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The purpose of this paper is to investigate the design method of partial observer canonical form (POCF), which is one of the important research tools for industrial plants.

Motivated by the two-steps method proposed in Xu et al. (2020), this paper extends this method to the case of Multi-Input Multi-Output (MIMO) nonlinear system. It decomposes the original system into two subsystems by observable decomposition theorem first and then transforms the observable subsystem into OCF. Furthermore, the necessary and sufficient conditions for the existing of POCF are proved.

The proposed method has a wide range of applications including completely observable nonlinear system, noncompletely observable nonlinear system, autonomous nonlinear system and forced nonlinear system. Besides, comparing to the existing results (Saadi et al., 2016), the method requires less verified conditions.

The new method concerning design POCF has better plants compatibility and less validation conditions.

This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.

The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved.

Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation.

For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario.

The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

The purpose of this paper is to find an omnidirectional robust gust response stabilization (GRS) scheme with anti-disturbance and state-limited features.

Disturbance observer and barrier Lyapunov techniques, which can, respectively, estimate the lumped disturbances of the dynamic system in real-time and ensure the middle states within some prescribed ranges according to some flight safety indexes.

In the existing literature, almost all of the GRS controllers are either only for the longitudinal dynamics or only for the latitudinal dynamics. Few studies have considered the gust response alleviation problem with omnidirectional wind disturbance and full aircraft model.

This paper proposes a fresh scheme to deal with a more holistic GRS problem; the disturbance observer based (DOB) barrier Lyapunov backstepping longitudinal controller has been put forward; DOB nonlinear dynamic inversion to handle the multi-input-multi-output lateral dynamics; and to closely connect the two loops of the latitudinal dynamics, a manipulating variable conversion method is proposed.

This paper aims to present an adaptive fuzzy sliding mode controller with nonlinear observer (AFSMCO) for the redundant robotic manipulator handling a varying payload to achieve a precise trajectory tracking in the task space. This approach could be applied to solve the problems caused by the dynamic effect of the varying payload to robotic system caused by model uncertainties.

First, a suitable observer using the recursive algorithm is presented for an accurate estimation of external disturbances caused by a variable payload. Second, the adaptive fuzzy logic is designed to approximate the parameters of the sliding mode controller combined with nonlinear observer (SMCO) to avoid chattering in real time. Moreover, Lyapunov theory is applied to guarantee the stability of the proposed closed-loop robotic system. Finally, the effectiveness of the proposed control approach and theoretical discussion are proved by simulation results on a seven-link robot and demonstrated by a humanoid robot platform.

The varying payload leads to large variations in the dynamics of the manipulator and the tracking error. To achieve high-precision position tracking, nonlinear observer was introduced to feed into the sliding mode control (SMC) which had improved the ability to resist the external disturbance. In addition, the chattering caused by the SMC was eliminated by recursively approximating the switching gain with the usage of adaptive fuzzy logic. Therefore, a distributed control strategy solves the problems of an SMC implementation in improving its tracking performance and eliminating the chattering of the system control.

The AFSMCO is proposed for the first time and used to control the redundant robotic manipulator that handles the varying payload. The proposed control algorithm possesses better robustness and higher precision for the trajectory tracking than classical SMC.

Discrete control of robot manipulators with uncertain model is the purpose of this paper.

The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm.

The proposed model-free discrete control is robust against all uncertainties associated with the model of robotic system including the robot manipulator and actuators, and external disturbances. Stability analysis verifies the proposed control approach. Simulation results show its efficiency in the tracking control.

A novel model-free discrete control approach for electrically driven robot manipulators is proposed. An adaptive fuzzy estimator is used in the controller to overcome uncertainties. The parameters of the estimator are regulated by a gradient descent algorithm. The most gradient descent algorithms have used a known cost function based on the tracking error for adaptation whereas the proposed gradient descent algorithm uses a cost function based on the uncertainty estimation error. Then, the uncertainty estimation error is calculated from the joint position error and its derivative using the closed-loop system.

The purpose of this paper is to derive a robust nonlinear attitude control law intended for practical application.

The method of input/output feedback linearization is utilized for having a linear model and a recently developed almost disturbance decoupling (ADD) approach is adopted for designing a robust satellite attitude control (SAC) system. The kinematics of the satellite is modeled by modified Rodriguez parameters because of their continuous invertibility. The design is simulated on the model of a realistic satellite project (BILSAT‐I), which is developed by the Turkish Scientific and Technological Research Council.

The torque requirement of the operation does not exceed the maximum limit provided by the actuator. The square error levels are staying under the boundary of final global attractor, which is one of the important proofs for the successful operation of the generated ADD control law.

The ADD concept is investigated on SAC problem. By that way, simple control structures with known disturbance attenuation capability can be designed.

Considering the external disturbances and dynamic uncertainties during the process of the trajectory tracking, this paper aims to address the problem of the welding robot trajectory tracking with guaranteed accuracy.

The controller consists sliding mode control, fuzzy control and low pass filter. The controller adopts low-pass filter to reduce the high frequency chattering control signal in sliding mode control. The fuzzy control model is used to simulate the external disturbance signal and the dynamic uncertainty signal, so that the controller can effectively restrain the chattering caused by the sliding mode control algorithm, realizing the track of the welding robot effectively and improving the robustness of the robot.

An innovative experiment device was adopted to realize the performance of the proposed controller. Considering the kinematic and dynamic uncertainty during the process of robot tracking, the tracking accuracy was realized within 0.3 mm.

This paper uses Lyapunov stability theory and Barbalat theorem to analyze the stability of the proposed controller.

The purpose of the proposed research methodology is to control the trajectory tracking of EDRM and also to cancel out the effect of no-smooth nonlinearities, which affect the system performance badly.

Robust adaptive neural network (RANN)-based backstepping control design methodology is presented in this paper. The proposed design methodology improves the trajectory tracking and running mean error.

The running mean error results show that the convergence of the proposed RANN-based backstepping technique is very fast as compare to the conventional PD control and due to this proposed control technique, the EDRM follows its desired trajectory perfectly.

The EDRM trajectory tracking performance increases which leads to a better working position of EDRM.

The originality of this research article is 93 per cent.

The purpose of this paper is to propose a novel Unmanned Combat Air Vehicle (UCAV) flight controller parameters identification method, which is based on predator-prey Biogeography-Based Optimization (PPBBO) algorithm, with the objective of optimizing the whole UCAV system design process.

The hybrid model of predator-prey theory and biogeography-based optimization (BBO) algorithm is established for parameters identification of UCAV. This proposed method identifies controller parameters and reduces the computational complexity.

The basic BBO is improved by modifying the search strategy and adding some limits, so that it can be better applied to the parameters identification problem. Comparative experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee finding the optimal controller parameters, with the rapid convergence.

The proposed PPBBO algorithm can be easily applied to practice and can help the design of the UCAV flight control system, which will considerably increase the autonomy of the UCAV.

A hybrid model of predator-prey theory and BBO algorithm is proposed for parameters identification of UCAV, and a PPBBO-based software platform for UCAV controller design is also developed.

The purpose of this paper is to obtain values that stabilize the lateral and longitudinal flight of the quadrotor for which the morphing amount and the best Proportional-Integral-Derivative (PID) coefficients are determined by using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm.

Quadrotor consists of body and arms; there are propellers at the ends of the arms to take off and rotors that rotate them. By reducing the angle between mechanism 1 and the rotors with the horizontal plane, the angle between mechanism 2 and the arms, the rotors rise and different configurations are obtained. Conventional multi-rotor aircraft has a fixed fuselage and does not need a tail rotor to change course as helicopters do. The translational and rotational movements are provided by the rotation of the rotors of the aircraft at different speeds by creating moments about the geometric center in 6-degree-of-freedom (DOF) space. These commands sent from the ground are provided by the flight control board in the aircraft. The longitudinal and lateral flight stability and properties of different configurations evaluated by dynamic analysis and simulations in 6 DOF spaces are investigated. An algorithm and PID controller are being developed using SPSA to achieve in-flight position and attitude control of an active deformable aircraft. The results are compared with the results of the literature review and the results of the previous article.

With SPSA, the best PID coefficients were obtained in case of morphing.

The effects of quadrotor arm height and hub angle changes affect flight stability. With the SPSA optimization method presented in this study, the attitude is quickly stabilized.

With the optimization method, the most suitable PID coefficients and angle values for the lateral and longitudinal flight stability of the quadrotor are obtained.

The transition rate and PID coefficients are determined by using the optimization method, which is advantageous in terms of cost and practicality.

With the proposed method, the aircraft can change shape to adapt to different environments, and the parameters required for more stable flight for each situation will be calculated, and this will be obtained more quickly and safely with the SPSA optimization method.