Modified power rate sliding mode control for robot manipulator based on particle swarm optimization

Fixed wing Unmanned Aerial Vehicles (FWUAVs) are widely utilized in both military and civilian sectors due to their ability to perform risky, inaccessible operations. The mathematical model of FWUAVs is complex, incorporating physical laws and coordinate systems with transformation matrices. In addition, controlling FWUAVs is challenging due to their nonlinear and coupled dynamics. This paper focuses on tracking trajectories for fixed wing unmanned aerial vehicles (FWUAVs) using a Robust Fractional Order Sliding Mode Controller (RFOSMC). An RFOSMC, which combines a conventional Sliding Mode Controller (SMC) with flexible fractional calculus, is proposed in this paper. Particle Swarm Optimization (PSO) is used to tune the control gains of RFOSMC. External disturbances and parameter variation are added to evaluate the controller’s performance. Comparative studies have been done with Linear Quadratic Regulator (LQR), Fractional Order PID (FOPID), SMC, and robust FOSMC. RFOSMC performs better than LQR, FOPID, and SMC in tracking accuracy, response speed, and overshoot. In addition, the performance comparison of RFOSMC and conventional SMC has been done based on performance index values of errors (ITAE), and RFOSMC showed a 76.5918% improvement. For a step input, RFOSMC showed the smallest settling time of 0.835s compared with 2.724s for SMC and 4.8573s for FOPID. Open-loop model verification and overall control system of FWUAV are done using MATLAB/Simulink software.

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm‐based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind‐based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored. The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs.

In this paper, a new asinh-type control scheme with gravity compensation for the position control problem of robot manipulators in joint space is presented. The properties and characteristics of the asinh control structure make the position error and the motion velocity asymptotically converge to the equilibrium point. A strict Lyapunov function to formally prove global asymptotic stability is developed. The tuning of the control gains is obtained by PSO (Particle Swarm Optimization) technique without saturating the servomotors. To illustrate the effectiveness and performance of the proposed scheme, an experimental comparative analysis between the proportional-derivative (PD) and atanh controls against the proposed algorithm on a three degrees of freedom direct-drive robot manipulator is carried out.

This paper focuses on maintaining the liquid level in tank two in the coupled tank control system with the help of the sliding mode control (SMC). SMC is the potent controller for coupled tank liquid level system under tracking conditions. Sliding mode controller (SMC) is nonlinear control it applies a discontinuous control signal. The main advantages of sliding mode are low sensitivity to plant parameter variations and disturbances. To maintain desired liquid levels in a nonlinear coupled tank is a great challenging task so this problem better handled by SMC. To control this liquid level process a SMC strategy is used we have first order SMC and second order SMC. Regulating this system in less time with proper control signal is not possible in first order SMC due to chattering is obtained which yields unwanted control signal. To fill tank-2 in less time with smooth control signal is possible in higher order SMC. Next, second order SMC is applied and stability is reached fast and no chattering phenomenon. These can be shown in the simulation results with the help of MATLAB. This application report describes how to develop SMC for liquid level control in coupled tank system.

Six degrees-of-freedom (DoF) robot manipulators are dynamically coupled and highly nonlinear multi-variable systems. Calculating the optimal joint angles from the operational space (i.e. inverse kinematics) and designing the optimal joint controller parameters are two important research topics. This paper proposes a 3-stage Particle Swarm Optimization (PSO)-based methodology for solving the inverse kinematics and optimizing the controller parameters. In the first stage, a PSO algorithm solves the inverse kinematics problem by minimizing a multi-objective cost function in the operational space (i.e. the error in the end-effector’s pose) and therefore finds the optimal joint angles. In the second stage, polynomial functions generate the desired trajectory between the initial and final poses in the joint space. Finally, a second PSO algorithm tunes six proportional-derivative (PD) controllers, one for each joint, to track the desired trajectory by minimizing another multi-objective cost function in the joint space. Two case studies, based on six DoF Puma 560, validate the performance of the proposed methodology. Simulation results show that the proposed 3-stage methodology provides fast and accurate results as the PSO algorithms are effective in solving the inverse kinematics problem and tuning the optimal PD parameters.

In this paper a novel controller for a flexible link manipulator based on fractional calculus is proposed. A hybrid system that combines the advantages in terms of robustness of the fractional control and the sliding mode control is proposed. Due to adding the extra degree of freedom, the fractional order sliding mode controller can achieve better control performance than the integer order sliding mode controller. For the problem of determining the design parameters, the particle swarm optimization (PSO) algorithm is used. The proposed controller is applied for a single‐link flexible manipulator robot. Finally, the performance and the significance of the closed loop system are investigated. The simulation results signify the performance of the proposed controller.

This paper suggests a new hybrid control strategy, called Optimal Type II Fuzzy Sliding Mode (OT2FSM) control scheme, to realize the control of general uncertain chaotic systems. The proposed controller not only guaranties the stability and robustness against the lumped uncertainties caused by unmodeled dynamics and external disturbances, but also can significantly reduce the control chattering inherent in conventional sliding mode control. In the proposed controller, a novel heuristic algorithm namely particle swarm optimization with random inertia weight (RNW–PSO) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input and output membership functions are optimized simultaneously by RNW–PSO. The globally asymptotic stability of the closed-loop system is mathematically proved. To evaluate the performance of the proposed controller, the results are compared with those obtained by Optimal H-infinity Adaptive PID (OHAPID) controller and an optimal Type-2 fuzzy proportional integral derivative (OT2FPID), which are the latest researches in the problem in hand. Simulation results show the effectiveness of the OT2FSM controller.

Trajectory tracking for a quadrotor under wind perturbations: sliding mode control with state-dependent gains

The success of the sliding mode control (SMC) is due to the simplicity of its implementation and its robustness against external disturbances via state space and input output model. In spite of this characteristic, the sliding mode control (SMC) suffers from a main drawback known as the ‘chattering phenomenon’. In order to overcome this problem, a new discrete second order sliding mode control via input output model is proposed in this paper. A stability analysis of the proposed control was then studied. To illustrate the effectiveness of the proposed discrete second order sliding mode control law, a classical discrete sliding mode control and discrete second order sliding mode control were applied to a real discrete second order system via input output model. The experimental results of the proposed discrete sliding mode control law show good performances in terms of the rejection of the external disturbances and the reduction of the chattering phenomenon.

This article presents an original motion control strategy for robot manipulators based on the coupling of the inverse dynamics method with the so-called second-order sliding mode control approach. Using this method, in principle, all the coupling non-linearities in the dynamical model of the manipulator are compensated, transforming the multi-input non-linear system into a linear and decoupled one. Actually, since the inverse dynamics relies on an identified model, some residual uncertain terms remain and perturb the linear and decoupled system. This motivates the use of a robust control design approach to complete the control scheme. In this article the sliding mode control methodology is adopted. Sliding mode control has many appreciable features, such as design simplicity and robustness versus a wide class of uncertainties and disturbances. Yet conventional sliding mode control seems inappropriate to be applied in robotics since it can generate the so-called chattering effect, which can be destructive for the controlled robot. In this article, this problem is suitably circumvented by designing a second-order sliding mode controller capable of generating a continuous control law making the proposed sliding mode controller actually applicable to industrial robots. To build the inverse dynamics part of the proposed controller, a suitable dynamical model of the system has been formulated, and its parameters have been accurately identified relying on a practical MIMO identification procedure recently devised. The proposed inverse dynamics-based second-order sliding mode controller has been experimentally tested on a COMAU SMART3-S2 industrial manipulator, demonstrating the tracking properties and the good performances of the controlled system.

The design of a globally stable second order sliding mode controller for sensorless induction motors is discussed in this paper. The adaptive component of the controller enables to compensate for the variation in time of the rotor resistance value, while the use of a suitably designed sliding mode speed/flux observer avoids the necessity of including speed and flux sensors within the control loop, making this proposal suitable for sensorless applications. The use of a second order sliding mode control approach, realized by regarding the second time derivatives of the stator currents as discontinuous auxiliary control inputs, is effective in limiting the mechanical wear, making this proposal more acceptable in practical applications than those based on conventional sliding mode control.

A DC-DC converter functioning in bidirectional (two-way) mode is a crucial component of direct current (DC) microgrids since it allows electricity to flow in both directions. However, because of load changes and other factors, the DC-bus voltage might become unstable. This research proposes a robust adaptive controller for a half-bridge two-way DC-DC converter founded on particle swarm optimization (PSO). Using a DC-DC half-bridge bidirectional converter, the effectiveness of various conventional and proposed control techniques is investigated. In comparison to a conventional sliding mode controller (CSMC), it is found that a PSO-based sliding mode control with an adaptive law is the optimal control approach for a bidirectional half-bridge DC-DC converter. This is because minimal steady-state error and the shortest rising and settling times are guaranteed. The benefits of robustness, chattering reduction, and simple design are combined in the suggested controller, which is especially beneficial when dealing with load and input voltage changes. The controller ensures robustness and stability in the face of parameter changes. Numerical simulations conducted in a MATLAB-Simulink environment on a DC-DC half-bridge converter operating in bidirectional mode show the controller's improved performance over its existing counterpart.

In this paper combination of standard and second order sliding mode controller are used and investigated for congestion control problem of differentiated Services (Diff-Serv) networks. Robustness against modeling uncertainties and disturbances are major features of sliding mode controller. Chattering phenomenon affects sliding mode congestion control; to overcome this problem, second order sliding mode control (SOSMC) is proposed as a congestion controller but this controller has slower response to SMC. Combination SMC and SOSMC gives better response. There are three types of traffics in Diff-Serv networks; i.e. three types of queues and based on that, the quality of their services are categorized. Each of these types of traffic has its own congestion control mechanisms. Stability of each controller is proved by Lyapunov stability law. To evaluate the capabilities of our proposed robust control strategy, simulation results are provided for both sliding mode controllers and second order slide mode controllers and their corresponding efficiencies are compared.

The main purpose of this paper is to design a robust second order sliding mode controller that can deal with uncertain nonlinear systems. This controller can keep the main advantages of the first order sliding mode controller, such as the ability to make the system asymptotically stable by forcing the error and its derivatives to have a zero value, the simplicity in the operation, and the robustness in the existence of perturbations. In spite of the features that characterize the first order sliding mode controller (1 SMC), it still suffers from the unwanted phenomenon “chattering”, which originates from a discontinuous control part (sign function). In this context, saturation function can be used instead of sign function to reduce this problematic chattering. Different from the saturation function method, the second order sliding mode controller can be used to overcome the chattering; suffered by the first order sliding mode controller and to retain the stability and performance of the system. In this paper, the twisting and the super twisting second-order algorithms of the sliding mode controller were used, and their results were compared with the first order sliding mode controller. So, this subject focused on the chattering problem who suffers from it the 1 SMC and try to reduce it by using the 2 SMC, the uncertain pendulum system was adopted in this work for the purpose of checking the three controllers. The simulations results showed that the second order sliding mode controller has the ability to reduce both the chattering magnitude and the steady state error and achieve an asymptotically stable system. The results were obtained by using MATLAB programming.

This paper presents a robust intelligent proportional-integral sliding mode controller for a variable speed wind turbine (VSWT). The main objective of the controller is to optimise the energy captured from the wind, and minimise the mechanical stress in the system. In order to guarantee the wind power capture optimisation without any chattering problems, this study propose to combine the sliding mode control (SMC), proportional integral (PI) control and particle swarm optimisation (PSO) algorithm. The PSO technique with efficient global search is used to optimise the PI and SMC parameters simultaneously to control the system trajectories to a sliding manifold that determines the system performance. The stability of the system using this controller is shown by the Lyapunov theory. The simulation results of the proposed PSO-PI based SMC (PSO-PISMC) method are compared with the PSO-I based SMC (PSO-ISMC) and the conventional PSO based SMC (PSO-SMC). The comparison results reveal that the proposed controller is more effective in reducing the tracking error and chattering. In addition, the controller shows more robustness against uncertainties and faster transient response of the system with reduced steady state error.