avoid the computation complexity problem in traditional backstepping, and the compensation signals based on fractional power are constructed to remove filtering errors. Using Lyapunov stability theory, we show that the attitude tracking error (TE) can converge into the desired neighborhood of the origin in finite time and all the signals in the closed-loop system are bounded in finite time although input saturation exists. The numerical simulations are used to show the effectiveness of the given algorithm.

Adaptive super-twisting sliding mode attitude coordination control for spacecraft formation flying with actuator saturation

This paper investigates the attitude coordination control issue for spacecraft formation flying in the presence of actuator saturation and external disturbances with unknown boundaries. Firstly, a super-twisting integral terminal sliding mode surface is developed, which ensures that the sliding mode surface and its first-time derivative converge to zero in finite time. Then, based on the developed sliding mode surface and adaptive technique, a robust anti-saturation distributed attitude coordination controller that enables the attitude and angular velocity to precisely track the desired time-varying command in finite time is devised under the undirected communication topology. Finally, stability analysis and numerical simulations validate the effectiveness and feasibility of the devised control approach.

Saturation-tolerant prescribed function-based adaptive fault-tolerant tracking control for ACV with uncertainty and faults

This paper presents an adaptive fault-tolerant control system for Trajectory tracking of air-cushioned vehicles (ACVs) in the presence of actuator saturation, internal and external system perturbations. The proposed approach utilizes a state observer to estimate and compensate for the disturbances and saturation fault information present in the tracking system of the ACV. To achieve the desired performance, a new prescribed performance function (PPF) is introduced that is both global and saturated-tolerant. The PPF ensures that the error converges to a predetermined function in fixed time for any initial value, although it weakens the limits on the amount of overshoot. The combination of the observer and PPF with a backstepping fault-tolerant controller leads to improved transient and steady-state performance of the tracking control system for ACV. Simulation results demonstrate the effectiveness of the proposed approach.

Time domain design of PI-C controllers

Abstract PI-C (Proportional-Integral-Compensating) controllers are observer-based compensators with integral action containing an inverse model of the plant. Originally developed for windup prevention in case of sensor saturation they are also useful in the presence of actuator saturation. They allow to achieve a nearly perfect disturbance attenuation while the loop gain satisfies the Circle Criterion. It has also been demonstrated that PI-C controllers can solve the windup problem in case of joint input and output saturation. The design of PI-C controllers has only been formulated in the frequency domain so far which, in the MIMO case, requires the manipulation of polynomial matrices. Here, the state-space design of PI-C controllers is presented.

Static anti-windup compensator design for locally Lipschitz systems under input and output delays.

This paper proposes a static anti-windup compensator (AWC) design methodology for the locally Lipschitz nonlinear systems, containing time-varying interval delays in input and output of the system in the presence of actuator saturation. Static AWC design is proposed for the systems by considering a delay-range-dependent methodology to consider less conservative delay bounds. The approach has been developed by utilizing an improved Lyapunov-Krasovskii functional, locally Lipschitz nonlinearity property, delay-interval, delay derivative upper bound, local sector condition, L2 gain reduction from exogenous input to exogenous output, improved Wirtinger inequality, additive time-varying delays, and convex optimization algorithms to obtain convex conditions for AWC gain calculations. In contrast to the existing results, the present work considers both input and output delays for the AWC design (along with their combined additive effect) and deals with a more generic locally Lipschitz class of nonlinear systems. The effectiveness of the proposed methodology is demonstrated via simulations for a nonlinear DC servo motor system, possessing multiple time-delays, dynamic nonlinearity and actuator constraints.

A New Robust LMI-based MPC for Compressor Surge Instability in the Presence of Actuator Saturation and Pipe Effects

A New Robust LMI-based MPC for Compressor Surge Instability in the Presence of Actuator Saturation and Pipe Effects

On Finite-Time Anti-Saturated Proximity Control with a Tumbling Non-Cooperative Space Target

For the challenging problem that a spacecraft approaches a tumbling target with non-cooperative maneuver, an anti-saturated proximity control method is proposed in this paper. First, a brand-new appointed-time convergent performance function is developed via exploring Bézier curve to quantitatively characterize the transient and steady-state behaviors of the pose tracking error system. The major advantage of the proposed function is that the actuator saturation phenomenon at the beginning can be effectively reduced. Then, an anti-saturated pose tracking controller is devised along with an adaptive saturation compensator. Wherein, the finite-time stability of both the pose and its velocity error signals are guaranteed simultaneously in the presence of actuator saturation. Finally, 2 groups of illustrative examples are organized and verify that the close-range proximity is effectively realized even with unknown target maneuver.

Learning-Based Adaptive Optimal Control for Flotation Processes Subject to Input Constraints

This article presents a learning-based adaptive optimal control approach for flotation processes subject to input constraints and disturbances using adaptive dynamic programming (ADP) along with double-loop iteration. First, the principle of the operational pattern is adopted to preset reagents’ addition based on the feeding condition. Then, this article leverages a deep learning model, which is composed of multiple neural layers to detect flotation indexes directly from the raw froth images. After that, the tracking error between the detected flotation indexes and the reference values can be minimized by using ADP-based double-loop iteration. Particularly, a policy-iteration (PI) method is utilized for the proposed learning-based ADP algorithm. In the inner loop, the optimal control problem is formulated as a linear quadratic regulator (LQR) problem using the low-gain feedback design method. In the outer loop, the design parameters, i.e., weighting matrices, are tuned automatically to satisfy the input constraints. Finally, the analytical results demonstrate that the proposed scheme can guarantee asymptotic tracking in the presence of actuator saturation and disturbances.

Control design under actuator saturation and multi-rate sampling

The problem of designing a stabilizing feedback controller in the presence of saturating actuators and multi-rate (asynchronous) aperiodic state measurements is studied. Specifically, we consider a scenario in which measurements of the plant states are collected at the controller end in a sporadic and asynchronous fashion. A hybrid controller is used to perform a fusion of measurements sampled at different times. In between sampling events, the controller behaves as a copy of the plant and provides a feedback control signal based on the reconstruction of the plant state. The presence of saturation at the plant input limits the value of the components of this signal to a bounded range. When a new measurement is available, the controller state undergoes an instantaneous jump. The resulting system is augmented with a set of timers triggering the arrival of new measurements and analyzed in a hybrid systems framework. Relying on Lyapunov tools for hybrid systems and techniques for control design under saturation, we propose sufficient conditions in the form of matrix inequalities to ensure regional exponential stability of a closed-set containing the origin of the plant, i.e., exponential stability with a guaranteed region of attraction. Specifically, explicit estimates of the basin of attraction are provided in the form of ellipsoidal sets. Leveraging those conditions, a design procedure based on semidefinite programming is proposed to design a stabilizing controller with maximized size of the basin attraction. The effectiveness of the proposed methodology is shown in an example.

Barrier Function Based Adaptive Sliding Mode Control for Uncertain Systems With Input Saturation

This article aims to present a barrier function based adaptive sliding mode (BFASM) control scheme for a class of uncertain nonlinear systems with actuator saturation. In conventional adaptive sliding mode control for saturated systems, the adaptive gain is often monotonically increasing with respect to the persistent disturbance, which results in aggressive control input with severe chattering that is often giving rise to system instability. In order to solve this problem, this article proposes a BFASM controller with explicit consideration of actuator saturation and uncertainties. The proposed method contains a reaching control input that can adapt to the time-varying disturbances. In addition, the proposed method does not require the upper bound information of disturbance that is commonly requested in conventional sliding mode control and thus removes the conservative assumption on a sufficiently large control gain. Stability analysis proves that the tracking error under the proposed control can asymptotically converge into a prespecified region in the presence of actuator saturation, parameter uncertainties, and external disturbances. The proposed method is also applied to an air-floating positioning system with input saturation, and experimental results demonstrate its efficacy.

Minimum Power Control Allocation for Incremental Control of Over-Actuated Transition Aircraft

Existing control allocation approaches usually minimize an or norm of the actuator commands that is loosely related to the thrust produced or the aerodynamic drag. This paper proposes a novel incremental control allocation method that directly minimizes the total power required for an over-actuated, propeller-driven transition aircraft. The minimum power cost function is derived in a convex form and leads to a closed-form algebraic solution for an additional command increment in the null space of the local control effectiveness matrix. The complete control allocation procedure consists of a two-step approach that is direction-preserving in the presence of actuator saturations and nonlinear due to the incremental formulation. The proposed control allocation is particularly advantageous in the case of a transition aircraft, where the tradeoff between using aerodynamic lift and vertical thrust can be solved naturally by including incremental attitude commands as virtual inputs in the minimum power control allocation. The resulting control allocation scheme allows to both maximize control authority and find minimum power trim solutions. Flight tests are performed on an over-actuated transition aircraft, showcasing the minimum power trim and control allocation without the use of flight mode switching or blending functions throughout all flight regimes.

Time‐varying sliding mode controller for over‐actuated systems with constrained and uncertain actuators in flight control applications

AbstractOne solution to the problem of distributing the control action among redundant actuators with uncertain dynamics is employing an adaptive control allocator. This paper proposes a sliding mode controller which exploits a time‐varying sliding surface to complement adaptive control allocation in the presence of actuator saturation. The proposed approach does not require error augmentation for tracking desired references, which diminishes the computational burden. Aerodata Model in Research Environment, which is an over‐actuated aircraft model, is adopted to demonstrate the efficacy of the proposed controller in simulation studies.

LMI-based [formula omitted] boundary practical consensus control for nonlinear multi-agent systems with actuator saturation

This paper mainly addresses the practical consensus problem of nonlinear multi-agent systems modeled by reaction–diffusion equations subject to the bounded external disturbances. Different from the existing consensus control methods associated with spatiotemporal dynamics, the proposed H∞ Neumann boundary controller based on distributed measurement data can guarantee the optimal disturbance attenuation performance under the actuator saturation. Initially, a consensus spatiotemporal error model is constructed by introducing the Kronecker product and equivalent directed graph. Subsequently, a linear matrix inequalities (LMIs)-based sufficient condition is derived by combining the improved Lyapunov-based approach and H∞ norm. Then, an optimization problem is proposed by applying invariant set, such that the consensus errors can converge to a minimized bounded region in the presence of actuator saturation. Finally, comparison simulations on the synchronization of FitzHugh–Nagumo (FHN) model are given to demonstrate the effectiveness of proposed methodology.

Nonfragile Quantitative Prescribed Performance Control of Waverider Vehicles With Actuator Saturation

The existing prescribed performance control (PPC) strategies exhibit the fragility and nonguarantee of the prescribed performance when they are applied to dynamic systems with actuator saturation, and moreover, all of them are unable to quantitatively design prescribed performance. This article aims at remedying those deficiencies by proposing a new nonfragile PPC method for waverider vehicles (WVs) such that the quantitative prescribed performance can be guaranteed for tracking errors in the presence of actuator saturation. First, readjusting performance functions are developed to achieve quantitative prescribed performance and prevent the fragile problem. Then, low-complexity fuzzy neural control protocols are presented for velocity subsystem and altitude subsystem of WVs, while there is no need of recursive back-stepping design. Furthermore, auxiliary systems are designed to generate effective compensations on control constraints, which contributes to the guarantee of the desired prescribed performance, being proved via Lyapunov synthese. Finally, compared simulation results are given to validate the superiority.

Semi‐global weighted output average tracking of heterogeneous multi‐agent systems in the presence of actuator saturation and external disturbances

AbstractThis article studies the problem of semi‐global weighted output average tracking of a heterogeneous multi‐agent system in the presence of actuator saturation and external disturbances. The multi‐agent system contains a group of leader agents and a group of follower agents. Both the dynamics of leader agents and the dynamics of follower agents are allowed to be heterogeneous. Applying the classical output regulation theory and the low gain feedback design technique, we construct for each follower agent a state feedback control protocol and an output feedback control protocol, both parameterized in a scalar parameter. In both the state feedback case and the output feedback case, we construct distributed leader state observers for each follower agent that estimate the states of the leader agents. In the case of output feedback, we also construct for each follower agent state observer that estimates its own state. It is shown that, under these control protocols with a sufficiently small value of the parameter, each follower agent can track the weighted average of the outputs of the leader agents from an arbitrarily large bounded set of initial conditions and in the presence of actuator saturation and external disturbances if there exist directed paths from each leader agent to all the follower agents. Simulation results are given to illustrate the theoretical results.

Non-fragile tracking control of constrained Waverider Vehicles with readjusting prescribed performance

The purpose of this article is to solve the fragile problem of prescribed performance control (PPC) with application to Waverider Vehicles (WVs). By fragile problem, it denotes that tracking errors may break away from prescribed funnels if actuators are saturated. Firstly, we propose a new type of readjusting prescribed performance which adaptively readjusts its prescribed funnels such that the spurred transient performance and steady-state performance are still guaranteed for tracking errors in the presence of actuator saturation. Then, we extend the finite-time PPC into the non-affine model of WVs, ensuring that tracking errors always evolve within prescribed funnels. Furthermore, the improved back-stepping procedure without the derivation of virtual control law is utilized to develop non-fragile tracking controllers, and specially, there is no need of any fuzzy/neural approximation, avoiding the computational burden problem. Finally, compared simulation results are presented to verify the effectiveness and advantage.