Proceedings of the Institution of Mechanical Engineers,Part I: Journal of Systems and Control Engineering

Most control methods deployed in lower extremity rehabilitation robots cannot automatically adjust to different gait cycle stages and different rehabilitation training modes for different impairment subjects. This article presents a continuous seamless assist-as-needed control method based on sliding mode adaptive control. A forgetting factor is introduced, and a small trajectory deviation from reference normal gait trajectory is used to learn the rehabilitation level of a human subject in real time. The assistance torque needed to complete the reference normal gait trajectory is learned through radial basis function neural networks, so that the rehabilitation robot can adaptively provide the assistance torque according to subject’s needs. The performance and efficiency of this adaptive seamless assist-as-needed control scheme are tested and validated by 12 volunteers on a rehabilitation robot prototype. The results show that the proposed control scheme could adaptively reduce the robotic assistance according to subject’s rehabilitation level, and the robotic assistance torque depends on the forgetting factor and the active participation level of subjects.

In this study, the problem of finite-time stability and boundedness for parabolic singular distributed parameter systems in the sense of L 2 norm is investigated. First, two new results on L 2 norm-based finite-time stability and finite-time boundedness for above-mentioned systems, inspired by the light of partial differential equations theory and Lyapunov functional method, are presented. Then, some sufficient conditions of L 2 norm-based finite-time stability and boundedness are established by virtue of differential inequalities and linear matrix inequalities. Furthermore, the distributed state feedback controllers are constructed to guarantee the L 2 norm-based finite-time stable and bounded of the closed-loop singular distributed parameter systems. Finally, numerical simulations on a specific numerical example and the building temperature control system equipped with air conditioning are given to demonstrate the validity of the proposed methods.

In this article, an adaptive neural network is proposed for the tracking control problem of unknown nonlinear interconnected systems with inaccessible states and sensor delays based on dynamic surface strategy. The system has unknown nonlinearities and immeasurable states. Thus, a neural network state observer based on delayed outputs of subsystems is applied. The main difficulty in obtaining local observers’ gains is that undelayed outputs are not available. As a result, by utilizing proper Lyapunov–Krasovskii functionals in dynamic surface design procedures, the gains of local observers are given in terms of linear matrix inequalities. Then, appropriate changes in coordinates are defined using delayed outputs, observed states, and filtered virtual controls for the purpose of designing dynamic surface controllers. Subsequently, proper Lyapunov–Krasovskii functionals are introduced to deal with sensor delays and obtain control laws and stability criteria. Furthermore, the proposed decentralized control scheme can suitably conquer the decentralized tracking problem of unknown large-scale systems with sensor delays and guarantee that all the signals in the closed-loop interconnected systems be uniformly ultimately bounded. Finally, to show the effectiveness and efficiency of the proposed approach, the theoretic achievements are employed to design a controller for a double-inverted pendulum system and a cascade chemical reactor system.

Control system performance assessment is significant, especially in practical applications. One of the most important indices for the performance assessment of control systems is minimum variance. Calculating the minimum variance index in multivariate systems requires prior knowledge of system parameters and models, and is therefore an obstacle in practical applications. In this article, an index is proposed for the performance assessment of multivariate control loops, evaluating the system performance with the minimum variance criterion and using only the system’s routine operation data. This index can quantify the performance using neither any prior knowledge of system parameters nor the system’s optimal operation data. The proposed index is based on the Hurst exponent, a parameter for measuring correlations in time series data. In this article, detrended fluctuation analysis and rescaled range analysis are used to estimate the Hurst exponents of system outputs. Using a combination of these Hurst exponents, an index is defined for the performance assessment of multivariate systems. The results of simulation examples illustrate that the proposed index can assess the performance efficiently.

This article focuses on the design of event-triggered asynchronous H ∞ fault-tolerant controller for Markov jump system subject to actuator faults and external disturbances. The asynchronization phenomenon not only occurs between the controlled system and controller but also exists between the controlled system and faulty actuator, which are portrayed as two corresponding hidden Markov models. Moreover, a mode-dependent event-triggered mechanism is introduced to facilitate network resources utilization. Then, by introducing mode-dependent Lyapunov-Krasovskii functional, a sufficient condition is obtained to guarantee that the closed-loop system is randomly mean square stable with H ∞ performance. Finally, two numerical examples are employed to illustrate the effectiveness of the proposed synthesis scheme.

In this study, a novel fuzzy logic–based decision support system approach to provide assistance in the selection of suitable input shaping techniques is presented. The proposed approach selects the suitable input shaping technique for point-to-point motion type of systems such as precise positioning, crane operations, flexible robotic systems and so on. The problem solution addressed is the selection of the input shaping technique and the settings for the selection of the input shaper. Some of these design issues require extensive expertise in command shaping and system modeling studies. To overcome these problems and the necessity for such an expertise in these application areas, the proposed technique is provided as a solution. The presented study also provides a review of input shaping methods as well as their advantages and disadvantages in terms of vibration elimination performance, traveling time and robustness features. In the final section of the study, the details of the simulations, as well as experimental results, are provided to validate the achieved high performance of the proposed technique. The experimental studies are conducted on a Quanser IP02 Gantry Crane experimental setup.

The pulverised coal and its mixture with biomass are one of the most popular fuels in industrial energy. To ensure, on one hand, minimal greenhouse gas emission and, on the other hand, maximum energy production efficiency, it is necessary to monitor the combustion process of these fuels. One way to do this is to monitor the flame intensity. This is an optical, non-invasive solution, and information on the status of the process is obtained with minimal delay. The article proposes a method for identifying undesired combustion states for which the excess air coefficient is greater or smaller than the value ensuring total combustion. Three deep recurrent neural network architectures for classifying the flame intensity time series were explored. The best results were obtained using the convolutional long short-term memory model, which offered the accuracy of 86.5%–99.8%, depending on the current thermal power. The prediction time of a single data sequence was about 0.6 ms. High accuracy and low time consumption of the proposed method create an opportunity for its use in industrial combustion systems of pulverised coal and its mixture with biomass.

Raw meal fineness is the percentage content of 80 µm sieving residue after the cement raw material is ground. The accurate prediction of raw meal fineness in the vertical mill system is very helpful for the operator to control the vertical mill. However, due to the complexity of the industrial environment, the process variables have coupling, time-varying delay and nonlinear characteristics in the grinding process of cement raw material. At present, few people pay attention to the coupling characteristics among variables, thus solving this problem is particularly important in raw meal fineness prediction. In this article, we propose a two-dimensional convolutional neural network method that is used to predict raw meal fineness during the grinding process of raw material. Convolutional neural network has strong feature extraction capabilities and does not require manual feature selection. The two-dimensional convolution kernels are used to extract the coupling, time-varying delay and nonlinear features among variables, especially the coupling features. In addition, two important parameters P and L of two-dimensional convolutional neural network model are optimized, respectively. The optimized model solves the problems of coupling, time-varying delay and nonlinearity among variables. Our two-dimensional convolutional neural network model is proved to be very effective by comparing with the state-of-the-art methods.

With the wide application of robots in the material distribution process on the assembly lines, single robot scheduling cannot meet the actual production needs. However, the high degree of mechanization also brings about environmental problems. Therefore, this article aims to develop a scheduling methodology to accomplish material supply tasks using multiple robots with energy consumption consideration. Meanwhile, a targeted mathematical model to minimize total weighted penalty costs and total energy consumption is developed. Due to the NP-hard nature of the problem, an adaptive hybrid mutation population extremal optimization multi-objective algorithm based on uniform distribution selection is proposed to solve multi-objective problems. Furthermore, a new coding method for initialization is designed to optimize the whole iterative process. The performance of the proposed algorithm is evaluated by comparing with three benchmark multi-objective algorithms. Computational experiments are represented to prove the validity and feasibility of the proposed algorithm.

The exhaustive and irresponsible use of fossil fuels has created numerous public and environmental health issues in the past few decades. To address this issue, this work has investigated the use of polanga (Calophyllum inophyllum) biodiesel/diesel blends in a diesel engine. This study focuses primarily on the optimization of performance and emission characteristics of a diesel engine fuelled with polanga-based biodiesel blends. The engine input factors were also investigated for desired optimal thermal performance. In this study, four input parameters, namely, engine loads, blends of polanga-based biodiesel, fuel injection pressure, and fuel injection timing were chosen for analysis. The corresponding engine output responses, namely, brake thermal efficiency, CO, NOx, and smoke emissions, are selected for their optimization by Taguchi method and response surface methodology. The results show that the best setting of above-mentioned input factors is reported at 44% engine load, 13% mixing of polanga biodiesel with diesel, 180 bar injection pressure of fuel, and 21.5 °bTDC injection timing of fuel. The comparison between results obtained by the optimization process and experimental results showed that the deviations were always found to be within the acceptable range of errors.

The dynamic characteristics of reciprocating pump–pipeline system are directly affected by the fluid–mechanism dynamic interaction related to the slider-crank mechanism, valves and pipes conveying fluid. In this article, the fluid–mechanism interaction and nonlinearities involved in the kinetic of slider-crank mechanism, the motions of pump valves and the dynamic transmission in pipeline are explored for the nonlinear dynamic modeling of reciprocating pump–pipeline interaction systems. The nonlinear fluid–mechanism coupling model and corresponding analysis procedure are presented for investigating the system dynamic characteristics at all operating conditions. An experiment platform consisting of a simplex plunger reciprocating pump and suction and discharge pipes with a flow control valve is established to validate the proposed model. By the comparisons of pressure pulsations under multi-working conditions, the results obtained from the proposed model show good agreement with the test data. The dynamic characteristic of pump, as well as the effects of interaction and nonlinearity on the flow pulsation, are studied with the proposed model. It is found that nonlinear factors such as joint clearance and nonlinear spring stiffness are of great importance to the lag characteristics of pump valves and the pressure pulsation of pump–pipe system. The amplitudes of pressure pulsation increase with the decrease of control valve opening nonlinearly, and the effect of flow control valve becomes significant when the opening is less than 40%.

In most applications of autonomous navigation, the state of a system must be estimated from noisy sensors. Accurate estimation of the true system state can be achieved using data fusion algorithms. Furthermore, the fusion scheme can be affected by many factors such as modeling errors and parameters uncertainties. The gaps and inconsistencies due to the sensors noise and modeling errors can be reached with robust nonlinear filtering. In this article, a new framework has been developed for data fusion algorithms based on nonlinear NH∞ filter with fuzzy adaptive bound and adaptive disturbances attenuations. Type-1 Fuzzy Adaptive NH_∞ algorithm has been proposed and compared with the Interval Type 2 Fuzzy Adaptive NH_∞, for unmanned vehicle localization. The proposed algorithms fuse data from low-cost sensors using inertial navigation system, Global Positioning System and monocular vision. Type-1 Fuzzy Adaptive NH_∞ and Interval Type 2 Fuzzy Adaptive NH_∞ algorithms, adaptively, handle the effects of noisy sensors, parameters uncertainties and modeling errors. Both algorithms use adaptive bounds ( δ i ) and adaptive disturbance attenuation ( γ ) for higher-order terms of the Taylor development. The adaptive bounds consider issues associated to Gyro drift, image distortion and Global Positioning System dilution of precision which make the proposed algorithms more accurate than the classical NH∞. The validating experiment and the efficiency of Type-1 Fuzzy Adaptive NH_∞ and Interval Type 2 Fuzzy Adaptive NH_∞ have been conducted in real scenario and in unstructured outdoor environment without any a priori knowledge of the evolution of the vehicle. The experimental results have demonstrated the robustness and efficiency of these two filtering strategies against uncertainties and their online sensor switching capability. The Interval Type 2 Fuzzy Adaptive NH_∞ algorithm provides more accuracy and robustness compared to the Type-1 Fuzzy Adaptive NH_∞.

A new adaptive time delay estimation technique with sliding mode control method is proposed and investigated in this passage for cable-driven manipulators. Time delay estimation technique is an effective tool to compensate for unmodeled dynamics and unknown disturbance, and adaptive time delay estimation performs better due to its adaptive control gain. The proposed adaptive method is based on the fuzzy logic algorithm which has a great advantage in input–output mapping thank to its flexibility. Tuning procedure is addressed to reveal the implementation of the newly proposed algorithm. Moreover, the desired trajectory is taken as an input of adaptive algorithm and better control performance is obtained through this attempt. The proposed controller is ultimately uniformly bounded and proof using the Lyapunov method is provided. Finally, comparative experiments show the validity and effectiveness of the proposed controller.

In real-time hybrid testing, systems are separated into a numerically simulated substructure and a physically tested substructure, coupled in real time using actuators and force sensors. Actuators tend to introduce spurious dynamics to the system which can result in inaccuracy or even instability. Conventional means of mitigating these dynamics can be ineffective in the presence of nonlinearity in the physical substructure or transfer system. This article presents the first experimental tests of a novel passivity-based controller for hybrid testing. Passivity control was found to stabilize a real-time hybrid test which would otherwise exhibit instability due to the combination of actuator lag and a stiff physical substructure. Limit cycle behaviour caused by nonlinear friction in the actuator was also reduced by 95% with passivity control, compared to only 64% for contemporary methods. The combination of passivity control with conventional methods is shown to reduce actuator lag from 35.3° to 13.7°. A big advantage of passivity control is its simplicity compared with model-based compensators, making it an attractive choice in a wide range of contexts.

A novel active suspension control strategy is introduced to improve dynamic response of vehicle suspension systems. The proposed algorithm is a fusion of classical controller design methods together with an online observer and is based on the cancelation of system disturbances. The operational calculus method and the differential algebraic theory are applied to build the observer/compensator that is appended to the classical linear quadratic regulator. An ultra-local model based on linear algebraic rules is presented avoiding the use of a precise mathematical model while guaranteeing the stability of the overall system. Simplicity of implementation, low power demand and significant enhancement of active suspension performance are the observed features of the proposed controller. The numerical simulations illustrate the effectiveness and the robustness against sprung mass variation of the proposed control method compared to proportional–integral–derivative controller, intelligent proportional–derivative controller, linear quadratic regulator and active disturbance rejection—linear quadratic regulator.

This article considers finite-time bounded controller design for one-sided Lipschitz nonlinear differential inclusions. Sufficient conditions of finite-time bounded criterion are given employing convex hull Lyapunov function approach. An algorithm is designed to calculate the finite-time bounded controller. Moreover, a system initial state selection method is presented to find the domain of system initial state aid for transforming quasi-linear matrix inequality–based conditions to linear matrix inequality-based conditions. Finally, a numerical example and a comparison experiment example are given to illustrate the effectiveness of this proposed design method.

The active control of a suspension system is meant to provide an isolated behavior of the system spring-mass (for example, increased comfort and performance). During this article, we are going to explain the importance of developing an intelligent control approach for active truck suspensions based on the artificial neural network. From where the main objective of this article is to obtain a mathematical model for active suspension systems then build a hydraulic model for active suspension control for trucks using an artificial neural network. In this article, a corresponding artificial neural network nonlinear active suspension controller has been designed and optimized for approximate road profiles, using simulation according to International Organization for Standardization 2631-5 and International Organization for Standardization 8608 standardizations. The model developed with MATLAB Toolbox, estimated and validated from data collected during tests carried out with a truck in other research work. To model the system, the laws of physics are used to describe the system and experimental data or information supplied about the system to determine the parameters of the system. The statement of the problem of this research is to develop a robust artificial neural network controller for the nonlinear active suspension system of the heavy truck that can improve the performances and its verifications using graphical and simulation output. The results of the simulation show that the methodology offers excellent performance. In addition, the robustness of the artificial neural network hydraulic controller is demonstrated for a variety of road profiles that increase the capabilities of the proposed methodology and prove its effectiveness.

Aiming at the problems of inaccurate fault detection and error alarm in the process of hot strip mill process, a fault detection scheme of canonical independent component analysis is proposed. The new scheme first uses canonical variable analysis to calculate the canonical variable matrix of observation data, which effectively solves the problem of autocorrelation and cross-correlation. Then the canonical variable matrix is decomposed by independent component analysis to obtain independent elements. Finally, the data are monitored online through constructing statistics. It is proved that the accuracy of the scheme for identifying fault data is reached to 100%, and the misjudgment rate data are reduced to less than 0.6% through the simulation study of the hot strip mill process data.

This research is concerned with the problem of parameter identification for ship response model. A novel nonlinear innovation–based algorithm is proposed by use of the hyperbolic tangent function and the stochastic gradient algorithm. In order to demonstrate the validity of the algorithm, two identification experiments are adopted by the “Galaxy” ship and the “Yupeng” ship. Furthermore, the comparison experiment is illustrated to verify the effectiveness of the proposed algorithm, including the least square algorithm, the traditional stochastic gradient algorithm and the improved nonlinear innovation–based stochastic gradient algorithm. The identification results indicate that the improved stochastic gradient algorithm is with higher accuracy by 95.2% than the original algorithm and 11.75% than the least square algorithm. In addition, the proposed algorithm is with advantages of fast speed and high accuracy of identification. That can be extended to other parameter identification systems with the limited test data.

Position controlling with less overshoot and control effort is a fundamental issue in the design and application of micro-actuators such as micro-positioner. Also, tracking a considered path is very crucial for some particular applications of micro-actuators such as surgeon robots. Herein, a proportional–integral–derivative controller is designed using a feedback linearization technique for path tracking control of a cantilever electromechanical micro-positioner. The micro-positioner is simulated based on a 1-degree-of-freedom lumped-parameter model. Three different paths are considered, and the capability of the designed controller on the path tracking with lower error and control effort is investigated. The obtained results demonstrate the efficiency of the designed proportional–integral–derivative controller not only for reducing the tracking error but also for decreasing the control effort.

The design of an output-based robust disturbance rejection controller, aimed to solve the state tracking for the articulations of an experimental biped robot, was the main outcome of this study. The robust disturbance rejection controller included an auxiliary hybrid observer entailed to recover the angular velocity for each articulation. The estimated states served to perform the approximation of disturbances and non-modeled parts in the biped robot dynamics by implementing an extended state observer structure. The observer used the tracking position errors as input information, as well as considering the limb articular constraints, which are natural for biologically inspired biped robots. The effect of state constraints motivated the implementation of a hybrid observer with saturated output error injection. The controller design used the estimation of constraint velocity for solving the design of a tracking trajectory control to resolve the reproduction of the gait cycle by the bipedal robotic system. The Lyapunov stability theory served to obtain the laws which adjust the observer gains as well as to prove the ultimate boundedness of the tracking error as well. The evaluation of the suggested controller was realized on a numerical representation of the biped robot. These simulations illustrated the tracking performance of the hybrid robust disturbance rejection controller for all biped robot articulations in a decentralized structure. Experimental evaluations were also considered to validate the robust disturbance rejection controller design. A fully actuated biped robot was constructed and controlled by the robust disturbance rejection controller. The tracking results obtained by the robust disturbance rejection controller (in both the numerical and experimental evaluations) overcame the classical approach performances of diverse controllers as state feedback (proportional-derivative form) and regular robust disturbance rejection controller which did not consider the articulation constraints.