Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO

Mahmoodabadi, Mohammad Javad; Taherkhorsandi, Milad; Bagheri, Ahmad

doi:10.1016/j.neucom.2013.07.009

Cited by 54 publications

(18 citation statements)

References 88 publications

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“…Several multi-objective optimization approaches for control system design have also been studied for the optimization of control performance. Similar to that of the single-objective counterpart, metaheuristic methods are prevalently used for the multi-objective tuning of classical control, such as PID [2,30,37], sliding mode control [24,33], as well as others [29]. A similar approach is applied in MPC tuning by using an offthe-shelf method of goal attainment [12,36].…”

Section: Introductionmentioning

confidence: 99%

Analytical results for the multi-objective design of model-predictive control

Bachtiar

Manzie

Moase

et al. 2016

Control Engineering Practice

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In model-predictive control (MPC), achieving the best closed-loop performance under a given computational resource is the underlying design consideration. This paper analyzes the MPC design problem with control performance and required computational resource as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control performance and the computational resource separately -often with the latter as a fixed constraint -which requires the implementation hardware to be known a priori. The proposed approach focuses on the tuning of structural MPC parameters, namely sampling time and prediction horizon length, to produce a set of optimal choices available to the practitioner. The posed design problem is then analyzed to reveal key properties, including smoothness of the design objectives and parameter bounds, and establish certain validated guarantees. Founded on these properties, necessary and sufficient conditions for an effective and efficient solver are presented, leading to a specialized multi-objective optimizer for the MOD-MPC being proposed. Finally, two real-world control problems are used to illustrate the results of the design approach and importance of the developed conditions for an effective solver of the MOD-MPC problem.

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Section: Introductionmentioning

confidence: 99%

Analytical results for the multi-objective design of model-predictive control

Bachtiar

Manzie

Moase

et al. 2016

Control Engineering Practice

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“…The desired joint angular displacement θ d is obtained after artificial teaching. The angular error e of the sixth joint is defined as the input, which is the difference between the desired angular displacement θ d and actual angular displacement θ 6 . Once the sliding surface has been chosen, the combination of input e and the sliding surface is the sliding mode function.…”

Section: The Rbfnn-smc Control Scheme For the Deburring Robot Systemmentioning

confidence: 99%

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

Tao

Zheng

Lin

2016

International Journal of Advanced Robotic Systems

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A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

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“…2 Switching functions of delta operator systems by four reaching law methods Fig. 3 Control inputs of delta operator systems by four reaching law methods (b) is expressed the case of reaching law (6), (c) is expressed the case of reaching law (7), and (d) is expressed the case of reaching law (11).…”

Section: Simulation Examplementioning

confidence: 99%

“…synchronization of a class of time-delay master-slave systems with Markovian jumping parameters and nonlinear uncertainties is given in [6]. A biped robot walking in the lateral plane on slope is designed by optimal SMC with ingenious multi-objective particle swarm optimization in [7]. Dissipativity-based SMC for switched stochastic systems is presented in [8].…”

Section: Introductionmentioning

confidence: 99%

Soft sliding mode controller design for uncertain delta operator systems

Liu

Kao

Wang

et al. 2015

Neural Comput & Applic

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Aiming at high-speed signal sampling system with internal parameter perturbation and external disturbance, a novel delta operator saturated variable rate reaching law based on sigmoid function is presented, and soft sliding mode controller design is addressed for delta operator systems with uncertainties in the paper. The structural feature of uncertain delta operator system is concerned. Soft sliding mode controller design for continuous-time system and discrete-time system can be converted to the case of delta operator system. The stability of uncertain delta operator soft sliding mode control (SMC) system is guaranteed by Lyapunov stability theory. Soft sliding mode controller design for uncertain delta operator systems is achieved by the proposed reaching law under a certain matching condition. It is illustrated that delta operator soft SMC system by the proposed reaching law in a simulation example can not only reach the scope of the switching plane in limited time, but also reduce system chattering effectively. The system on stability and process steady is better than that of delta operator exponential reaching law and delta operator variable rate reaching law. At the same time, the system has a good completely robustness compared with delta operator statured reaching law.

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Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO

Cited by 54 publications

References 88 publications

Analytical results for the multi-objective design of model-predictive control

Analytical results for the multi-objective design of model-predictive control

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

Soft sliding mode controller design for uncertain delta operator systems

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