Less conservative robust reference governors and their applications

This letter proposes a set-theoretic Failure Mode and Effect Management (FMEM) strategy that handles stuck/jammed actuators and enforces pointwise-in-time state and control constraints. The approach exploits nesting between constraint admissible and recoverable sets to ensure the existence of a recovery sequence. A reference governor is applied to track reference commands while imposing constraint satisfaction using the remaining working actuators. Numerical results of an aircraft longitudinal flight application are reported.

This paper proposes a new formulation for the Reference Governor (RG) applied to linear systems affected by exogenous disturbances, specifically those large enough to cause constraint violation. This formulation is based on a set-theoretic approach which includes external disturbances within a new RG, that we call Recovery RG (RRG). The idea is to update the Maximal Output Admissible Set (MAS) and compute a constraint-admissible input that recovers the system from constraint violation at steady-state. The proposed RRG overcomes some of the limitations of standard RG schemes, and maintains their desirable features such as formulation simplicity and finite time determination of the MAS. We validate this new scheme by simulating a linearized turbocharged gasoline engine affected by additive disturbances.

The current control system of a fully submerged hydrofoil craft has manual input of fore-foil depth and control mode selection to improve the performance of the control system. However, the manual input needs skillful human operation and observation of waves the encountered to work well over a wide range of waves. In order to use information about the waves encountered in the control system, we considered the estimation of wave elevation and wave disturbance which was caused by the orbital motion of the waves in irregular waves. First, we investigated the wave disturbance by a fully submerged hydrofoil craft, in a state-space model of wave disturbance, and in hydrofoil craft motion, etc. We than considered estimations of the wave elevation and wave disturbance using a shaping filter, a Kalman filter, an autoregressive (AR) model, etc. Finally, we confirmed through simulations that the estimation results and estimation error of wave elevation and wave disturbance were valid.

In the setting of discrete-time linear systems with unmeasured set-bounded disturbances, this paper proposes a control scheme that combines a disturbance free input-constrained Model Predictive Control (uMPC) policy and a reference governor (RG) which modifies the reference command to enforce constraints that are not enforced by the uMPC policy. Such a scheme, referred to as the robust RGMPC, can handle (possibly nonlinear) state and input constraints and only requires solving an optimisation problem for MPC with polytopic input constraints, for which fast algorithms exist. Conditions are given which ensure recursive feasibility of the robust RGMPC scheme, finite-time convergence of the modified reference command to the desired reference command and asymptotic convergence of the state to an invariant neighbourhood of the associated set-point. Simulation results for a constrained spacecraft rendezvous manoeuvre demonstrate that the rRGMPC scheme has lower average computational time than several robust state and input-constrained MPC controllers.

This paper proposes a Failure Mode and Effect Management (FMEM) strategy for constrained systems with redundant actuators based on the combined use of constraint admissible and recoverable sets. Several approaches to ensure reconfiguration of the system without constraint violation in the event of actuator failures are presented. Numerical simulation results are reported.

A DATA-COMPRESSED TECHNIQUE OF A REFERENCE GOVERNOR IN A PIECEWISE AFFINE FUNCTION

In this paper, a novel adaptive robust control scheme is proposed for pose correction of eye-to-hand photogrammetry-based industrial robots subject to uncertainties. The proposed method uses two control loops: internal and external loops. The former is the dynamic controller designed for controlling the robot’s joints. The external loop is the kinematic controller to correct the pose error using the estimated end-effector’s pose acquired by the photogrammetry sensor (in this research C-track AMETEK). An adaptive integral terminal fractional-order super-twisting algorithm (AITFOSTA) is developed and employed for both control loops. AITFOSTA is an integral sliding mode controller (ISMC) whose nominal control law is a terminal one and its switching part is replaced with a fractional-order super-twisting algorithm (FOSTA), reducing the chattering to a great extent while rejecting the uncertainties. Additionally, an adaptive uncertainty and disturbance estimator based on radial basis function neural network (RBFNN) is designed and used as a compensator to reduce the uncertainty bounds, contributing to further chattering reduction. The stability analysis of the proposed controller is also presented. Experimental results on a PUMA200 industrial robot show superiority of the proposed method over other well-known approaches by reaching an unprecedented tracking accuracy, i.e., 0.06 mm and 0.18 deg for position and orientation, respectively.

Uncertain friction is a key factor that influences the accuracy of servo system in CNC machine. In this paper, based on the principle of Active Disturbance Rejection Control (ADRC), a control method is proposed, where both the extended state observer (ESO) and the reduced order extended state observer (RESO) are used to estimate and compensate for the disturbance. The authors prove that both approaches ensure high accuracy in theory, and give the criterion for parameters selection. The authors also prove that ADRC with RESO performs better than that with ESO both in disturbance estimation and tracking error. The simulation results on CNC machine show the effectiveness and feasibility of our control approaches.

Softsensor design is one of the key technologies in industry, because important variables such as product quality are not always measured on-line. Therefore, to reduce off-specification products and enhance productivity, the development of an accurate softsensor is crucial. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables. The procedure of two-stage SSID is as follows: 1) identify a state space model by using measured input and output variables, 2) estimate unmeasured disturbance variables from residual variables, and 3) identify a state space model to estimate key variables from the estimated disturbance variables and the other measured input variables. The proposed two-stage SSID can estimate unmeasured disturbances without assumptions that the conventional Kalman filtering technique must make; thus it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the properties of disturbances do not stay constant with time. The superiority of the proposed method over conventional methods is demonstrated through their application to an industrial ethylene fractionator.

Offset-free Model Predictive Control formulations refer to a class of algorithms that are able to achieve output tracking of reference signals despite the presence of plant/model mismatch or unmeasured nonzero mean disturbances. The general approach is to augment the nominal system with disturbances, i.e. to build a disturbance model, and to estimate the state and disturbance from output measurements. Some alternatives are available, which are based on a non augmented system with state disturbance observer, or on velocity form representations of the system to be controlled. In this paper, we review the disturbance model approach and two different approaches in a coherent framework. Then, differently from what is reported in the literature, we show that the two alternative formulations are indeed particular cases of the general disturbance model approach.

Homothetic tube-based robust offset-free economic Model Predictive Control

This paper presents estimation of disturbance to create a compound disturbance observer based on sliding modes. Disturbance observer(DOB) and sliding mode control are both effective methods in motion control. However, DOB is confined to nonlinear disturbance while sliding mode control(SMC) is limited by chattering phenomenon. The proposed method uses sliding mode to compensate the dynamic nonlinear equivalent disturbance and the switching gain of sliding mode, which is designed by disturbance estimation error using fuzzy mapping. The method also helps to decrease chattering with the help of DOB. The validity of the proposals is confirmed by experiments.KeywordsFlight simulatorMotion ControlDisturbance observerSliding modeDisturbance estimate

This article presents a new cascaded extended state observer (CESO)-based sliding-mode control (SMC) for an underactuated flexible joint robot (FJR). The control of the FJR has many challenges, including coupling, underactuation, nonlinearity, uncertainties and external disturbances, and the noise amplification especially in the high-order systems. The proposed control integrates the CESO and SMC, in which the CESO estimates the states and disturbances, and the SMC provides the system robustness to the uncertainty and disturbance estimation errors. First, a dynamic model of the FJR is derived and converted from an underactuated form to a canonical form via the Olfati transformation and a flatness approach, which reduces the complexity of the controller design. Furthermore, by taking the advantage of available measurable states, the CESO is adopted to attenuate the noises and make SMC feasible for high-order systems. Moreover, the CESO estimates the disturbances, which relaxes the upper bound of the disturbance in the SMC and reduces the chattering due to smaller switching gains. A stability analysis of the closed-loop system is presented based on the Lyapunov method. The effectiveness of the proposed control is verified in simulations and experimentally on a real-time FJR system.

Tracking control of motion systems typically requires accurate nonlinear friction models, especially at low speeds, and integral action. However, building accurate nonlinear friction models is time consuming, friction characteristics dramatically change over time, and special care must be taken to avoid windup in a controller employing integral action. In this paper a new approach is proposed for the optimal tracking control of motion systems with significant disturbances, parameter variations, and unmodeled dynamics. The ‘desired’ control signal that will keep the nominal system on the desired trajectory is calculated based on the known system dynamics and is utilized in a performance index to design an optimal controller. However, in the presence of disturbances, parameter variations, and unmodeled dynamics, the desired control signal must be adjusted. This is accomplished by using neural network based observers to identify these quantities, and update the control signal on-line. This formulation allows for excellent motion tracking without the need for the addition of an integral state. The system stability is analyzed and Lyapunov based weight update rules are applied to the neural networks to guarantee the boundedness of the tracking error, disturbance estimation error, and neural network weight errors. Experiments are conducted on the linear axes of a mini CNC machine for the contour control of two orthogonal axes, and the results demonstrate the excellent performance of the proposed methodology.

Discrete-time adaptive predictive sliding mode trajectory tracking control for dynamic positioning ship with input magnitude and rate saturations