Two-dimensional PFC with novel iterative error compensation and multi basis functions for batch processes under partial actuator failure

Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the Variable-Stability In-flight Simulator Test Aircraft (VISTA) F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream". Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuator-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared to what is possible without spawning additional filters, making partial failure adaptation a viable methodology in a manner heretofore unachieved.

In this paper we present a new state space model based linear quadratic fault-tolerant tracking control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the linear quadratic fault-tolerant control, the batch process is first treated with a new state space representation that incorporates both the state and the output tracking error dynamics. Then relevant concepts of the subsequent linear quadratic control are formulated, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults compared with traditional linear quadratic control. Application to injection velocity control shows that the proposed scheme achieves the design objective well with performance improvement.

Active Fault Tolerant Control for a Class of Networked Systems with Partial Actuator Failures

Unmanned Underwater Vehicles (UUVs) are being deployed in advanced applications that require precise manoeuvring close to complex underwater structures such as oilrigs and subsea installations or moving objects such as ships and submarines. The effect of vehicle's hydrodynamic parameter variations is significant in such scenarios and in extreme conditions the UUV may experience loss of control. In addition, external disturbances and actuator failures degrade the performance of the UUV. Adaptive control has been identified as a promising solution that can improve the performance in such situations. However, adaptive control is not widely used in UUVs mainly due to the trade-off between fast learning and smooth control signals. The latter can be guaranteed at low learning rates but require additional input to improve learning. The Predictor Model Reference Adaptive Control (PMRAC) is one such method that uses a prediction error to improve learning. In this paper, the performance of PMRAC in UUV applications is investigated and compared to standard Model Reference Adaptive Control (MRAC) at low learning rates under normal operational conditions, partial actuator failure, and under the influence of external disturbances. Simulation results show that PMRAC significantly reduces the tracking error compared to MRAC. In addition, PMRAC is less affected and recovers quickly from actuator failure and external disturbances, while generating smooth control signals with less oscillation compared to MRAC.

ABSTRACTThis study is concerned with the bumpless transfer problem for switched systems with partial actuator failures, in order to obtain smooth system performance output transition. Taking into account that the system requires a controller switching from current sub-controller to a fault-tolerant sub-controller after actuator fault. And bumpless transfer for control input cannot be traditionally designed when the actuator fault occurs, while performance smoothing can be considered and it is actually the ultimate goal of bumpless transfer. Specifically, the actuator fault model is firstly established and partial actuator fault is considered. Then, the system performance output signal is deemed as the main design variable of bumpless transfer, and closed-loop control systems both previous and after controller switching are constructed. Moreover, by using model matching thought and the adaptive sliding mode control technique, a bumpless transfer compensator design strategy is given to drive the performance output variable (after controller switching) to track the one of reference model. At last, simulation results of numeric and application examples demonstrate the effectiveness of the proposed bumpless transfer strategy.

This article deals with the design of a parametric eigenstructure assignment (PEA)-based multi-objective reconfigurable output feedback controller for a multiple-input multiple-output (MIMO) system against partial actuator failure. The significance of the PEA technique is that it provides more design degrees of freedom to obtain multi-objective functions, namely robust stability and improved transient response. Robust stability and transient response are ensured by a minimum condition number of the eigenvector and a minimum norm of controller gain. These two objectives are conflicting in nature, and, hence, the main aim is to minimize both objectives simultaneously by means of a multi-objective evolutionary algorithm (MOEA). In this study, a multi-objective optimization problem is formulated and is solved by non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-II (SPEA-II). Four indicators are used to evaluate each algorithm performance: spacing, error ratio, generational distance, and hypervolume. Then a reconfigurable controller is designed using PEA against actuator failure, which guarantees closed-loop stability. The effectiveness of the proposed controllers is demonstrated by implementing them in an interacting three-tank benchmark system.

Vibration suppression of flexible hose for autonomous aerial refueling based on adaptive boundary compound controller

To guarantee the control performance of the discrete system under unmeasurable state and partial actuator failure, a robust dynamic output feedback predictive fault-tolerant control method derived from switching strategy is developed. A new iterative error switching model is established, which both ensures tracking performance and lays the foundation for the development of the next switching controller. Based on this model, output feedback switching fault-tolerant controller is developed. Compared with conventional fault-tolerant control methods, the designed controller can be used under the case of unmeasurable states, and it is more fault tolerant and less conservative. According to the Lyapunov stability theory, the robust stability analysis is carried out under normal and fault conditions, respectively. Then, depending on the findings of the stability analysis, sufficient conditions to guarantee the stability of the closed-loop normal and faulty system are derived. Finally, the suggested method's efficacy is confirmed using the injection moulding process as an example.

In response to the limitations of modern industrial processes in terms of control accuracy and safety, this paper proposes a robust predictive fault-tolerant control method based on signal compensation. This method is generally applicable to nonlinear industrial processes with uncertainty and partial actuator failures. Firstly, a novel model is established for nonlinear industrial processes which includes fault factors and unknown lumped dynamic nonlinear terms. On this basis, sub-compensators are designed to eliminate the influence of unmodeled dynamics on the control effect. This method effectively reduces the conservatism of traditional fault-tolerant control and improves the tracking performance of the control system. In addition, this article provides a detailed demonstration of the stability and convergence of the closed-loop system, and verifies the effectiveness of the proposed method through comparative experiments. The experimental results demonstrate that the proposed method exhibits a excellent control performance in comparison to several mainstream methods.

In this paper, a backstepping-based nonlinear controller is developed to control the quadrotor in the presence of constant and time-varying disturbances. The control law is derived based on Lyapunov stability arguments. The controller is integrated with a disturbance observer to estimate and attenuate the effect of the disturbing forces and moments influencing the quadrotor. In this approach, no disturbance model is required for the disturbance observer. Discontinuous terms are added to the control law to ensure the negative definiteness of the Lyapunov function derivative without neglecting the disturbance observer estimation errors. Command filtering is used to compute the derivatives of the virtual control signals to avoid the complex analytic derivation of these derivatives, and to avoid differentiating the discontinuous terms existing in the controller. The effectiveness of the developed controller is investigated in simulations against disturbances due to partial actuator failure, wind, and unmodeled rotor dynamics.

A novel disturbance observer-based backstepping controller with command filtered compensation for a MIMO system

We analyze computer-generated holograms (CGH) of Lohmann types I and III and discuss their overall errors employing the following modifications, listed in order of increasing importance: phase-only correction (Ptype), virtual increase of the cell number, optimized use of the available plotting area, expansion of the amplitude dynamic range (D-type), circular overflow correction (OFC), and iterative overall error compensation. We compare the computing and plotting times for the listed CGH variants and their accuracy. As an application, we show the reconstruction of very complicated fields which may excite multimode and singlemode optical fibers, and a line grating with an in-between phase shift of 180 degrees ; the latter structure is useful in producing lambda/4-shifted semiconductor DFB lasers. Finally, we comment on some advanced techniques employing a desk-top computer and laser printer for producing hologram masks.

This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of a high speed, computer-controlled machining process. It is specially useful in mass-produced parts produced by a high-speed machine tool system. This method uses an iterative learning technique which adopts machine commands and cutting errors experienced from previous manoeuvres as references for compensation actions in the current manoeuvre. Non-repetitive disturbances and nonlinear dynamics of the cutting processes and servo systems of the machine which greatly affect the convergence of the learning control systems were studied in this research. State feedback and output feedback methods were used for controller design. Stability and performance of learning control systems designed via the proposed method were verified by simulations on a single degree of freedom servo positioning system.

This paper is based on a novel combined approach of constant correction and variable correction scheme for fixed width multiplier (FWM) with detail mathematical analysis of error correction function. The paper summarizes the various milestone topologies of fixed width multipliers and their implementation strategies. The mathematical analysis of a compensation function is based on the probability theory and mathematical expectation applied to least significant part of the partial product matrix, which is ultimately responsible for occurrence of error. The newly designed topology of an error compensation function is compared with the state of art FWM architectures in terms of hardware complexity and mean square error. The proposed combined approach gives the better error compensation without any cost of extra hardware. This design is tested as a part of DSP based application in 1024 and 8192 point FFT.

Traffic flow prediction remains a significant challenge in intelligent transportation systems. To enhance prediction accuracy, a hybrid model is proposed that integrates an improved Variational Mode Decomposition (VMD), a Residual Spatio-Temporal Graph Convolutional Network (RSTGCN), a Long-term Time Series Informer Network (LTSIN), and an iterative error compensation strategy. This approach effectively addresses the non-stationarity, spatial correlations, and temporal dependencies inherent in traffic data. First, the VMD is optimized using a Crested Porcupine Optimizer algorithm to decompose traffic flow time series into intrinsic mode functions (IMFs), each capturing different traffic patterns. Next, these IMFs are then input into the LTSIN and RSTGCN modules. The LTSIN models long-term temporal dependencies, while the RSTGCN captures short-term temporal and spatial relationships. Residual connectivity within the RSTGCN allows the model to retain node features and aggregate information from neighboring nodes, mitigating the over-smoothing issue typically seen in graph convolutional networks. Lastly, an iterative error correction mechanism is applied, where the IMF-derived predictions are refined and reconstructed to yield the final forecast. The effectiveness of the proposed model is demonstrated by extensive experiments, including ablation experiments, baseline comparisons, and multi-step forecasting experiments. Compared to existing models such as GCN and Informer, the proposed method reduces MAE by 8.78 and 6.79, and MAPE by 6.48% and 5.52%, respectively. These results indicate that the proposed model achieves superior prediction accuracy and can be effectively applied to traffic flow prediction tasks.